Communication Projects – ElysiumPro

In this paper, we study vector parameter estimation in multiple-input multiple-output (MIMO) wireless powered sensor networks (WPSN) where sensor nodes operate by harvesting the radio frequency signals transmitted from energy access points (E-AP). We investigate a joint design of sensor data precoders, a fusion rule, and energy covariance matrices to minimize the mean square error (MSE) of the parameter estimate based on a non-linear energy harvesting model. First, we propose a centralized algorithm to solve the MSE minimization problem. Next, to reduce the computational complexity at the fusion center (FC) and feedback overhead from the sensors to the FC, we present a distributed algorithm to locally compute the precoders and the energy covariance matrices. We employ the alternating direction method of multipliers (ADMM) technique to minimize the MSE in a distributed manner without any coordination from the FC. In the proposed distributed algorithm, each sensor node calculates its own precoders and determines the local information of the fusion rule, and then messages are broadcast to other sensor nodes and E-APs. Simulation results demonstrate that the distributed algorithm performs close to the centralized algorithm with reduced complexity. Moreover, the proposed methods exhibit superior estimation performance over conventional techniques in WPSNs.

The energy consumption in wireless sensor networks stills a very interesting challenge in both industrial and research field. The IEEE 802.15.4 standard is an excellent result of the collaboration between these both fields in order to well managing the energy consumed in the node by controlling its duty cycle. It presents the most efficient standards for low energy consumption that is why it is considered as the main technology for the Internet of Thing. This paper consists mainly of validating through an experimental test bed, the two proposed methods. The first approach proposed is about computing the energy consumed by the node in its different transmission states with more efficiency. Although the second approach deals with computing, it is most convenable duty cycle basing on the remaining energy in the battery of the node which presents a very interesting issue for the energy consumption in wireless communication.

It is very challenging to design an efficient network-wide broadcast protocol for asynchronous wireless sensor networks (AWSNs). Since the neighbors adopt sleep schedule independently and the probability that they wake up concurrently is very low, each single-hop broadcast has to be substituted by multiple unicast transmissions. Moreover, without the schedule knowledge of designated neighbors, a sender has to keep active until all these nodes are covered, which consumes a large amount of energy. In this letter, we propose an energy-efficient network-wide broadcast protocol (EENWB) for AWSNs. By introducing more aggressive cooperations among the neighboring senders, some senders can go to sleep much earlier, only at the cost of increasing the active periods of their cooperators slightly. Extensive simulation results show that EENWB can reduce the total energy consumption greatly.

In this paper, the joint spectrum sensing and resource allocation problem is investigated in a multi-band-multi-user cognitive radio (CR) network. Assuming imperfect spectrum sensing information, our goal is to jointly optimize the sensing threshold and power allocation strategy such that the average total throughput of secondary users (SUs) is maximized. Additionally, the power of SUs is constrained to keep the interference introduced to primary users (PUs) under certain limits, which gives rise to a nonconvex mixed integer non-linear programming (MINLP) optimization problem. Our contribution in this paper is threefold. First, it is illustrated that the dimension of the nonconvex MINLP problem can be significantly reduced, which helps to re-formulate the optimization problem without resorting to integer variables. Second, it is demonstrated that the simplified formulation admits the canonical form of a monotonic optimization and a ∈-optimal solution can be achieved using the polyblock outer approximation algorithm. Third, a practical low-complexity spectrum sensing and resource allocation algorithm is developed to reduce the computational cost. Finally, the effectiveness of proposed algorithms is verified by simulations.

The energy-efficiency of routing algorithm is crucial for improving the lifetime of battery constrained Wireless Sensor Networks (WSNs). The consideration of nodes heterogeneity in routing is essential for achieving optimal resource utilization. This letter considers sensor nodes with random initial energies and random disparities in data generation rate (traffic) to model a realistic clustering based WSN suited for heterogeneous sensing applications. The letter presents an energy model for the scenario and proposes a Traffic and Energy Aware Routing (TEAR) scheme to improve the stability period. The simulation results indicate that TEAR outperforms other clustering based routing algorithms under the scenario.

We consider a distributed detection system for a wireless sensor network (WSN) over slow-fading channels. Each sensor only has knowledge of quantized channel state information (CSI) which is received from the fusion center (FC) via a limited feedback channel. We then consider transmit power allocation at each sensor in order to maximize a J-divergence based detection metric subject to a total and individual transmit power constraints. Our aim is to jointly design the quantization regions of all sensors CSI and the corresponding power allocations. A locally optimum solution is obtained by applying the Generalized Lloyd Algorithm (GLA). To overcome the high computational complexity of the GLA, we then propose a low-complexity near-optimal scheme which performs very close to its GLA based counterpart. This enables us to explicitly formulate the problem and to find the unique solution despite the non-convexity of the optimization problem. An asymptotic analysis is also provided when the number of feedback bits becomes large. Numerical results illustrate that only a small amount of feedback is needed to achieve a detection performance close to the full CSI case.

In this paper, we study the secure beamforming design for a cognitive radio network (CRN), where a primary transmitter–receiver pair coexists with ansecondary transmitter–receiver pair. Each pair constitutes a multiple-input single-output link. We consider an underlay scheme and a cooperative scheme. For the underlay scheme, the secondary user (SU) is allowed to transmit simultaneously in the presence of the primary transmission. For the cooperative scheme, the secondary transmitter acts as a relay to forward the secrecy information of the primary transmission in exchange for its own transmission. For both schemes, the SU is untrusted and considered a potential eavesdropper. Our goal is to minimize the total power consumption while satisfying the primary user’s required secrecy rate and the SU’s required information rate. Using suitable optimization tools, we design the jointly optimal secure beamforming for the underlay scheme and an alternative optimizing algorithm for the cooperative scheme. To further reduce the complexity, we also design suboptimal zero-forcing beamformers for both schemes. The simulation results verify the proposed schemes.

We address the problem of online admission control in multi-hop, multi-transceiver cognitive radio networks where the channel access is regulated by a bare-bones time-division multiple access protocol and the primary user activity is modeled as an ON/OFF process. We show that the problem of computing the available end-to-end bandwidth-necessary for admission control-is NP-Complete. Rather than working on an approximation algorithm and analyzing its worst-case performance, we relax the problem of online admission control by using a randomized scheduling algorithm and analyzing its average performance. Randomized scheduling is widely used because of its simplicity and efficiency. However, computing the resulting average throughput is challenging and remains an open problem. We solve this problem analytically and use the solution as vehicle for BRAND-a centralized heuristic for computing the average bandwidth available with randomized scheduling between a source destination pair in cognitive radio networks. Driven by practical considerations, we introduce a distributed version of BRAND and prove its correctness. An extensive numerical analysis demonstrates the accuracy of BRAND and its enabling value in performing admission control.

Ensuring total rate maximization whilst keeping Greenhouse gas emission (GHGE) at a minimum in green cooperative cognitive radio networks (GCCRNs) is key to achieving maximum network efficiency. This study employs the lexicographic multiobjective optimization methods to overcome the challenge posed by rate maximization, GHGE minimization, and relay selection in GCCRNs simultaneously. First, through application of a technique for throughput maximization, a complex nonconvex optimization problem is transformed into its simplified convex programing equivalent. Next, the GHGE minimization is tackled by convex programing and finally a mathematical relay selection procedure is presented based on zero-norm principle which provides an effective technique to obtain a minimum node selection. Simulation results backed by a theoretical analysis confirm the superiority of the proposed approach in terms of network performance and complexity compared to some of the well-known schemes investigated in this study.

The rapid growth of wireless communication technology has resulted in the increasing demand on spectrum resources. However, according to a recent study, most of the allocated frequency experiences significant underutilization. One important issue associated with spectrum management in heterogeneous cognitive radio networks is: How to appropriately allocate the spectrum to secondary sender–destination (S–D) pair for sensing and utilization. In this paper, the authors investigate the spectrum allocation problem under a more practical scenario where the heterogeneous characteristics of both the secondary S–D and primary channels are taken into consideration. With the objective to maximize the achievable throughput for secondary S–D, we formulate the spectrum allocation problem as a linear integer optimization problem under spectrum availability constraint, spectrum span constraint, and interference free constraint. This problem is proven to be Non-deterministic Polynomial (NP)-complete, and a recent result in theoretical computer science called randomized rounding algorithm with polynomial computational complexity is developed to find the ρ-approximation solution. Evaluation results show that our proposed algorithm can achieve a close-to-optimal solution at a low level of computation complexity.

Cognitive radio networks (CRN) have been proposed to exploit licensed bands opportunistically, with secondary users' (SU) activity being subordinated to primary users (PU). The two most popular types of spectrum access in CRN are {interweave} and {underlay}. There is no clear consensus which one provides better results, for different metrics, despite the fact that there has been a lot of research dedicated to each mode. In this work, we analyze this problem theoretically, providing formulas that are in closed form. These expressions allow the performance comparison of interweave and underlay modes under a unified network setup. Our focus are two metrics, {throughput} and {delay}, which we analyze relying on renewal-reward theory and queueing theory, respectively. These results enable an SU to decide what mode to use depending on what the optimization objective is, given the key network parameters. Furthermore, relying on the results of our analysis, we propose hybrid policies, which are dynamic, and allow the SU to switch between the two modes at any point. These policies offer an additional performance improvement of up to 50%. We validate our results with extensive realistic simulations.

Routing and channel assignment schemes for cognitive radio networks (CRNs) are often designed assuming the half-duplex (HD) transmission capability per user. However, recent advances in full-duplex (FD) communications and self-interference suppression techniques challenge the traditional HD transmission capability, in which FD communication can significantly improve spectrum utilization. In this work, we investigate the routing and channel assignment problem in FD-based CRNs. Two types of FD communications are considered. The first type only allows for simultaneous transmission and reception over different channels, while the second type allows for simultaneous transmission and reception over the same channel. Specifically, for a given cognitive radio (CR) source–destination pair, we first formulate the channel assignment problem for each path between the communicating pair as an optimization problem with the main objective of minimizing the number of distinct assigned channels for that path such that the number of simultaneous active hops across the path is maximized. We show that the optimization problem is a binary linear programming problem, which is, in general, nondeterministic polynomial time-hard. Thus, we present a near-optimal solution based on a sequential fixing procedure, where the binary variables are iteratively determined by solving a sequence of relaxed programs. Accordingly, we develop a novel routing scheme that selects the best path along with the channel assignment such that the highest capacity is achieved. Simulation results are provided, which show that a careful routing and channel assignment scheme for FD CRNs can significantly improve the network performance.

We address the problem of power allocation and secondary user (SU) selection in the downlink from a secondary base station (SBS) equipped with a large number of antennas in an underlay cognitive radio network. A new optimization framework is proposed in order to select the maximum number of SUs and compute power allocations in order to satisfy instantaneous rate or QoS requirements of SUs. The optimization framework also aims to restrict the interference to primary users (PUs) below a predefined threshold using available imperfect CSI at the SBS. In order to obtain a feasible solution for power allocation and user selection, we propose a low-complexity algorithm called DeleteSU-with-Maximum-Power-allocation (DMP). Theoretical analysis is provided to compute the interference to PUs and the number of SUs exceeding the required rate. The analysis and simulations show that the proposed DMP algorithm outperforms the stateof-the art selection algorithm in terms of serving more users with minimum rate constraints, and it approaches the optimal solution if the number of antennas is an order of magnitude greater than the number of users.

In the next-generation cognitive radio networks, numerous secondary users will share the spectrum resource with the primary users. As it may not be possible to support all the communication rate requirements, there are many supporting sets for the secondary users as long as the communication rates of the primary users are guaranteed? In this paper, we study the maximum feasible set problem to access as many secondary users as possible, under the constraints of power budgets and communication rates in cognitive radio networks. In this interesting issue, the existing literature generally removes a subset of the secondary users so that the remaining users achieve the thresholds with communication rates and power budgets. However, the removal algorithms causes more interference when there are plenty of unsupported secondary users. We leverage the spectral radius of the network characteristic matrix as the admission price to access the new secondary user. Then, we design a hybrid access control algorithm to reduce the interference time and approximate the maximum network capacity. Moreover, different supported sets produce the different energy efficiency, even having the same network capacity, while all users require the high communication rates. Numerical results demonstrate that our algorithms provide the decent energy efficiency under the communication rate constraints.

This paper investigates the effect of multiuser gain provided by the secondary user selection on the secrecy performance of the primary users. We first propose a simple scheme where the user with the minimal interference channel is selected, and derive a closed-form lower bound of the achievable ergodic secrecy rate (ESR) of the primary users. In the high signal-to-noise ratio (SNR) regime, asymptotic result shows that the multiuser gain scales logarithmically with the number of the secondary users for a fixed interference temperature. Inspired by non-orthogonal multiple access strategy, we then propose a maximal jamming rate-based scheme, where the secondary with maximal interference channel is selected and it will transmit with an elaborately designed rate so that the primary receiver can cancel out the interference completely with successive interference cancellation. A closed-form expression of achievable ESR is also presented. Theoretical and simulation results show that the both proposed schemes can achieve multiuser gain and improve the security of the primary users significantly.

Energy-efficient and secure wireless communications have recently earned tremendous interests due to economic, environmental and military concerns. This paper investigates the tradeoff between the secrecy throughput and the energy efficiency in cognitive radio networks (CRNs), where primary and secondary users with different priorities of spectrum access can either interfere or cooperate with each other. To gain an understanding of the intricate effects that system parameters have on network performance, we exclusively focus on characterizing several key aspects that may have potential impacts on secure CRNs, including the transmission power, the number of interfering users, and the interference resistance coefficient. Based on the obtained analytical results, we further propose a cooperative spectrum sharing paradigm to improve both the secrecy throughput and the energy efficiency of primary users. The main idea is that primary users lease a fraction of licensed spectrum to secondary users and in return, the secondary transmitter acts as both a relay for primary transmissions and a friendly jammer against eavesdropping. Both theoretical and numerical results disclose that: (i) When the interference from secondary transmitters is small, there is an optimal transmission power that maximizes the secrecy throughput for primary users compared to CRNs without the security issue.

The problem of distributed estimation over wireless sensor networks in an adversarial environment with the presence of attacks on sensed and communicated information is considered. To tackle with this problem, a secure diffusion leastmean squares (S-dLMS) algorithm is proposed. The proposed SdLMS can be considered as a hybrid system, which consists of a non-cooperative LMS (nc-LMS) subsystem and a diffusion LMS (dLMS) subsystem. The nc-LMS subsystem is used to provide a reliable reference estimate, which is further used for constructing the threshold test to detect the trust neighbors of each node. Then, based on the detected secure network topology, the dLMS subsystem is performed by combining the received information from the trust neighbors. The performance of the proposed SdLMS algorithm in the mean and mean-square senses is analyzed, and then an adaptive rule is suggested to select the threshold for detection. Finally, some simulations are performed to show the effectiveness of the proposed S-dLMS algorithm under fixed and time-varying attacks, respectively.

In the traditional reliable multicast schemes of wireless cellular network, base station (BS) repeatedly transmits the same packet until it is received by all receivers. The use of device-to-device (D2D) communication can greatly offload the traffic of BS. This paper considers D2D communication-based multicast from BS to a cluster of devices which are close to one another (e.g., in the same building). So far, the efficient D2D retransmission scheme available is to associate each NACK-device (which did not correctly receive the data from BS) to some near ACK-device (which correctly received the data) for forming subclusters, and let ACK-devices retransmit the data to their respective associated NACK-devices in the FDMA mode by using multiple channels, aiming to minimize the time–frequency resource cost. Noticing that the total available channels are very limited and the devices’ energy is a very precious resource, in this paper, we first present the subcluster-based single-channel D2D retransmission way where the ACK-devices use the same channel in the TDMA mode. Then, aiming to minimize the total energy consumption of retransmitters, we formulate the joint optimization of NACK-devices’ association and retransmitters’ transmission powers to be a mixed integer programming problem. Finally, we propose an efficient algorithm for this problem to find a good association pattern and transmission powers. Simulation results show that, using D2D communication greatly reduces multicast traffic load of BS. Moreover, compared to its counterparts with a fixed number of retransmitters, our retransmission scheme greatly reduces the total energy consumption of retransmitters.

As an indispensable use case for the 5G wireless systems on the roadmap, ultra-reliable and low latency communications (URLLC) is a crucial requirement for the coming era of wireless industrial automation. The key performance indicators for URLLC stand in sharp contrast to the requirements of enhanced mobile broadband (eMBB): low-latency and ultra-reliability are paramount but high data rates are often not required. This paper aims to develop communication techniques for making a paradigm shift from the conventional human-type broadband communications to the emerging machine-type URLLC. One fundamental task for URLLC is to deliver short commands from a controller to a group of actuators within the stringent delay requirement and with high-reliability. Motivated by the factory automation setting in which tasks are assigned to groups of devices that work in close proximity to each other thus can form clusters of reliable device-to-device (D2D) networks, this paper proposes a novel two-phase transmission protocol for achieving URLLC. In the first phase, within the latency requirement, the multi-antenna base station (BS) combines the messages of all devices within each group together and multicasts them to the corresponding groups; messages for different groups are spatially multiplexed. In the second phase, the devices that have decoded the messages successfully, herein defined as the leaders, help relay the messages to the other devices in their groups. Under this protocol, we design an innovative leader selection based beamforming strategy at the BS by utilizing sparse optimization technique.

This paper investigates wireless powered communication networks in an interference channel. In this system, due to asymmetric time allocation of the downlink and the uplink among multiple cells, cross-link interference may occur which significantly affects the overall performance. Considering this interference issue, we study a minimum rate maximization problem to overcome a severe imbalance on a rate distribution among users. The minimum rate maximization problem is non-convex, and thus we propose an algorithm that updates the time allocation and the users’ transmit power based on the Lagrangian duality method and the Perron-Frobenius theorem, respectively. Simulation results verify that the proposed methods outperform conventional schemes.

This paper studies device-to-device wireless communications where two energy-constrained internet-of-things (IoT) nodes, which do not have constant power supplies, wish to exchange their information with each other. Because of small form factor, the IoT nodes are normally equipped with simple energy storages, which might suffer from a high self-discharging effect. Therefore, the energy stored in each node would not be available after a few time duration. In this system, we investigate power splitting (PS) based energy exchange methods by exploiting radio frequency (RF) wireless energy transfer techniques, and propose a new concept called wireless information and power exchange (WIPE). In this WIPE protocol, each node operates either in a transmit mode and a receive mode at each time slot. First, a transmit node sends the information signal to a receive node which utilizes a PS circuit for information decoding and energy harvesting. Then, the harvested energy of the receive node is stored in the energy storage. At the consecutive time slot, two nodes switch their operations, i.e., the receive node in the previous time slot now operates in a transmit mode which transfers RF signals by using the harvested energy. This procedure continues by changing the operations of two nodes at each time slot. For the proposed WIPE protocol, we provide two different PS ratio optimization schemes which maximize the weighted sum throughput performance according to the level of channel state information (CSI) knowledge.

In this paper, we present a comprehensive secrecy performance analysis of wireless powered communication networks with multiple eavesdroppers, where an energy-limited information source with multiple antennas harvests the radio frequency energy from a dedicated power beacon (PB) before transmission. To exploit the benefits of multiple antennas at source, two popular multi-antenna transmission schemes, i.e., maximal ratio transmission and transmit antenna selection, are investigated for two intercepting ways at Eves, i.e., non-colluding and colluding scenarios, respectively. Specifically, adopting the time-switching protocol at PB, we derive exact closed-form expressions of the secrecy outage probability and the average secrecy rate for both two transmission schemes taking into account the outdated channel state information. Furthermore, in order to deeply extract insights on the system design, we further present tractable asymptotic secrecy outage probability and ergodic secrecy capacity at high signal-to-noise ratio regimes, which easily enable us to obtain the secrecy diversity order and coding gain of two transmission schemes, respectively. From our analysis, several important concluding remarks are obtained as follows: 1) full secrecy diversity order can be achieved by both two transmission schemes with no feedback delay, however, it reduces to one in the presence of feedback delay; 2) as the number of eavesdroppers increases, the secrecy performance gap between non-colluding and colluding scenarios becomes large; and 3) maximal ratio transmission (MRT) scheme always outperforms transmit antenna selection (TAS) scheme with no feedback delay.

This paper considers multiuser multiple-input multiple-output (MIMO) wireless powered communication networks (WPCNs). In this system, a multiantenna power beacon (PB) transfers wireless energy to several multiantenna users, and then the users transmit data to a multiantenna base station. Time durations for the energy harvesting (EH) operations at each user can be differently set based on the users’ channel conditions, which results in an asynchronous protocol on the EH and the information transmission. We maximize the sum rate performance of the asynchronous WPCN by jointly optimizing the energy precoding matrices at the PB, the information precoding matrices, and the EH time durations at the users. By using convex optimization techniques, the optimal algorithm for the sum rate maximization problem is provided with analytical expressions of the optimal precoding matrices. Simulation results verify that the proposed optimal algorithm outperforms conventional schemes.

In this paper, we study the performance of non-orthogonal multiple access (NOMA) schemes in wireless powered communication networks (WPCN) focusing on the system energy efficiency (EE). We consider multiple energy harvesting user equipments (UEs) that operate based on harvest-then-transmit protocol. The uplink information transfer is carried out by using power-domain multiplexing, and the receiver decodes each UE’s data in such a way that the UE with the best channel gain is decoded without interference. In order to determine optimal resource allocation strategies, we formulate optimization problems considering two models, namely half-duplex and asynchronous transmission, based on how downlink and uplink operations are coordinated. In both cases, we have concave-linear fractional problems, and hence Dinkelbach’s method can be applied to obtain the globally optimal solutions. Thus, we first derive analytical expressions for the harvesting interval, and then we provide an algorithm to describe the complete procedure. Furthermore, we incorporate delay-limited sources and investigate the impact of statistical queuing constraints on the energy-efficient allocation of operating intervals. We formulate an optimization problem that maximizes the system effective-EE while UEs are applying NOMA scheme for uplink information transfer. Since the problem satisfies pseudo-concavity, we provide an iterative algorithm using bisection method to determine the unique solution. In the numerical results, we observe that broadcasting at higher power level is more energy efficient for WPCN with uplink NOMA. Additionally, exponential decay QoS parameter has considerable impact on the optimal solution, and in the presence of strict constraints, more time is allocated for downlink interval under half-duplex operation with uplink TDMA mode.

Future wireless networks are required to offer new applications and services, which will experience high dispersions in time and frequency, incurred mainly by coarse synchronization. Coarse synchronization is induced by signaling overhead reduction and dictated by the tremendous optimization of the radio interface efficiency. It is expected to dramatically damage waveform orthogonality in conventional orthogonal frequency-division multiplexing (OFDM) systems and to result in oppressive intercarrier interference (ICI). To alleviate the degradation in performance caused by ICI, the concept of nonorthogonal multiplexing has been promoted, as a serious alternative to strict orthogonal multiplexing, for guaranteeing the OFDM benefits without requiring high-level synchronization. Within this nonorthogonal multiplexing framework, ping-pong optimized pulse shaping-OFDM (POPS-OFDM) has been introduced as a powerful tool to efficiently design waveforms, which withstand future multicarrier systems’ dispersion impairments. In this paper, we investigate the discrete time version of the POPS-OFDM approach and study its sensitivity and robustness against estimation and synchronization errors. Based on numerical results, we show that POPS-OFDM provides an important gain in the signal-to-interference ratio, typically higher than 5 dB, with respect to conventional OFDM.

As an indispensable use case for the 5G wireless systems on the roadmap, ultra-reliable and low latency communications (URLLC) is a crucial requirement for the coming era of wireless industrial automation. The key performance indicators for URLLC stand in sharp contrast to the requirements of enhanced mobile broadband (eMBB): low-latency and ultra-reliability are paramount but high data rates are often not required. This paper aims to develop communication techniques for making a paradigm shift from the conventional human-type broadband communications to the emerging machine-type URLLC. One fundamental task for URLLC is to deliver short commands from a controller to a group of actuators within the stringent delay requirement and with high-reliability. Motivated by the factory automation setting in which tasks are assigned to groups of devices that work in close proximity to each other thus can form clusters of reliable device-to-device (D2D) networks, this paper proposes a novel two-phase transmission protocol for achieving URLLC. In the first phase, within the latency requirement, the multi-antenna base station (BS) combines the messages of all devices within each group together and multicasts them to the corresponding groups; messages for different groups are spatially multiplexed. In the second phase, the devices that have decoded the messages successfully, herein defined as the leaders, help relay the messages to the other devices in their groups.

In this paper, we investigate the resource allocation problem for D2D communications underlaying wireless powered communication networks, where multiple D2D pairs harvest energy from a power station equipped with multiple antennas and then transmit information signals simultaneously over the same spectrum resource. The aim is to maximize the sum throughput via joint time scheduling and power control, while satisfying the energy causality constraints. The formulated non-convex problem is first transformed into a nonlinear fractional programming problem with a tactful reformulation. Then, by leveraging D.C. (difference of two concave functions) programming, a suboptimal solution to the non-convex problem is obtained by iteratively solving a sequence of convex problems. Simulation results demonstrate that the proposed scheme works well in different scenarios and can significantly improve the system throughput compared with the-state-of-the-art schemes.

Optical wireless communication (OWC) with orbital angular momentum (OAM)-based spatial multiplexing enables to offer ultra-high information capacity as well as high spectral and energy efficiencies, which is a promising alternative to establish the fronthaul toward the fifth generation (5-G) wireless communication networks. The air turbulence in the optical wireless channel, however, would result in adverse effect in the performance of OWC with spatial multiplexing. We study the impact of air turbulence on the purities of multiplexed OAM states. The split step propagation scheme combining with the Monte-Carlo phase screen method is employed to accurately emulate the OWC link with OAM-based spatial multiplexing. The numerical results reveal that the air turbulence-induced cross-talk among the parallel OAM spatial channels would severely degrades the performance of the fronthaul in 5-G which is established by the OWC with OAM-based spatial multiplexing.

For 5G wireless communication networks, low latency and high spectral efficiency are required to provide ubiquitous communication for emerging applications. In this letter, we propose a novel framework which considers these requirements simultaneously by integrating the notion of effective capacity (EC) and non-orthogonal multiple access (NOMA) into virtualized wireless networks (VWNs). At first, we consider a virtual base station (BS) architecture to achieve isolation between different service providers (slices). Then, a resource allocation design for maximization of the EC is formulated as a non-convex optimization problem which takes into account the average maximum power constraints and delay quality-of-service (QoS) requirements. To strike a balance between computational complexity and system performance, we apply the successive convex approximation (SCA) to the non-convex optimization problem. Subsequently, an iterative low-complexity suboptimal algorithm is proposed. Numerical results illustrate that there is a non-trivial fundamental tradeoff between delay and spectral efficiency in VWNs while NOMA provides a significant improvement in terms of EC compared to conventional orthogonal multiple access (OMA).

In order to keep a balance between the transmission delay of backhaul and the spectrum efficiency of access links, the coverage performance of wireless networks with non-orthogonal multiple access (NOMA) and content caching is studied. First, an explicit expression for the coverage probability of a typical user is presented by using stochastic geometry and order statistics. This expression can provide useful insights in order to improve the coverage performance of NOMA communications. Second, a closed-form expression for the average coverage probability is derived. Finally, simulation results are provided to validate the accuracy of the analytical framework and demonstrate the performance gain, due to NOMA.

Considering a multi-user interference network with an eavesdropper, this paper aims at the power allocation to optimize the worst secrecy throughput among the network links or the secure energy efficiency in terms of achieved secrecy throughput per Joule under link security requirements. Three scenarios for the access of channel state information are considered: the perfect channel state information; partial channel state information with channels from the transmitters to the eavesdropper exponentially distributed; and not perfectly known channels between the transmitters and the users with exponentially distributed errors. The paper develops various path-following procedures of low complexity and rapid convergence for the optimal power allocation. Their effectiveness and viability are illustrated through numerical examples. The power allocation schemes are shown to achieve both high secrecy throughput and energy efficiency.

We address a fundamental question in wireless networks that, surprisingly, has not been studied before: what is the maximum density of concurrently active links that satisfy a certain outage constraint? We call this quantity the spatial outage capacity (SOC), give a rigorous definition, and analyze it for Poisson bipolar networks with ALOHA. Specifically, we provide exact analytical and approximate expressions for the density of links satisfying an outage constraint and give simple upper and lower bounds on the SOC. In the high-reliability regime where the target outage probability is close to zero, we obtain an exact closed-form expression of the SOC, which reveals the interesting and perhaps counter-intuitive result that all transmitters need to be always active to achieve the SOC, i.e., the transmit probability needs to be set to 1 to achieve the SOC.

Nonorthogonal multiple access (NOMA) exhibits superiority in spectrum efficiency and device connections in comparison with the traditional orthogonal multiple access technologies. However, the nonorthogonality of NOMA also introduces intracell interference that has become the bottleneck limiting the performance to be further improved. To coordinate the intracell interference, we investigate the dynamic user scheduling and power allocation problem in this paper. Specifically, we formulate this problem as a stochastic optimization problem with the objective to minimize the total power consumption of the whole network under the constraint of all users' long-term rate requirements. To tackle this challenging problem, we first transform it into a series of static optimization problems based on the stochastic optimization theory. Afterward, we exploit the special structure of the reformulated problem and adopt the branchand-bound technique to devise an efficient algorithm, which can obtain the optimal control policies with a low complexity. As a good feature, the proposed algorithm can make decisions only according to the instantaneous system state and can guarantee the long-term network performance. Simulation results demonstrate that the proposed algorithm has good performance in convergence and outperforms other schemes in terms of power consumption and user satisfaction.

Dynamic network architectures (DNAs) have been developed under the assumption that some terminals can be converted into temporary access points (APs) anytime when connected to the Internet. In this paper, we consider the problem of assigning a group of users to a set of potential APs with the aim to maximize the downlink system throughput of DNA networks, subject to total transmit power and users’ quality of service (QoS) constraints. In our first method, we relax the integer optimization variables to be continuous. The resulting non-convex continuous optimization problem is solved using successive convex approximation framework to arrive at a sequence of second-order cone programs (SOCPs). In the next method, the selection process is viewed as finding a sparsity constrained solution to our problem of sum rate maximization. It is demonstrated in numerical results that while the first approach has better data rates for dense networks, the sparsity oriented method has a superior speed of convergence. Moreover, for the scenarios considered, in addition to comprehensively outperforming some well-known approaches, our algorithms yield data rates close to those obtained by branch and bound method.

This paper investigates the effect of relay randomness on the end-to-end throughput in multi-hop wireless networks using stochastic geometry. We model the nodes as Poisson Point Processes and calculate the spatial average of the throughput over all potential geometrical patterns of the nodes. More specifically, for problem tractability, we first start with the simple nearest neighbor (NN) routing protocol, and analyze the end-to-end throughput so as to obtain a performance benchmark. Next, note that the ideal equal-distance routing is generally not realizable due to the randomness in relay distribution, we propose a quasi-equal-distance (QED) routing protocol. We derive the range for the optimal hop distance, and select the relays to formulate a quasi-equidistant deployment. We analyze the end-to-end throughput both with and without intra-route resource reuse. Our analysis indicates that: (i) the throughput performance of the proposed QED routing can achieve a significant performance gain over that of the NN routing. As the relay intensity gets higher, the performance of QED routing converges to that of the equidistant routing. (ii) If the node intensity is a constant over the network, then intra-route resource reuse is always beneficial when the routing distance is sufficiently large. (iii) With randomly distributed relays, the communication distance can generally be extended. However, due to the uncertainty in relay distribution, long distance communication is generally not feasible with random relays. This implies that the existence of a reasonably defined infrastructure is critical in effective long distance communication. Our analysis is demonstrated through numerical examples.

This paper aims at solving two classes of energy efficiency (EE) maximization problems on power allocation in wireless communication systems with multiple parallel channels. Firstly, the EE maximization problem with sum power constraint is solved based on the geometric water-filling approach; and secondly, the approach is extended into the EE maximization problem with additional least throughput requirement constraint. Our proposed algorithms make use of the water-filling structure of the optimal solution and provide exact and computation efficient solution to the energy-efficient power allocation problems. The proposed algorithms also have excellent scalability, which is applicable for large scale wireless communication systems. Optimality of the proposed algorithms is strictly proved, and the proposed algorithms only require low degree polynomial computational complexity. Numerical results are presented to demonstrate the efficiency of the proposed algorithms. To the best of our knowledge, no prior algorithms in the existing literature could provide such solutions to the EE maximization problems under the merit of exactness and the efficiency.

A new detection scheme for multiuser multipleinput multiple-output (MIMO) systems is analytically presented. In particular, the transmitting users are being categorized in two distinct priority service groups, while they communicate directly with a multi-antenna receiver. The linear zero-forcing scheme is applied in two consecutive detection stages upon the signal reception. In the first stage, the signals of one service group are detected, followed by the second stage including the corresponding detection of the remaining signals. An appropriate switching scheme based on specific transmission quality requirements is utilized prior to the detection so as to allocate the signals of a given service group to the suitable detection stage. The objective is the provision of the reception quality for both service groups. The proposed approach can be implemented directly in cognitive radio communication assigning the secondary users to the appropriate service group. The exact outage probability of the considered system is derived in closed form. The special case of massive MIMO is further studied yielding some useful engineering outcomes; the effective channel coherence time and a certain optimality condition defining both the transmission quality and effective number of independent transmissions.

In this paper, an uplink cross-layer resource allocation problem based on imperfect channel state information (CSI) is modeled as min–max fractional stochastic programming for heterogeneous wireless access. The resource allocation is subject to constraints in delay, service outage probability, system radio bandwidth, and total power consumption. The joint bandwidth and power allocations are based on CSI at the physical layer and queue state information (QSI) at the link layer. In order to determine the transmission rate of each mobile terminal according to the queue buffer occupancy, a probability upper bound of exceeding the maximum packet delay in terms of a required transmission rate is presented based on M/D/1 model. Then, the bandwidth and power allocation problem is transformed into bi-convex programming, and an optimal distributed bandwidth and power allocation algorithm is proposed. To reduce computational complexity, a suboptimal distributed bandwidth and power allocation algorithm is presented. Simulation results demonstrate that the proposed algorithms improve the energy efficiency greatly.

In this paper, we propose a combined full duplex (FD) and half duplex (HD) based transmission and channel acquisition model for open-loop multiuser multiple-input multiple output (MIMO) systems. Assuming residual self-interference (SI) at the BS, the idea is to utilize the FD mode during the uplink (UL) training phase in order to achieve simultaneous downlink (DL) data transmission and UL CSI acquisition. More specifically, the BS begins serving a user when its CSI becomes available, while at the same time, it also receives UL pilots from the next scheduled user. We investigate both zero-forcing (ZF) and maximum ratio transmission (MRT) MIMO beam forming techniques for the DL data transmission in the FD mode. The BS switches to the HD mode once it receives the CSI of all users and it employs ZF beam forming for the DL data transmission until the end of the transmission frame. Furthermore, we derive closed form approximations for the lower bounded ergodic achievable rate relying on the proposed model. Our numerical results show that the proposed FD-HD transmission and channel acquisition approach outperforms its conventional HD counterpart and achieves higher data rates.

In this paper, we consider the distributed multiple-input multiple-output systems which experience both the small-scale and large-scale fading. By employing the Rayleigh-lognormal model to characterize the composite fading channels, we derive the uplink sum rate in terms of the signal-to-noise ratio for zero forcing (ZF) and ZF decision feedback receivers, respectively. Afterwards, we propose an optimal power loading strategy by constructing an optimization problem that maximizes the dominant term of the sum rate. Then on this basis, a two-stage precoder is designed to further improve the performance, where only the first and second order statistics of the channels are needed at the transmitters. Numerical simulations testify the effectiveness of the analytical sum rates and present the superiority of our optimal power loading strategy as well as the two-stage precoder.

Communications over power lines in the frequency range above 2 MHz, commonly referred to as broadband (BB) power line communications (PLC), has been the focus of increasing research attention and standardization efforts in recent years. BB-PLC channels are characterized by a dominant colored non-Gaussian additive noise, as well as by periodic variations of the channel impulse response and the noise statistics. In this work we study the fundamental rate limits for BB-PLC channels by bounding their capacity while accounting for the unique properties of these channels. We obtain explicit expressions for the derived bounds for several BB-PLC noise models, and illustrate the resulting fundamental limits in a numerical analysis.

In this paper, we study an energy-harvesting cellular two-way relay network with massive multiple-input multiple-output, where multiple energy-harvesting mobile stations (MSs) communicate with a base station (BS) via an energy-harvesting relay station (RS). A signal space alignment (SSA) based simultaneous wireless information and power transfer (SWIPT) protocol is proposed for efficient energy harvesting and information exchange. We analyze the performance of the proposed SSA-SWIPT scheme, and derive closed-form expressions for the asymptotic signal-to-interference-plus-noise ratios and the achievable sum-rate when the numbers of antennas at the BS and the RS go large simultaneously. We also investigate the optimal power splitting ratios at the RS and the MSs to maximize the achievable sum-rate. Numerical results are provided to verify the analysis and it is shown that employing massive antennas can improve the efficiencies of wireless energy harvesting and information transmission.

We propose a Bayesian framework for the problem of multi-user dectection in the context of an unknown and time-varying number of active users. In this paper, we combine orthogonal frequency-division multiplexing modulation with multi-antenna reception to mitigate both the asynchronism and frequency-selectivity of the wireless medium. We develop a method for user identity and data detection with joint channel parameter estimation, performed on a per-OFDM block basis to account for a highly dynamic random-access channel. Based on a factor graph approach, we derive an inference algorithm based on message-passing resulting in an iterative code-aided receiver. We show that a suitable Gaussian approximation leads to a complexity that increases only linearly with the maximum number of users. Computer simulations show that the proposed iterative receiver has a low probability of erroneous activity detection, while maintaining a high antenna diversity order for active users.

In orthogonal frequency division multiplexing (OFDM) with index modulation, the modulation parameters to be classified include both the signal constellation and the number of active subcarriers. This is different from conventional OFDM schemes where only the signal constellation needs to be classified. In this paper, to solve this challenging problem, the likelihood-based automatic modulation classification (AMC) is studied. First, in the scenario with known channel state information (CSI), two classifiers based on average likelihood ratio test (ALRT) and hybrid likelihood ratio test (HLRT), respectively, are derived. Concretely, in HLRT-based classifier, energy-based detector and log-likelihood ratio-based detector are employed to identify the active subcarriers. Second, a HLRT-based blind AMC is proposed in the scenario with unknown CSI, where the efficient implementation of expectation maximization algorithm is presented to estimate channel fading coefficients and noise variance. Finally, the effectiveness of the proposed AMC algorithms is confirmed by computer simulations.

This letter investigates the power allocation problem for the downlink of an orthogonal frequency division multiple (OFDM)-based non-orthogonal multiple access visible light communication (NOMA-VLC) system. Unlike the commonly used two-user OFDM-NOMA VLC model, in this letter, an arbitrary number of multiplexed users is considered when formulating the sum rate maximization problem. Moreover, both user-level and subcarrier-level power optimization are taken into account to improve the system performance. The corresponding theoretical analysis is presented, with the consideration of optimal decoding order and each user's required signal-to-interference-plus-noise ratio (SINR). Given a fixed power constraint on each OFDM subcarrier, we derive the optimal power ratio for an arbitrary number of multiplexed users in OFDM-NOMA VLC. Numerical results show that with a required SINR of 3 dB, the proposed enhanced power allocation algorithm has 39.59% and 40.11% sum-rate enhancement when compared to optimized fixed power allocation algorithm and gain ratio power allocation algorithm, respectively.

Energy efficient massive multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) antenna systems have received a great deal of attention for use in industrial network applications due to the possibility of reducing operation costs and carbon footprint. One of the difficulties in realizing high energy efficiency (EE) massive MIMO-OFDM antenna systems is the high peak-to-average power ratio (PAPR) of the signal, which seriously limits the efficiency of power amplifiers (PA). Selected mapping (SLM) is a powerful PAPR reduction scheme for OFDM related systems, however, there is implicit consensus that SLM could not be applied to massive MIMO-OFDM antenna systems due to its high computational complexity and side information (SI) burden. In this paper, we propose an SLM based PAPR reduction scheme that can be applied to massive MIMO-OFDM antenna systems based on antenna grouping. Using the antenna grouping based suboptimal scheme, we show that an SLM based PAPR reduction scheme can be successfully applied to massive MIMO-OFDM antenna systems with significant increase of EE. The proposed scheme has very high flexibility with various adjustable parameters, so one can easily choose the settings they desire between performance-complexity trade-off. Numerical analysis shows that the propose scheme can increase EE by 18.69% compared to the conventional system.

Secure communication in underlay spectrum sharing multi-user massive multiple-input multiple-output systems with active eavesdropping is investigated. The primary base-station (PBS) shares its licensed spectrum with the secondary base-station (SBS), which constrains it’s transmit power subject to a primary interference temperature. The active eavesdropper contaminates the uplink pilots in an attempt to intercept the confidential downlink transmissions toward the legitimate primary/secondary user nodes (PUs/SUs). The achievable PU/SU rates, the rates leaked into the active eavesdropper, and the achievable secrecy rates are derived for the finite and infinite PBS/SBS antenna regimes. A power-ratio-based active pilot attack detection scheme is investigated, and thereby, the probability of detection is derived. Our analysis and simulation results reveal that the active pilot attacks severely degrades the achievable secrecy rates even in the asymptotic PBS/SBS antenna regime. Hence, in order to provide guaranteed physical layer security, the active pilot attacks must be detected, and dynamic pilot decontamination techniques need to be employed.

In this letter, we consider the channel estimation problem under pilot contamination in massive multiple-input multiple-output (MIMO) systems. We propose a novel channel estimation scheme, which decomposes the space supported by the covariance matrix of the received signals into three subspaces using factor analysis. For a large number of base station antennas, a practically interference-free subspace is created, in which an accurate channel estimate can be obtained. The proposed factor analysis based scheme exploits spatial correlation to significantly reduce the computational complexity. Simulation results are provided to show the efficacy of the proposed scheme.

Energy-efficient and secure wireless communications have recently earned tremendous interests due to economic, environmental and military concerns. This paper investigates the tradeoff between the secrecy throughput and the energy efficiency in cognitive radio networks (CRNs), where primary and secondary users with different priorities of spectrum access can either interfere or cooperate with each other. To gain an understanding of the intricate effects that system parameters have on network performance, we exclusively focus on characterizing several key aspects that may have potential impacts on secure CRNs, including the transmission power, the number of interfering users, and the interference resistance coefficient. Based on the obtained analytical results, we further propose a cooperative spectrum sharing paradigm to improve both the secrecy throughput and the energy efficiency of primary users. The main idea is that primary users lease a fraction of licensed spectrum to secondary users and in return, the secondary transmitter acts as both a relay for primary transmissions and a friendly jammer against eavesdropping. Both theoretical and numerical results disclose that: (i) When the interference from secondary transmitters is small, there is an optimal transmission power that maximizes the secrecy throughput for primary users compared to CRNs without the security issue.

Software Defined Wireless Sensor Networks is a new and emerging network paradigm that seeks to address the impending issues in Wireless sensor networks. It is formed by applying Software Defined Networking to wireless sensor networks whose basic tenet is the centralisation of control intelligence of the network. The centralisation of the controller rouses many challenges such as security, reliability, scalability and performance. A distributed control system is proposed in this paper to address issues arising from and pertaining to the centralised controller. Fragmentation is proposed as a method of distribution, which entails a two level controller structure consisting of local controllers closer to the infrastructure elements and a global controller which has a global view of the entire network. Distributed controller system brings several advantages and the experiments carried out shows that it performs better than a central controller. Furthermore the results also show that fragmentation improves the performance and thus have a potential to have major impact in the IoT.