Non-orthogonal multiple access (NOMA) is widely recognized for enhancing the energy and spectral efficiency through effective radio resource sharing. However, uplink NOMA systems face greater challenges than their downlink counterparts, as their bit error rate (BER) performance is hindered by an inherent error floor due to error propagation caused by imperfect successive interference cancellation (SIC). This paper investigates the BER performance improvements enabled by reconfigurable intelligent surfaces (RISs) in multi-user uplink NOMA transmission. Specifically, we propose a novel RIS-assisted uplink NOMA design, where the RIS phase shifts are optimized to enhance the received signal amplitudes while mitigating the phase rotations induced by the channel. To achieve this, we first develop an accurate channel model for the effective user channels, which facilitates our BER analysis. We then introduce a channel alignment scheme for a two-user scenario, enabling efficient SIC-based detection and deriving closed-form BER expressions. We further extend the analysis to a generalized setup with an arbitrary number of users and modulation orders for quadrature amplitude modulation signaling. The analysis is also extended to consider imperfect channel state information (CSI) knowledge and the multi-antenna base station (BS) cases. Using the derived BER expressions, we develop an optimized uplink NOMA power allocation (PA) scheme to minimize the average BER while satisfying the user transmit power constraints. It will be shown that the proposed NOMA detection scheme, in conjunction with the optimized PA strategy, eliminate SIC error floors at the base station. The theoretical BER expressions are validated using simulations, which confirms the effectiveness of the proposed design in eliminating BER floors.

The increasing complexity and density of modern networks necessitate advanced, AI-driven solutions to manage traffic efficiently and maintain high-quality service. In this paper, we present a novel reinforcement learning (RL) framework designed to optimize handover parameters for load balancing in cellular networks. Our framework adopts a hierarchical multi-agent RL approach. Closely adjacent cells (a.k.a., cluster) are controlled by cluster-level agents, whereas inter-cluster parameters are controlled by a network-level agent. By intricate design of state spaces and agent communication, both cluster-level and network-level agents work collaboratively to enhance network performance in terms of throughput and coverage. This method reduces the action and state spaces for each agent, facilitating faster learning, scalable network-wide control, and more efficient decision-making. Our simulation results demonstrate significant improvements in downlink throughput with respect to fully decentralized agents. Our approach incurs negligible throughput loss when compared to a fully centralized agent with full knowledge of the entire network. Our approach not only achieves scalable load balancing with minimal overhead but also allows for customizable reward functions tailored to different network needs.

As the demand for mobile data continues to grow, the energy consumption of mobile networks becomes a major concern. Specifically, base stations account for over 76% of energy usage in mobile networks. We consider a two-tier cellular system, one offering basic coverage while the other offers extra capacity. To save energy, existing network features can opportunistically shut down capacity layer cells when physical resources are lightly utilized. Nevertheless, this is performed without guaranteeing coverage cells can maintain the sought user-centric service quality. To address this challenge, we propose a machine learning (ML)-aided search approach that dynamically designs energy-saving configurations for each cell in each hour while being constrained with a pre-defined quality of service (QoS) measure. This ML model was trained using data collected from 10,283 cells of a live network. We introduce two different approaches to provide these settings: Adaptive QoS Threshold Optimization Algorithm (AQTOA) and an Exhaustive Search (ES) baseline. AQTOA is a low complexity ML-aided search algorithm designed to determine the optimal shutdown threshold for capacity cells while ensuring that QoS requirements are met. Through extensive live network experimentation, the AQTOA results indicate a 1.8% improvement in energy savings compared to the earlier static settings models while maintaining a more strict QoS level than the one addressed in the previous work.

The exponential growth of mobile data traffic has intensified the need for energy-efficient and fair resource allocation in cellular networks. This paper addresses this challenge through two key contributions: a novel user association (UA) algorithm and a reinforcement learning (RL)-based dynamic power allocation framework employing Proximal Policy Optimization (PPO). The proposed UA algorithm dynamically assigns users to frequency bands to optimize energy efficiency, minimize dropped users, and enhance fairness. The RL agent dynamically adjusts power levels across high-frequency bands to further improve energy efficiency while maintaining Quality of Service (QoS).The simulation results demonstrate that the RL-based power allocation provides over a 15% improvement in energy efficiency compared to fixed full power configurations. Moreover, the proposed UA performs better than the Max-SINR baseline in terms of energy efficiency, load balancing fairness, and dropped users metrics. These findings underscore the potential of combining intelligent UA algorithms with RL-based power control to address the demands of next-generation cellular networks.

The vast proliferation of wireless data networks demands efficient resource allocation strategies to accommodate the increasing number of devices and the dynamic nature of cellular networks. As future networks face challenges like severe congestion and varying traffic demands, achieving satisfactory Quality of Service (QoS) and Quality of Experience (QoE) requires dynamic management. This paper introduces an improved self-optimization framework that adopts deep reinforcement learning (RL) to dynamically adjust key network parameters, such as handover settings, power levels, and MIMO technology. This approach significantly enhances network throughput by effectively balancing load distribution. The proposed framework explores the trade-off between system complexity and performance gains, demonstrating that an agent tailored to optimize a frequently recurring single scenario can outperform generalized agents under specific network conditions.

Federated learning is a machine learning approach where multiple devices collaboratively learn with the help of a parameter server by sharing only their local updates. While gradient-based optimization techniques are widely adopted in this domain, the curvature information that second-order methods exhibit is crucial to guide and speed up the convergence. This paper introduces a scalable second-order method, allowing the adoption of curvature information in federated large models. Our method, coined Fed-Sophia, combines a weighted moving average of the gradient with a clipping operation to find the descent direction. In addition to that, a lightweight estimation of the Hessian's diagonal is used to incorporate the curvature information. Numerical evaluation shows the superiority, robustness, and scalability of the proposed Fed-Sophia scheme compared to first and second-order baselines.

This work investigates the performance of intelligent reflective surfaces (IRSs) assisted uplink nonorthogonal multiple access (NOMA) in energy-constrained networks. Specifically, we formulate and solve two optimization problems; the first aims at minimizing the sum of users’ transmit power, while the second targets maximizing the system-level energy efficiency (EE). The two problems are solved by jointly optimizing the users’ transmit powers and the beamforming coefficients at the IRS, subject to the users’ individual uplink rate and transmit power constraints. A novel and low-complexity algorithm is developed to optimize the IRS beamforming coefficients by optimizing the objective function over the complex circle manifold (CCM). To efficiently optimize the IRS phase shifts over the manifold, the optimization problem is reformulated into a feasibility expansion problem which is reduced to a max-min signal-to-interference-plus-noise ratio (SINR). Then, with the aid of a smoothing technique, the exact penalty method is applied to transform the problem from constrained to unconstrained. The proposed solution is compared against three semi-definite programming (SDP)-based benchmarks which are semi-definite relaxation (SDR), SDP-difference of convex (SDP-DC) and sequential rank-one constraint relaxation (SROCR). The results show that the manifold algorithm provides better performance than the SDP-based benchmarks, and at a much lower computational complexity for both the transmit power minimization and EE maximization problems. The results also reveal that IRS-NOMA is only superior to orthogonal multiple access (OMA) when the users’ target achievable rate requirements are relatively high.

In this paper, we present an intelligent reflective surface (IRS)-assisted physical layer network coding (PNC) system in a two-way relaying channel (TWRC). Specifically, IRS is used to align the effective channels of the two received superimposed signals at the relay, which allows canceling the carrier phase offset (CPO) between the two received signals. The IRS phase shifts are optimized to maximize the received PNC signal amplitude while having a zero CPO constraint. An efficient manifold optimization-based approach is proposed to solve this problem, where the optimization is performed on the complex circle manifold. Moreover, we improve the performance of channel-coded IRS-assisted PNC by introducing the weighted non-binary PNC (WN-PNC) scheme, where the binary data are mapped to, and encoded over, Galois Fields (GFs). We present two WN-PNC cases where the data is encoded over GF(4) and GF(8), then modulated using quadrature phase shift keying (QPSK) and 8-quadrature amplitude modulation (8-QAM), respectively. We also design proper PNC mapping functions for both cases, ensuring that no PNC ambiguity can occur at the relay. Our simulation results show the efficacy of the proposed manifold optimization-based approach and the error performance improvement of the WN-PNC over the binary PNC case.

Intelligent reflecting surfaces (IRSs) continue to gain a growing research interest for their potential to support next-generation wireless communications without incurring additional power consumption. In this work, we propose a deep reinforcement learning (DRL)-driven and IRS-aided active/passive beamforming solution for multi-user multiple-input single-output (MISO) settings in beyond 5G networks, which is both lightweight and energy-efficient. The proposed solution is based on a hybrid finely-engineered design that leverages two Twin-Delayed DDPG (TD3) agents. Compared to classical optimization techniques, our numerical evaluation shows that the proposed DRL approach achieves 60% reduction in online computation complexity at the expense of only 1 dB higher power consumption.

Balancing traffic among cellular networks is very challenging due to many factors. Nevertheless, the explosive growth of mobile data traffic necessitates addressing this problem. Due to the problem complexity, data-driven self-optimized load balancing techniques are leading contenders. In this work, we propose a comprehensive deep reinforcement learning (RL) framework for steering the cell individual offset (CIO) as a means for mobility load management. The state of the LTE network is represented via a subset of key performance indicators (KPIs), all of which are readily available to network operators. We provide a diverse set of reward functions to satisfy the operators' needs. For a small number of cells, we propose using a deep Q-learning technique. We then introduce various enhancements to the vanilla deep Q-learning to reduce bias and generalization errors. Next, we propose the use of actor-critic RL methods, including Deep Deterministic Policy Gradient (DDPG) and twin delayed deep deterministic policy gradient (TD3) schemes, for optimizing CIOs for a large number of cells. We provide extensive simulation results to assess the efficacy of our methods. Our results show substantial improvements in terms of downlink throughput and non-blocked users at the expense of negligible channel quality degradation.

Wireless networks have been going through tremendous proliferation recently. As a result, a continuous configuration and management are necessary to sustain a balanced performance while facing such continued growth and endless changes. A self-managed network is required to replace manual management, which is costly, time-consuming, and error-prone. In this paper, we propose a machine-learning-based cellular network management system. The proposed system aims to enhance the network stability and adaptability to temporal changes (e.g., load imbalances across cells). The presented approach is a deep reinforcement learning scheme that enables a network manager to learn a policy that maximizes the network average sum throughput while trying to minimize the consumed energy and the number of blocked users. In addition to controlling the transmitted power and the cell individual offset, MIMO can be switched ON and OFF to control the consumed energy without affecting the quality of service. This results in a hybrid action space, i.e., our action vector has some binary actions as well as continuous actions. We present a novel algorithm to deal with this hybrid action space. Our results reveal that our proposed algorithm is flexible, efficient, and reliable. We report significant performance gains compared to some baselines (without self-management) and previously proposed algorithms.

In the Internet of Things (IoT) era, data freshness is critical for real-time monitoring and control applications. Data freshness is quantified via the Age of Information (AoI), which tracks the age of the most recent received packet at the destination. This paper utilizes a spatiotemporal mathematical model to characterize the AoI of a target IoT link that exists within a large-scale IoT network. The large-scale IoT network is modeled by a heterogeneous Poisson field (HPF) of interferers. Then, the AoI of the target link, with a single packet storage capability, is characterized via an absorbing Markov chain that accounts for the interwoven effects of packet size, transmission rate, and interfering IoT devices. In particular, the proposed model investigates the impact of packet segmentation in order to operate at a reliable rate in the presence of IoT interference. To this end, the AoI of preemptive and non-preemptive transmission schemes are studied and compared. Comparing the AoIs of the preemptive and non-preemptive transmission schemes, the results show that no scheme always outperforms the other. In contrast, the number of segments and preemption scheme should be determined based on the packet size, arrival rate, and interference congestion level to minimize the AoI.

This paper presents a novel approach for power-constrained internet of things (IoT) networks that employ non-orthogonal multiple access (NOMA) and are assisted by an intelligent reflecting surface (IRS) for uplink transmissions. The main objective of this work is to maximize the sum rate of power-constrained IoT networks by jointly designing the IRS phase shifts and the users’ transmit power allocation. The proposed solution optimizes the power allocation and phase shifts alternatively. We devise a novel approach to optimize the IRS phase shifts that is based on manifold optimization techniques. Specifically, the IRS phase shifts optimization problem is formulated and solved over the complex circle manifold. Our results show that the proposed method outperforms the widely used semi-definite relaxation (SDR) technique as higher sum rates with less power consumption can be achieved.

Cellular networks' congestion has been one of the most common problems in cellular networks due to the huge increase in network load resulted from enhancing communication quality as well as increasing the number of users. Since mobile users are not uniformly distributed in the network, the need for load balancing as a cellular networks' self-optimization technique has increased recently. Then, the congestion problem can be handled by evenly distributing the network load among the network resources. Lots of research has been dedicated to developing load balancing models for cellular networks. Most of these models rely on adjusting the Cell Individual Offset (CIO) parameters which are designed for self-optimization techniques in cellular networks. In this paper, a new deep reinforcement learning-based load balancing approach is proposed as a solution for the LTE Downlink congestion problem. This approach does not rely only on adapting the CIO parameters, but it rather has two degrees of control; the first one is adjusting the CIO parameters, and the second is adjusting the eNodeBs' transmission power. The proposed model uses Double Deep Q-Network (DDQN) to learn how to adjust these parameters so that a better load distribution in the overall network is achieved. Simulation results prove the effectiveness of the proposed approach by improving the network overall throughput by up to 21.4% and 6.5% compared to the base-line scheme and the scheme that only adapts CIOs, respectively.

In this paper, we propose a reinforcement learning (RL) approach to design an access scheme for secondary users (SUs) in a cognitive radio (CR) network. In the proposed scheme, we introduce a deep Q-network to enable SUs to access the primary user (PU) channel based on their past experience and the history of the PU network's automatic repeat request (ARQ) feedback. In essence, SUs cooperate to avoid collisions with other SUs and, more importantly, with the PU network. Since SUs cannot observe the state of the PUs queues, they partially observe the system's state by listening to the PUs' ARQ packets. To model this system, a Partially Observable Markov Decision Process (POMDP) is adopted, and an RL deep Q-network is employed for the SUs to learn the best actions. A comparative study between the proposed scheme with baseline schemes from the literature is presented. We also compare the proposed scheme with the perfect sensing system (which constitutes an upper bound on the performance) and the system exploiting only the last ARQ feedback. Our results show that the proposed RL based access scheme yields comparable performance to the baseline ARQ-based access schemes, yet, with minimal knowledge about the environment compared to the baseline which assumes perfect knowledge of key system parameters, e.g., PUs arrival rates. On the contrary, our proposed scheme autonomously learns these parameters and, hence, dynamically adapts to their variation.

In this paper, we propose a Reinforcement Learning-based MAC layer protocol for cognitive radio networks, based on exploiting the feedback of the Primary User (PU). Our proposed model relies on two pillars, namely an infinite-state Partially Observable Markov Decision Process (POMDP) to model the system dynamics besides a queuing-theoretic model for the PU queue, where the states represent whether a packet is delivered or not from the PU's queue and the PU channel state. Based on the stability constraint for the primary user queue, the quality of service (QoS) for the PU is guaranteed. Towards the paper's objectives, three Reinforcement Learning approaches are studied, namely Q-Learning, Deep Q-Network (DQN), and Deep Deterministic Policy Gradient (DDPG). Our ultimate objective is to enhance the channel access techniques in the MAC protocols by solving the POMDP without any prior knowledge of the environment.

In this paper, we consider the problem of channel feedback in massive multiple-input-multiple-output (MIMO) systems. For the downlink scenario, we present a detailed comparison between the performance of the quantized and the analog channel feedback schemes for the case of having multiple antenna users. Both schemes’ performance is evaluated by deriving an upper bound on the rate gap between the rate of the system with perfect channel state information (CSI) and with imperfect CSI for both feedback schemes. We compare the two schemes, namely, quantized channel feedback and analog channel feedback, under the same resources allocated for channel feedback for a fair comparison. Moreover, we consider two different downlink transmission schemes; the first one does not consider power allocation across the streams and the second one does power allocation (water-filling) across the streams. Our results show that the analog feedback scheme performs better in the low signal to noise (SNR) region when performing power allocation across the multiple data streams. However, the quantized channel feedback scheme performs better at the high SNR region, where the quantized CSI can provide a better approximation of the actual CSI. Finally, simulation results are presented to verify our theoretical analysis and demonstrate our conclusions.

We consider the problem of jointly optimizing the transmission power and cell individual offsets (CIOs) in the downlink of cellular networks using reinforcement learning. To that end, we reformulate the problem as a Markov decision process (MDP). We abstract the cellular network as a state, which comprises of carefully selected key performance indicators (KPIs). We present a novel reward function, namely, the penalized throughput, to reflect the tradeoff between the total throughput of the network and the number of covered users. We employ the twin deep delayed deterministic policy gradient (TD3) technique to learn how to maximize the proposed reward function through the interaction with the cellular network. We assess the proposed technique by simulating an actual cellular network, whose parameters and base station placement are derived from a 4G network operator, using NS-3 and SUMO simulators. Our results show the following: 1) optimizing one of the controls is significantly inferior to jointly optimizing both controls; 2) our proposed technique achieves 18.4% throughput gain compared with the baseline of fixed transmission power and zero CIOs; 3) there is a tradeoff between the total throughput of the network and the number of covered users.

Scheduling in 5G networks is a challenging task due to the heterogeneous Quality of Service (QoS) requirements of traffic sources. In this paper, we consider the problem of uplink scheduling in 5G networks for mixed traffic that includes Ultra-Reliable Low Latency Communications (URLLC) devices and enhanced Mobile Broad-Band (eMBB) users. For this purpose, a mathematical model for Grant Free (GF) services is derived for the k-repetitions Hybrid Automatic Repeat reQuest (HARQ). We formulate the scheduling problem as a mixed-integer non-linear programming optimization problem. We introduce a complete system model that includes grant-free and grant-based subsystems. We then introduce our proposed solution to the scheduling problem that addresses the two traffic types. Different scheduling techniques are then compared and a performance upper bound is added as a reference. The results show that the proposed technique provides near-optimal results and outperforms other scheduling techniques with a significant complexity reduction.

The effects ofquantizationandcodingon the estimation quality of Gauss-Markov processes are considered, with a special attention to the Ornstein-Uhlenbeck process. Samples are acquired from the process, quantized, and then encoded for transmission using eitherinfinite incremental redundancy(IIR) orfixed redundancy(FR) coding schemes. A fixedprocessingtime is consumed at the receiver for decoding and sending feedback to the transmitter. Decoded messages are used to construct a minimum mean square error (MMSE) estimate of the process as a function of time. This is shown to be an increasing functional of theage-of-information(AoI), defined as the time elapsed since the sampling time pertaining to the latest successfully decoded message. Such functional depends on the quantization bits, codewords lengths and receiver processing time. The goal, for each coding scheme, is to optimize sampling times such that the long-term average MMSE is minimized. This is then characterized in the setting ofgeneral increasing functionals of AoI,not necessarily corresponding to MMSE, which may be of independent interest in other contexts. We first show that the optimal sampling policy for IIR is such that a new sample is generated only if the AoI exceeds a certainthreshold,while for FR it is such that a new sample is deliveredjust-in-timeas the receiver finishes processing the previous one.Enhancedtransmissions schemes are then developed in order to exploit the processing times to make new data available at the receiver sooner. For both IIR and FR, it is shown that there exists an optimal number of quantization bits that balances AoI and quantization errors, and hence minimizes the MMSE. It is also shown that for longer receiver processing times, the relatively simpler FR scheme outperforms IIR.

In this paper, we propose a machine learning-based indoor/outdoor (IO) user classification algorithm in cellular systems as pertains to 3G networks. We consider different scenarios. The experimental results show that the best machine learning algorithm for IO classification is the boosting algorithm with an accuracy that reaches 88.9%.

In this paper, we consider the problem of energy optimization in mobile networks by enabling the MIMO feature only when necessary. Enabling MIMO features at the base station increases energy consumption unnecessarily under many operating conditions. In this study, we employ machine learning-based approaches to decide on whether a SISO scheme can achieve the required Quality of Experience (QoE). If SISO can satisfy the target QoE, the base-station can decide to switch the MIMO feature off which can result in considerable energy savings. We consider two different machine learning approaches, namely, multi-layer perceptron (MLP) and recurrent neural networks (RNNs), to learn the SISO features from realistic mobile network data. The trained models are tested against the data obtained from MIMO cells in which the MIMO feature is disabled. Our results show the effectiveness of our proposed approach which presents a real-time, automated approach for MIMO enabling decisions.

Balancing traffic among network installed radio base stations is one of the main challenges facing mobile operators because of the unhomogeneous geographical distribution of mobile subscribers in addition to practical and environmental limitations preventing acquiring the best locations to build radio sites. This increases the challenge of satisfying the increasing data speed demand for smartphone users. In this paper, we present a reinforcement learning framework for optimizing neighbor cell relational parameters that can better balance the traffic between different cells within a defined geographical cluster. We present a comprehensive design of the learning framework that includes key system performance indicators and the design of a general reward function. System level simulations show that reinforcement learning based optimization for neighbor cell borders can significantly improve overall system performance; in particular, with a reward function defined as throughput, an improvement up to 50% is achieved.

The effects of quantization and coding on the estimation quality of a Gauss-Markov, namely Ornstein-Uhlenbeck, process are considered. Samples are acquired from the process, quantized, and then encoded for transmission using either infinite incremental redundancy or fixed redundancy coding schemes. A fixed processing time is consumed at the receiver for decoding and sending feedback to the transmitter. Decoded messages are used to construct a minimum mean square error (MMSE) estimate of the process as a function of time. This is shown to be an increasing functional of the age-of-information, defined as the time elapsed since the sampling time pertaining to the latest successfully decoded message. Such (age-penalty) functional depends on the quantization bits, codeword lengths and receiver processing time. The goal, for each coding scheme, is to optimize sampling times such that the long term average MMSE is minimized. This is then characterized in the setting of general increasing age-penalty functionals, not necessarily corresponding to MMSE, which may be of independent interest in other contexts.

The accelerated move toward the adoption of the Industrial Internet-of-Things (IIoT) paradigm has resulted in numerous shortcomings as far as security is concerned. One of the IIoT affecting critical security threats is what is termed as the false data injection (FDI) attack. The FDI attacks aim to mislead the industrial platforms by falsifying their sensor measurements. FDI attacks have successfully overcome the classical threat detection approaches. In this article, we present a novel method of FDI attack detection using autoencoders (AEs). We exploit the sensor data correlation in time and space, which in turn can help identify the falsified data. Moreover, the falsified data are cleaned using the denoising AEs (DAEs). Performance evaluation proves the success of our technique in detecting FDI attacks. It also significantly outperforms a support vector machine (SVM)-based approach used for the same purpose. The DAE data cleaning algorithm is also shown to be very effective in recovering clean data from corrupted (attacked) data.

A status updating communication system is examined, in which a transmitter communicates with a receiver over a noisy channel. The goal is to realize timely delivery of fresh data over time, which is assessed by an age-of-information (AoI) metric. Channel coding is used to combat the channel errors, and feedback is sent to acknowledge updates' reception. In case decoding is unsuccessful, a hybrid ARQ protocol is employed, in which incremental redundancy (IR) bits are transmitted to enhance the decoding ability. This continues for some amount of time in case decoding remains unsuccessful, after which a new (fresh) status update is transmitted instead. In case decoding is successful, the transmitter has the option to idly wait for a certain amount of time before sending a new update. A general problem is formulated that optimizes the codeword and IR lengths for each update, and the waiting times, such that the long term average AoI is minimized. Stationary deterministic policies are investigated, in which the codeword and IR lengths are fixed for each update, and the waiting time is a deterministic function of the AoI. The optimal waiting policy is then derived, and is shown to have a threshold structure, in which the transmitter sends a new update only if the AoI grows above a certain threshold that is a function of the codeword and IR lengths. Choosing the codeword and IR lengths is discussed in the context of binary symmetric channels.

In this paper, we examine the use of autoencoders as an optimization tool for the construction of noncoherent space-time MIMO codes. In particular, we consider the quasi-static block fading channel, where the channel state information is not available at either the transmitter or the receiver, and changes independently between transmissions. Different from traditional constructions which aim to maximize an approximation of the minimum pairwise distance of the constellation, we use the autoencoder to directly target minimizing the probability of error. We show that this different optimization goal leads to constellations with more favorable pairwise distances' distribution and better error performance at low to medium signal to noise ratios where the minimum distance is not the limiting factor. Finally, we present simulation results showing that the constructed codes outperform traditional Grassmannian codes up to a signal-to-noise ratio of 20 dB using the traditional generalized likelihood ratio test detector.

In this paper, we investigate binary frequency shift keying (BFSK) over ambient OFDM signals. By cycling through a sequence of antenna loads providing different phase shifts at the tag, we are able to unidirectionally shift the ambient spectrum either up or down in frequency allowing the implementation of BFSK. We exploit the guard band and the orthogonality of the OFDM subcarriers to avoid both direct-link and adjacent channel interference. Different from energy detection based techniques which suffer from asymmetric error probabilities, the proposed scheme has symmetric error probabilities. Furthermore, we analyze the error performance of the optimal noncoherent detector and obtain an exact expression for the average probability of error. Finally, simulation results corroborate our analysis and show that the proposed scheme outperforms energy detection based schemes available in the literature by up to 3 dB.

In the era of internet‐of‐things (IoT), many applications utilise wireless sensor networks (WSNs)including smart grids (SGs). Designing WSNs to fulfill the SGs requirementsimposes some challenges such as limited power and signal propagationimpairments, especially, in harsh environments. Consequently, saving powerconsumption in WSNs‐based SGs is among the most significant challenges. Thetotal power required at a certain sensor depends on two main parameters: thepacket length and inter‐node distance. This paper investigates the optimalpacket length and inter‐node distance to be utilised in a SG over six differentenvironments aiming at maximising the network lifetime. The investigation isbased on a link‐layer model using Tmote Sky nodes taking into consideration thesix environments impact. A mixed‐integer programming (MIP) model is utilised todetermine the best packet length and number of nodes for maximising the networklifetime. This model analyses the performance of maximum SG network lifetimeover those environments and addresses the inter‐node distance effect on thenetwork lifetime maximisation. Simulation results show that decreasing thenumber of nodes covering a certain area is preferable to prolonging the networklifetime. Furthermore, for the considered models, the longer the packet lengthis, the longer the network lifetime will be.

In recent years, ambient backscatter communications have gained a lot of interests as a promising enabling technology for the Internet-of-Things and green communications. In ambient backscatter communication systems, ultra-low power devices are able to transmit information by backscattering ambient radio-frequency signals generated by legacy communication systems such as Wi-Fi and cellular networks. This paper is concerned with ambient backscatter communications over legacy orthogonal frequency division multiplexing (OFDM) signals. We propose a backscatter modulation scheme that allows backscattering devices to take advantage of the spectrum structure of ambient OFDM symbols to transmit information. The proposed modulation scheme allows both binary and higher-order modulation using noncoherent energy detection. We investigate the detector design and analyze the error performance of the proposed scheme. We provide an exact expression for the error probability for the binary case, whereas accurate approximate expressions for the error probability are derived for the M-ary case. We corroborate our analysis using Monte-Carlo simulation and investigate the effects of varying the OFDM symbol size, maximum channel delay spread, and the number of receive antennas on the error performance. Our numerical results show that the proposed technique outperforms other techniques available in this paper for backscatter communication over ambient OFDM signals in different scenarios.

The vehicle-to-vehicle (V2V) communications channel is highly time-varying, making reliable communication difficult. This problem is particularly challenging because the de facto standard for V2V communications, the IEEE 802.11p standard, is based on the IEEE 802.11a standard, which was designed for the indoor and relatively stationary wireless LAN channel. In particular, the frame structure, which allows large packets and has low pilot density makes channel estimation difficult. In this paper, we propose several semi-blind channel estimation and tracking algorithms that are suitable for highly time-varying channels using the 802.11p frame structure. Two of the proposed schemes utilize the finite alphabet property of the transmitted symbols and utilize pilot information. A third scheme is a variant of decision-directed channel estimation that utilizes knowledge of the preamble. All schemes apply time-domain channel impulse response truncation for improved performance. We compare the performance of the proposed schemes using six different V2V channel models. The proposed schemes realize huge performance gains over previously proposed ones, reaching 20 dB in some cases, where previously proposed schemes are unusable. These performance gains are realized for all the V2V channel models, at different vehicle velocities, and for all modulation schemes and packet sizes. Two of the proposed schemes are low-complexity schemes that avoid expensive search operations, yet offer significantly improved performance.

Routing protocols in multi-hop cognitive radio networks (CRNs) can be classified into two main categories: local and global routing. Local routing protocols aim at decreasing the overhead of the routing process while exploring the route by choosing, in a greedy manner, one of the direct neighbors. On the contrary, global routing protocols choose the optimal route by exploring the whole network to the destination paying the flooding overhead cost. In this paper, we propose a primary user-aware$k$-hop routing scheme where$k$is the discovery radius. This scheme can be plugged into any CRN routing protocol to adapt, in real time, to network dynamics like the number and activity of primary users. The aim of this scheme is to cover the gap between local and global routing protocols for CRNs. It is based on balancing the routing overhead and the route optimality, in terms of primary users avoidance, according to a user-defined utility function. We analytically derive the optimal discovery radius ($k$) that achieves this target. Evaluations on NS2 with a side-by-side comparison with traditional CRNs protocols show that our scheme can achieve the user-defined balance between the route optimality, which in turn reflected on throughput and packet delivery ratio, and the routing overhead in real time.

In this paper, we consider the problem of Angle of Departure (AoD) based channel feedback in Frequency Division Duplex (FDD) massive Multiple-Input Multiple-Output (MIMO) systems with multiple antennas at the users. We consider the use of Zero-Forcing Block Diagonalization (BD) as the downlink precoding scheme. We consider two different cases; one in which the number of streams intended for a user equals the number of antennas at that user and the other case in which the number of streams is less than the number of user antennas. BD requires the feedback of the subspace spanned by the channel matrix at the user or a subspace of it in the case of having less number of streams than the number of antennas at a specific user. Based on our channel model, we propose a channel feedback scheme that requires less feedback overhead compared to feeding back the whole channel matrix. Then, we quantify the rate gap between the rate of the system with perfect Channel State Information (CSI) at the massive MIMO Basestation (BS) and our proposed channel feedback scheme for a given number of feedback bits. Finally, we design feedback codebooks based on optimal subspace packing in the Grassmannian manifold. We show that our proposed codes achieve performance that is very close to the performance of the system with perfect CSI at the BS.

In recent years, ambient backscatter communications have gained a lot of interest as a promising enabling technology for Internet-of-Things and green communications. In ambient backscatter communication systems, battery-less devices are able to transmit information by backscattering ambient RF signals generated by legacy communication systems such as digital TV broadcasting, Wi-Fi, or cellular. This paper is concerned with ambient backscatter communications over legacy cellular OFDM signals. We propose a novel modulation scheme that allows backscattering devices to take advantage of the spectrum structure of ambient OFDM symbols to transmit information. We analyze the error performance of the proposed scheme, provide an exact expression for the error probability, and validate our analysis using Monte-Carlo simulation. We investigate the effects of varying the OFDM symbol size and maximum channel delay spread on the error performance. Our numerical results show that the proposed technique outperforms other techniques available in the literature for backscatter communication over ambient OFDM signals in different scenarios.

In this paper, a cognitive radio system is studied in which the secondary user (SU) leverages the primary user (PU) channel quality indicator feedback (CQI) and the PU automatic repeat request (ARQ). The SU randomly accesses the PU channel with access probabilities based on its spectrum sensing outcome and the PU feedbacks. The SU's access probabilities are selected though an optimization problem with the objective to maximize the SU's throughput while ensuring the stability of the PU's packet queue. This system is modeled using a multidimensional Markov chain. This model enabled us to derive a closed-form expression for the SU's throughput, which is used in the throughput maximization problem. The proposed scheme is shown to improve the SU service rate compared to the system where no PU feedback is exploited by the SU, the system where the SU utilizes only the PU CQI feedback, and the system where the SU utilizes only the PU ARQ feedback.

In this paper, we study full-duplex enabled small cell networks. We consider a two-node network, in which each node can operate in full-duplex. After treating interference as noise, we derive each node's achieved uplink and downlink rate, for both the half-duplex and full-duplex transmissions. Afterwards, the rate maximization problem used in obtaining the achieved rate region is formulated, and a very low complexity algorithm is proposed to obtain the crystallized rate regions. The idea is to explore the boundary points of the rate region and then find their convex hull. Finally, numerical analysis is presented to compare the exact achieved rate regions with the crystallized regions and to validate that the crystallized regions can approximate the exact regions with much less complexity. Also, in some cases, the rate region derived from power adaptation will be confined to the crystallized rate region; which suggests that in these cases, time-sharing will be more beneficial, in terms of data rates, than power adaptation.

In this paper, we consider rate maximization of a single full-duplex (FD) massive MIMO base station (BS) with multiple downlink (DL) and uplink (UL) users. The BS applies transmit precoders on the DL transmission. These precoders are designed to reduce the multiuser interference among DL transmissions and to reduce the self-interference (SI) level from the DL transmissions at the BS UL receiving antennas. The self-interference is reduced by zero- nulling the DL transmissions at some of the UL receiving antennas. We derive lower bounds on the achievable DL and UL capacities. Based on the derived bounds, we optimize over the ratio of the transmit to receive antennas at the base station to maximize the achievable capacities. We also optimize over the portion of UL receive antennas that are zero-nulled by the DL transmit precoders to limit the effect of SI on the UL transmissions. Two different formulations of the rate region maximization problem are presented. The first formulation, which maximizes the UL rate for a given DL rate, is proved to be a convex optimization problem; for this formulation, an exact solution for the optimization problem is derived. The second formulation, which maximizes the DL rate for a given UL rate, is proved to be non-convex; therefore, the dual problem is formulated and solved. Numerical results validate the derived DL and UL bounds as well as the proposed rate maximization solutions.

We characterize time and power allocations to optimize the sum-throughput of a Wireless Powered Communication Network (WPCN) with Non-Orthogonal Multiple Access (NOMA). In our setup, an Energy Rich (ER) source broadcasts wireless energy to several devices, which use it to simultaneously transmit data to an Access Point (AP) on the uplink. Differently from most prior works, in this paper we consider a generic scenario, in which the ER and AP do not coincide, i.e., are two separate entities. We study two NOMA decoding schemes, namely Low Complexity Decoding (LCD) and Successive Interference Cancellation Decoding (SICD). For each scheme, we formulate a sum-throughput optimization problem over a finite horizon. Despite the complexity of the LCD optimization problem, due to its non-convexity, we recast it into a series of geometric programs. On the other hand, we establish the convexity of the SICD optimization problem and propose an algorithm to find its optimal solution. Our numerical results demonstrate the importance of using successive interference cancellation in WPCNs with NOMA, and show how the energy should be distributed as a function of the system parameters.

In this paper, we consider the design of multi-resolution non-coherent multiple-input multiple-output (MIMO) systems that enable Unequal Error Protection (UEP). A method for designing multi-layer non-coherent Grassmannian constellations is introduced. Specifically, the proposed method yields multi-layer constellations that are amenable to a natural set partitioning strategy. The resulting subsets from such partitioning are used to encode the more protected symbols. On the other hand, the less protected symbols are mapped to points within these subsets. Furthermore, we present two methods to establish the link between the gain of the more protected layer and the design parameters of this construction. Finally, we exploit the underlying structure to develop a sequential decoding approach. Numerical results suggest that employing such decoding scheme leads to computational savings with respect to the optimal decoding while maintaining comparable performance.

Due to the fast boom of security threats in wireless sensor networks (WSNs) sensitive applications, we propose a game-theoretic protection approach for sensor nodes in a clustered WSN based on a repeated game. The proposed game model is developed for detecting malicious sensor nodes that drop the high priority packets (HPPs) aiming at maximizing the high priority data trustworthiness (HPT). Simulation results indicate the improved HPT of the proposed protection model which attains the Pareto optimal HPT as compared to a non-cooperative defense mechanism.

Wireless Sensor Networks (WSNs) security plays an intrinsic role to guarantee efficient data transmission, stable network topologies, and robust routing algorithms. In this paper, we propose a modified Stackelberg game of a previous work for high assurance of data trustworthiness in a Power Grid Network (PGN). The proposed approach is presented to mitigate a more severe attack scenario compared to that considered in the previous work; this attack scenario frequently manipulates sets of the deployed nodes in the PGN, which cannot be treated using the previously proposed approach. Our proposed scheme reduces the required number of nodes to be protected to achieve the desired data trustworthiness. Simulation results prove efficient detection for corrupted transmitted data based on limited number of nodes as compared to the previously proposed approach.

Device-to-Device (D2D) communications have been highlighted as one of the promising solutions to enhance spectrum utilization of LTE-Advanced networks. In this paper, we consider a D2D transmitter cooperating with a cellular network by acting as a relay to serve one of the cellular users. We consider the case in which the D2D transmitter is equipped with an energy harvesting capability. We investigate the trade-off between the amount of energy used for relaying and the energy used for decoding the cellular user data at the relaying node. We formulate an optimization problem to maximize the cellular user rate subject to a minimum rate requirement constraint for the D2D link. Moreover, we consider the case when receiving nodes are equipped with successive interference cancellation (SIC) capability and investigate the effect of using SIC on our proposed system performance. Finally, we show via numerical simulations the benefits of our cooperation-based system as compared to the non-cooperative scenario.

Due to the increasing demand for higher data rates and the congestion in communication systems, new research is focusing on the cooperation between the two most successful communication systems, LTE and WiFi. The overall performance of a WiFi system degrades with increasing the number of served users due to collisions. We propose in this paper a novel scheme for LTE and WiFi coexistence, where an LTE femto Base Station cooperates with a WiFi Access Point to maximize both of their profits. Our proposed scheme has the advantage of relieving a congested WiFi system. Thus, this creates a time gap for the LTE system to transmit its data. In addition, we investigate the capability of WiFi and LTE systems to work simultaneously under a certain maximum interference limit. We have formulated a multi- objective optimization problem for maximizing the rate of the WiFi system and the capacity of the LTE system. We developed an algorithm based on particle swarm optimization to determine the appropriate time ratios for WiFi and LTE transmission, the transmitting power of LTE under WiFi transmission, and the number of WiFi nodes to be transferred to LTE system. Simulation results confirm the capability for LTE to transmit besides WiFi without affecting its transmission rate.

Energy harvesting has been gaining a lot of attention in the past decade due to its ability to provide a -virtually-endless energy supply. Nodes in a Wireless Powered Communication Network (WPCN) depend, totally or partially, on the energy harvested from the Central Node (CN) which has a constant power supply. This work addresses a solution to the problem of lack of fairness in the distribution of energy broadcast to nodes from the CN. The solution presented here depends on cooperation between nodes, in which nodes that have harvested more energy can help other nodes with their transmission to achieve fairness. The main objective is to achieve a maximized common throughput by selecting the best relay node assuming Amplify-and-Forward relaying. An optimization problem is formulated to allocate time and energy resources for nodes' transmissions and relaying. The formulated optimization problem is proved to be convex, which allows for efficient solution calculation. Simulation results show the improved performance of our proposed cooperation and relay selection algorithms as compared to the no-cooperation scenario.

The authors propose a game‐theoretic approach using the Stackelberg game for securing cognitive radio sensor network (CRSN) against the spectrum sensing data falsification attack; this attack aims at corrupting the spectrum decisions communicated from the ambient sensor nodes (ASNs) to the fusion centre by imposing interference power. The proposed game approach is developed for two different attack–defence scenarios. In the first scenario, the attacker selects to attack a group of delivered reports of the ASNs that have a protection degree below a specific threshold. In the second scenario, the attacker applies its maximum attack interference power to the delivered reports of the ASNs that have been reported to be least protected in the previous round. Simulation results indicate the improved performance of the proposed protection model as compared with two baseline defence mechanisms, namely, the random and equal‐protection defence mechanisms with static signal‐to‐noise ratio (SNR) and variable SNRs. Consequently, Stackelberg game features prove to be beneficial for securing communication over CRSN.

We consider the problem of optimizing the performance of a cooperative cognitive radio user subject to constraints on the quality-of-service (QoS) of the primary user (PU). In particular, we design the probabilistic admission control parameter of the PU packets in the secondary user (SU) relaying queue and the randomized service parameter at the SU under non-work-conserving (non-WC) and WC cooperation policies. In the non-WC policy, two constrained optimization problems are formulated; the first problem is maximizing the SU throughput while the second problem is minimizing the SU average delay. In both problems, a constraint is imposed on the maximum allowable average delay of the PU. We show the equivalence of the two problems and develop a low-complexity line search algorithm to find the optimal parameters. Subsequently, the idea of optimizing the SU average delay is developed for the more complex WC policy, for its superior resource utilization and performance. Due to the sheer complexity of this optimization problem, we formulate another problem whose solution yields a suboptimal upper bound on the optimal SU delay. Afterwards, a practical WC-policy-based algorithm is designed in order to closely approach the optimal value of the SU delay. We show, through numerical results, that the proposed cooperation policies represent the best compromise between enhancing the SU QoS and satisfying the PU QoS requirements. Furthermore, the superior performance of the suboptimal WC policy over the non-WC policy is illustrated. Finally, the merits of the WC-policy-based algorithm are demonstrated through extensive simulations.

In this paper, we characterize the total degrees of freedom (DoFs) of the full-duplex asymmetric multiple-input multiple- output (MIMO) three-way channel. Each node has a separate-antenna full-duplex MIMO transceiver with a different number of antennas, where each antenna can be configured for either signal transmission or reception. We study this system under two message configurations; the first configuration is when each node has two unicast messages to be delivered to the two other nodes, while the second configuration is when each node has two unicast messages as well as one broadcast message to be delivered to the two other nodes. For each configuration, we first derive upper bounds on the total DoF of the system. Cut-set bounds in conjunction with genie-aided bounds are derived to characterize the achievable total DoF. Afterward, we analytically derive the optimal number of transmit and receive antennas at each node to maximize the total DoF of the system, subject to the total number of antennas at each node. Finally, the achievable schemes for each configuration are constructed. The proposed schemes are mainly based on zero-forcing and null-space transmit beamforming. We show that the derived outer and inner bounds on the total DoF are tight for each message configuration.

We consider the design of space-time codes for the multiple-input multiple-output multicast communication systems with two classes of receivers. The first class comprises high-resolution (HR) receivers which have access to reliable channel state information (CSI) and can perform coherent detection, and the second class comprises low-resolution (LR) receivers which do not have access to CSI and can only perform non-coherent detection. We propose a layered encoding structure in which LR information available to both classes of receivers is encoded using Grassmannian constellations, and an incremental component, which is available only to the HR receivers, is encoded in the particular bases of the transmitted Grassmannian constellation points, thereby giving rise to constellations on the Stiefel manifold. The proposed structure enables reliable coherent communication of the HR information without compromising the reliability with which the basic LR information is non-coherently communicated. To effect rate-efficient communication of the incremental, HR layer, we use optimization methods on the Stiefel manifold to develop a novel technique for designing the unitary constellations directly. This approach alleviates the restriction imposed by the traditional techniques in which unitary space-time codes are constructed from scalar constellations. As such, this approach enables better control of the distance spectrum of the developed constellations and more effective utilization of the degrees of freedom that underlie the Stiefel manifold. For the LR receivers, we use maximum likelihood detection, whereas for the HR receivers, we develop a computationally-efficient two-step sequential detector which detects the LR information prior to detecting the incremental component superimposed on it. The detectors and the layered structure with the aforementioned constellations enable full diversity and maximum degrees of freedom to be achieved on the Grassmann and Stiefel manifolds.

In this paper, we propose a game-theoretic protection model for Wireless Sensor Network (WSN) nodes within a cluster based on a Stackelberg game. The proposed game approach is developed for two different attack-defense scenarios. In the first scenario, the attacker selects to attack a group of nodes that have a protection degree below a specific threshold. In the second scenario, the attacker targets the nodes that have been reported to be least protected in the previous round. Simulation results indicate the improved performance of the proposed protection model as compared to the no-defense case.

In this paper, we develop a geometry-inspired methodology for generating systematic and structured Grassmannian constellations with large cardinalities. In the proposed methodology we begin with a small close-to-optimal “parent” Grassmann constellation. Each point in this constellation is augmented with a number of “children” points, which are generated along a set of geodesics emanating from that point. These geodesics are chosen to ensure close-to-maximal spacing. In particular, the directions of the geodesics and the distance that each “children” point is moved are chosen to maximize the pairwise Frobenius distance between the resulting constellation points. Although finding these directions directly seems difficult, by embedding the Grassmann manifold on a sphere of larger dimension, we were able to develop structures that are not only simple to generate but that also yield constellations that, under certain conditions, satisfy the maximum distance criterion and lie within a decaying gap from a tight upper bound. Numerical results suggest that the performance of the new constellations is comparable to that of the ones generated directly and significantly better than the performance of the ones generated using the exponential map.

In this paper, we characterize the asymmetric total degrees of freedom (DoF) of a multiple-input multiple-output (MIMO) 3-way channel. Each node has a separate-antenna full-duplex MIMO transceiver with a different number of antennas, where each antenna can be configured for either signal transmission or reception. Each node has two unicast messages to be delivered to the two other nodes. We first derive upper bounds on the total DoF of the system. Cut-set bounds in conjunction with genie-aided bounds are derived to characterize the achievable total DoF. Afterwards, we analytically derive the optimal number of transmit and receive antennas at each node to maximize the total DoF of the system, subject to the total number of antennas at each node. Finally, the achievable schemes are constructed. The proposed schemes are mainly based on zero-forcing and null-space transmit beamforming.

Compressive Sensing has been utilized in Cognitive Radio Networks (CRNs) to exploit the sparse nature of the occupation of the primary users. Also, distributed spectrum sensing has been proposed to tackle the wireless channel problems, like node or link failures, rather than the common “centralized approach” for spectrum sensing. In this paper, we propose a distributed spectrum sensing framework based on consensus algorithms where SU nodes exchange their binary decisions to take global decisions without a fusion center to coordinate the sensing process. Each SU will share its decision with its neighbors, and at every new iteration each SU will take a new decision based on its current decision and the decisions it receives from its neighbors; in the next iteration, each SU will share its new decision with its neighbors. We show via simulations that the detection performance can tend to the performance of majority-rule Fusion Center based CRNs.

We study the problem of optimizing the performance of cognitive radio users with opportunistic real-time applications subject to primary users quality-of-service (QoS) constraints. Two constrained optimization problems are formulated; the first problem is maximizing the secondary user throughput while the second problem is minimizing the secondary user average delay, subject to a common constraint on the primary user average delay. In spite of the complexity of the optimization problems, due to their non-convexity, we transform the first problem into a set of linear programs and the second problem into a set of quasiconvex optimization problems. We prove that both problems are equivalent with identical feasible sets and optimal solutions. We show, through numerical results, that the proposed cooperation policy represents the best compromise between enhancing the secondary users QoS and satisfying the primary users QoS requirements.

Synchronized phasor measurement units (PMUs) play an important role in the process of monitoring, controlling, and protection of today's smart grid networks. Therefore, a strategic placement of these PMUs is essential to perform these tasks. Previous studies in this area mostly concentrate on minimizing the total number of PMUs while maintaining the system fully observable in various contingency situations. However, most of them neglect the communication constraints among PMUs, e.g., the finite communication capacity1available when using power line communications (PLC) among PMUs. In this paper, we introduce two different formulations to solve the problem of optimal PMUs placement (OPP), while taking into consideration a communication constraint for the case where the communication between the PMUs and the control center is established via wired power lines. Therefore, there will be a constraint on the max-flow min-cut in the wired smart grid model. In the first formulation, we find the best location or bus in the grid for the controller to be located in order to support the communication max-flow min-cut constraint. In the second formulation, we fix the controller at a certain bus and find the optimal solution that maintains the networks full observability and also satisfies the communication max-flow min-cut constraint. We also apply our formulations to different IEEE standard bus systems, and our results reveal that there are some buses at which we should avoid placing the controller, and the solution of the conventional OPP problem may not support the max-flow min-cut constraint.

The optimal resource allocation scheme in a full-duplex Wireless Powered Communication Network (WPCN) composed of one Access Point (AP) and two wireless devices is analyzed and derived. AP operates in a full-duplex mode and is able to broadcast wireless energy signals in downlink and receive information data in uplink simultaneously. On the other hand, each wireless device is assumed to be equipped with Radio-Frequency (RF) energy harvesting circuitry which gathers the energy sent by AP and stores it in a finite capacity battery. The harvested energy is then used for performing uplink data transmission tasks. In the literature, the main focus so far has been on slot-oriented optimization. In this context, all the harvested RF energy in a given slot is also consumed in the same slot. However, this approach leads to sub-optimal solutions because it does not take into account the Channel State Information (CSI) variations over future slots. Differently from most of the prior works, in this paper we focus on the long-term weighted throughput maximization problem. This approach significantly increases the complexity of the optimization problem since it requires to consider both CSI variations over future slots and the evolution of the batteries when deciding the optimal resource allocation. We formulate the problem using the Markov Decision Process (MDP) theory and show how to solve it. Our numerical results emphasize the superiority of our proposed full-duplex WPCN compared to the half-duplex WPCN and reveal interesting insights about the effects of perfect as well as imperfect self-interference cancellation techniques on the network performance.

This paper studies optimal resource allocation in a wireless powered communication network with two groups of users; one is assumed to have radio frequency (RF) energy harvesting capability and no other energy sources, while the other group has legacy nodes that are assumed not to have RF energy harvesting capability and are equipped with dedicated energy supplies. First, the base-station (BS) with a constant power supply broadcasts an energizing signal over the downlink. Afterwards, all users transmit their data independently on the uplink using time division multiple access (TDMA). We propose two transmission schemes, namely OPIC and OPAC, subject to different energy constraints on the system. Within each scheme, we formulate two optimization problems with different objective functions, namely maximizing the sum throughput and maximizing the minimum throughput, for enhanced fairness. We establish the convexity of all formulated problems which opens room for efficient solution using standard techniques. Our numerical results show the superiority of our realistic system accommodating legacy nodes, along with RF harvesting nodes, compared to the baseline WPCN system with RF energy harvesting nodes only. Moreover, the results reveal new insights and throughput-fairness trade-offs unique to our new problem setting.

We propose a primary user-aware k-hop routing scheme that can be plugged into any cognitive radio network routing protocol to adapt, in real time, to the environmental changes. The main use of this scheme is to make the compromise required between the route overhead and its optimality based on a user-defined utility function. We analytically derive the optimal discovery radius (k) that achieves this target. Evaluations on NS2 show that our scheme can enhance the current routing protocols in terms of throughput with minimal overhead.

In this paper, we develop two approaches for designing unitarily-constrained space-time block codes, which are suitable for communicating high-resolution information in layered multiple-input multiple-output broadcast channels. Unlike existing space-time codes, which are usually synthesized from standard phase-shift keying (PSK) or quadrature amplitude modulation constellations, the space-time codes proposed herein are designed using direct optimization over the unitary group. In comparison with conventional unitary space-time block codes, including Alamouti code with PSK constellations, the space-time codes generated by the proposed approaches exhibit significantly better performance, more favorable distance spectra and more effective utilization of the degrees of freedom that underlie the unitary group.

The vehicle-to-vehicle (V2V) communication channels are highly time-varying, making reliable communication difficult. This problem is particularly challenging because the standard for V2V communication (IEEE 802.11p standard) is based on the WLAN IEEE 802.11a standard, which was designed for indoor and relatively stationary channels. In this paper, novel channel estimation and tracking algorithms for highly time varying channels are proposed. The proposed algorithms utilize the finite alphabet property of the transmitted symbol, time domain truncation, decision-directed feedback, pilot information as well as V2V channel characteristics. The proposed algorithms improve the overall system performance in terms of bit error rate, enabling the system to achieve higher data rates and larger packet lengths at high relative velocities. Simulation results show that the proposed algorithms achieve improved performance for all the V2V channel models with different velocities, and for different modulation schemes and packet sizes as compared to the conventional least squares estimator and other previously proposed channel estimation techniques for V2V channels.

Smart grid operations face a significant threat from the presence of cyber attacks or bad data that may contaminate the system observations. Therefore, in this paper, we are interested in introducing a new strategy for detecting the presence of bad data in smart grids and we also try to simultaneously estimate it in order to be able to separate the bad data from the system observations. We aim to obtain the attack free observations which reflect the true state of the smart grid. This can be done by defining a joint detection-estimation strategy based on Bayesian and non-Bayesian settings where the costs in general will be functions of the observation. We start with Bayes approach and derive the detector (which, in general, may not be a LRT) and then we set the problem by defining some maximum constraint under the null hypothesis based on the derived detector and minimize certain cost under the alternative hypothesis. Our results reveal that the proposed model is applicable on some cases that other models reported in previous works failed to deal with.

Since the inception of the concept of Wireless Sensor Networks (WSNs), their applicability within the context of environmental monitoring systems (EMS) has constantly been explored. Egypt stands to gain much from WSN-based EMS systems if they are properly applied within its considerably large agricultural industry. A system is developed and tested using locally available hardware within the technical, economic and social constraints of modern day Egypt. We show that the constraints in question mostly impact the routing layer of the WSN necessitating the development of a modified implementation of the LEACH routing protocol. The resulting system is thoroughly evaluated and our recommendations for its deployment are presented.

This paper studies the queues stability in a random access network in which the nodes have finite energy sources. The network consists of two nodes, each having a battery for energy storage. Each transmission consumes a fixed amount of energy, and the batteries are replenished through energy harvesting. Moreover, the nodes leverage the feedback information for collision resolution. In case of a collision, the destination stores the collided packets, and sends negative acknowledgement (NACK). Once the NACK is heard, one of the nodes retransmits its collided packet. The destination uses the retransmitted packet and the stored collided packets to recover the two packets involved in the collision. Therefore, the two nodes are served in two transmissions, but the retransmitting node has used more energy in the process. To characterize the stability region of this system, each node is modeled with two queues, the first for storing packets and the second models the energy in the battery. The random access nature of the network, as well as the interdependence between the battery and packet queues in each node, result in an interacting system of queues. To decouple this interaction, and characterize the stability region, we resort to a dominant system approach for the analysis. The stability region obtained is compared with the stability region of the system without energy constraints, and the losses due to finite energy are identified.

We consider the design of space-time codes for multi-resolution multiple-input multiple-output (MIMO) broadcast communication systems. Two classes of receivers are considered: high-resolution (HR) receivers, which have access to reliable channel state information (CSI) and can perform coherent detection, and low-resolution (LR) receivers which do not have access to CSI and can only perform non-coherent detection. We propose a layered encoding structure, whereby, for the LR receivers, the transmitted codewords are chosen to be points on the Grassmann manifold whereas, for the HR receivers, incremental information is encoded in the particular bases of the transmitted codewords, thereby representing points on the Stiefel manifold. For the HR receivers, we develop a computationally-efficient two-step detector. Using this detector, we show that the proposed structure enables reliable coherent communication of the incremental HR information without compromising the reliability with which the basic LR information is non-coherently communicated. We also show that this structure enables full diversity to be achieved for both LR and HR receivers. Finally, we show that this structure achieves the maximum number of degrees of freedom for non-coherent LR channels and coherent HR channels with unitarily-constrained input signals.

In this paper, we study the stability of two interacting queues under random multiple access in which the queues leverage the feedback information. We derive the stability region under random multiple access where one of the two queues exploits the feedback information and backs off under negative acknowledgement (NACK) and the other, higher priority, queue will access the channel with probability one. We characterize the stability region of this feedback-based random access protocol and prove that this derived stability region encloses the stability region of the conventional random access (RA) scheme that does not exploit the feedback information.

In this paper, we consider a fading relay channel where the source uses two layers source coding with successive refinement. The two source layers are transmitted using superposition coding at the source and relay with optimal power allocation, and successive interference cancellation at the receivers (i.e. relay and destination). The power allocation for the two layers at the source and relay is subject to optimization in order to maximize the expected user satisfaction that is defined by a utility function of the total decoded rates at the destination. We assume that only the channel statistics are known. The relay is half-duplex and applies decode and forward. We characterize the expected utility function in terms of the channel statistics of the fading channels, and we solve the optimization problem using the numerical random search method. We provide many numerical examples to show the prospected gains of using the relay on the expected utility for different channel conditions. Furthermore, we obtain that for some conditions, it is optimal to send only one layer.

Spectrum Sensing in wideband cognitive radio networks is considered one of the challenging issues facing opportunistic utilization of the frequency spectrum. Collaborative compressive sensing has been proposed as an effective technique to alleviate some of these challenges through efficient sampling that exploits the underlying sparse structure of the measured frequency spectrum. In this paper, we propose to model this problem as a compressive support recovery problem, and apply the adaptive Sequential Compressive Sensing (SCS) approach to recover spectrum holes. We propose several fusion techniques to apply the proposed approach in a collaborative manner. The experimental analysis through simulations shows that the proposed scheme can substantially increase the probability of spectrum hole detection as compared to traditional CS recovery approaches while using a very low sampling rate analog to information converter, and without requiring the knowledge of any statistical information about the environmental noise.

In this paper, we study the problem of cooperative communications in cognitive radio systems where the secondary user has limited relaying room for the overheard primary packets. More specifically, we characterize the stable throughput region of a cognitive radio network with a finite relaying buffer at the secondary user. Towards this objective, we formulate a constrained optimization problem for maximizing the secondary user throughput while guaranteeing the stability of the primary user queue. We consider a general cooperation policy where the packet admission and queue selection probabilities, at the secondary user, are both dependent on the state (length) of the finite relaying buffer. Despite the sheer complexity of the optimization problem, attributed to its non-convexity, we transform it to a linear program. Our numerical results reveal a number of valuable insights, e.g., it is always mutually beneficial to cooperate in delivering the primary packets in terms of expanding the stable throughput region. In addition, the stable throughput region of the system, compared to the case of infinite relaying queue capacity, marginally shrinks for limited relaying queue capacity.

Collecting data continuously in Wireless Sensor Networks (WSNs) with limited power and bandwidth is still a challenging issue. Recently, the sparse nature of these data motivated the use of Compressive Sensing (CS) as an efficient data gathering technique. In this paper, several algorithms are proposed to effectively exploit the temporal correlation and the sparsity inherent in sensor network data over time. These algorithms combine recent advances in compressive sensing (CS) theory, data compression, and data gathering algorithms. Experimental analysis through simulation evinces that the proposed algorithms significantly reduce the power consumption by reducing the number of sent measurements for the same Normalized Mean Square Error (NMSE).

Compressive Sensing (CS) has recently opened the door for efficient algorithms to solve various data gathering problems. Among these problems is sparse events detection in wireless sensor networks. In this problem, it is desirable to reduce the sensing cost by minimizing the number of sensors and the amount of data sent by each sensor. In this paper, we model the problem of sparse event detection as a compressive support recovery problem. We exploit the sparse and the binary nature of the event signal in the reconstruction algorithm using sequential compressive sensing. This provides an efficient solution to the problem, even under the assumptions of wide sensing area and high levels of noise. Simulation results show an improved performance under different compression ratios as compared to previous CS based approaches. It also shows the robustness of the proposed approach at low SNRs.

In this paper, we examine a cognitive spectrum access scheme in which a secondary user exploits the primary feedback information. We consider an overlay model in which the secondary user accesses the channel by certain access probabilities that are function of the spectrum sensing metric. In setting our problem, we assume that the secondary user can receive the primary link's feedback automatic repeat request (ARQ), but through an erasure channel. This means that the primary feedback may either be received correctly or is erased with a certain erasure probability. We study the cognitive radio network from a queuing theory point of view. Access probabilities are determined by solving a secondary throughput maximization problem subject to a constraint on the primary queues' stability. Fortunately, our problem is convex and can be solved using standard optimization techniques. Our scheme yields improved results in the secondary throughput than the non-feedback based access scheme attributed to the efficient utilization of the primary user's unerased feedback messages.

In this paper, we study the problem of cooperative spectrum sharing among a primary user (PU) and multiple secondary users (SUs) under quality of service (QoS) constraints. The SUs network is controlled by the PU through a relay which gets a revenue for amplifying and forwarding the SUs' signals to their respective destinations. The relay charges each SU a different price depending on its received signal-to-interference-and-noise ratio (SINR). The primary relay controls the SUs network and maximize any desired PU utility function. The PU utility function represents its QoS, which is affected by the SUs access, and its gained revenue to allow the access of the SUs. The problem of maximizing the primary utility is formulated as a Stackelberg game and solved through three different approaches, namely, the optimal, the heuristic and the suboptimal algorithms.

In this paper, we consider a novel layered coding approach with two layers. One of the two layers, denoted by the base-layer, can be received by any receiver even if it does not have reliable channel estimates. The other, refining-layer can only be received by any receiver that has channel state information. We propose signal constellations that allow the transmission of coherent and non-coherent information for the single-antenna transmitters. We derive upper bounds for the pairwise error probability for the coherent and non-coherent receivers and prove that our proposed signal constellations can achieve a diversity of order M for the 1 × M system, for both the coherent and non-coherent receivers.

In this paper, we consider a sequential binary hypothesis testing framework in wireless sensor networks. We study the effect of sensor censoring on network performance in terms of the average error probability and average number of observations required until a global decision is made. The detection process is mathematically modeled as a random walk process with two absorbing barriers. We resort to Chernoff bound in order to find upper bounds on the error probabilities and the average stopping time. The main contribution of this paper is to prove that in a sequential binary hypothesis network where sensors send their hard decisions to the fusion center, censoring can enhance the network performance in comparison to non-censoring networks in certain SNR regimes. Numerical evaluation is provided to illustrate the gains achieved through censoring.

In this paper, we investigate the problem of minimizing the frame completion delay for Instantly Decodable Network Coding (IDNC) in relay-assisted wireless multicast networks. We first propose a packet recovery algorithm in the single relay topology which employs generalized IDNC instead of strict IDNC previously proposed in the literature for the same relay-assisted topology. This use of generalized IDNC is supported by showing that it is a super-set of the strict IDNC scheme, and thus can generate coding combinations that are at least as efficient as strict IDNC in reducing the average completion delay. We then extend our study to the multiple relay topology and propose a joint generalized IDNC and relay selection algorithm. This proposed algorithm benefits from the reception diversity of the multiple relays to further reduce the average completion delay in the network. Simulation results show that our proposed solutions achieve much better performance compared to previous solutions in the literature.

In this paper, we investigate a spectrum-sensing algorithm for detecting spatial dimension holes in Multiple-Input Multiple-Output (MIMO) transmissions for OFDM systems using Compressive Sensing (CS) tools. This extends the energy detector to allow for detecting transmission opportunities even if the band is already energy filled. We show that the task described above is not performed efficiently by regular MIMO decoders (such as MMSE decoder) due to possible sparsity in the transmit signal. Since CS reconstruction tools take into account the sparsity order of the signal, they are more efficient in detecting the activity of the users. Building on successful activity detection by the CS detector, we show that the use of a CS-aided MMSE decoder yields better performance rather than using either CS-based or MMSE decoders separately.

Spectrum sensing in wideband cognitive radio networks is challenged by several factors such as hidden primary users (PUs), overhead on network resources, and the requirement of high sampling rate. Compressive sensing has been proven effective to elevate some of these problems through efficient sampling and exploiting the underlying sparse structure of the measured frequency spectrum. In this paper, we propose an approach for collaborative compressive spectrum sensing. The proposed approach achieves improved sensing performance through utilizing Kronecker sparsifying bases to exploit the two dimensional sparse structure in the measured spectrum at different, spatially separated cognitive radios. Experimental analysis through simulation shows that the proposed scheme can substantially reduce the mean square error (MSE) of the recovered power spectrum density over conventional schemes while maintaining the use of a low-rate ADC. We also show that we can achieve dramatically lower MSE under low compression ratios using a dense measurement matrix but using Nyquist rate ADC.

In this paper, we examine a cognitive spectrum access scheme in which secondary users exploit the primary feedback information. We consider an overlay secondary network employing a random access scheme in which secondary users access the channel by certain access probabilities that are functions of the spectrum sensing metric. In setting our problem, we assume that secondary users can eavesdrop on the primary link's feedback. We study the cognitive radio network from a queuing theory point of view. Access probabilities are determined by solving a secondary throughput maximization problem subject to a constraint on the primary queues' stability. First, we formulate our problem which is found to be non-convex. Yet, we solve it efficiently by exploiting the structure of the secondary throughput equation. Our scheme yields improved results in, both, the secondary user throughput and the primary user packet delay as compared to the scheme where no feedback information is exploited. In addition, it comes very close to the optimal genie-aided scheme in which secondary users act upon the presumed perfect knowledge of the primary users' activity.

In this work, we analyze the diversity gain region (DGR) of the single-antenna Rayleigh fading Z-Interference channel (ZIC). More specifically, we characterize the achievable DGR of the fixed-power split Han-Kobayashi (HK) approach under these assumptions. Our characterization comes in a closed form and demonstrates that the HK scheme with only a common message is a singular case, which achieves the best DGR among all HK schemes for certain multiplexing gains. Finally, we show that generalized time sharing, with variable rate and power assignments for the common and private messages, does not improve the achievable DGR.

We characterize the achievable three-dimensional tradeoff between diversity, multiplexing, and delay of the single antenna Automatic Retransmission reQuest (ARQ) Z-interference channel. Non-cooperative and cooperative ARQ protocols are adopted under these assumptions. Considering no cooperation exists, we study the achievable tradeoff of the fixed-power split Han-Kobayashi (HK) approach. Interestingly, we demonstrate that if the second user transmits the common part only of its message in the event of its successful decoding and a decoding failure at the first user, communication is improved over that achieved by keeping or stopping the transmission of both the common and private messages. Under cooperation, two special cases of the HK are considered for static and dynamic decoders. The difference between the two decoders lies in the ability of the latter to dynamically choose which HK special-case decoding to apply. Cooperation is shown to dramatically increase the achievable first user diversity.

In this paper, we consider binary hypothesis testing for distributed detection in Wireless Sensor Networks. Sensor nodes individually take a decision upon which hypothesis is currently present. Communication between sensor nodes and the Fusion Center is done through a Type-Based Multiple Access (TBMA) scheme, and the Fusion Center gives a global decision about the hypothesis under consideration. We consider the case where each sensor has the ability to "censor" transmission, meaning that a sensor node can locally withhold transmission if local observation is unreliable. The major contribution in this paper is to show that for the TBMA scheme with sensors sending binary decisions to the Fusion Center, censoring can achieve lower probability of decision error even if sufficient energy and/or rate of transmission is available.

In this paper, we consider a cognitive relaying network in which the secondary user accesses the channel with a certain access probability that depends on the feedback information sent by the primary destination. In addition, the secondary user is granted relaying capabilities by which it can relay primary traffic that was unsuccessfully transmitted by the primary user. We show that this proposed scheme enhances the performance of the secondary user as well as the primary user, while the QoS requirements of the primary user is unviolated. The secondary user can avoid sure collisions with the primary transmissions exploiting the feedback information from the primary user. Also, due to the fact that relaying the unsuccessfully transmitted primary traffic increases the availability of the channel for its own packets, the secondary throughput is increased and the primary delay is decreased.

We consider a random access primary network. At the beginning of each time slot, a number of secondary users sense the channel and make an access decision based on the spectrum sensing outcome and the channel state information (CSI). Specifically, the channel is accessed by a secondary transmitter with a probability that depends on both the sensing metric and the gain or signal-to-noise-ratio (SNR) of the channel between the transmitter and its respective receiver. Spectrum sensing operates in a “soft” mode where the sensing metric is used directly rather than making a binary decision concerning primary activity. We consider backlogged secondary users and primary users with infinite queues. The secondary access probabilities are obtained via solving an optimization problem designed to maximize the secondary throughput given a constraint on primary queue stability. The problem is shown to be convex and, hence, the global optimum can be obtained efficiently. Numerical results reveal a significant performance improvement in the secondary throughout with stable primary queues over the use of spectrum sensing with conventional detection or the implementation of sensing alone without making use of the CSI information.

We consider the effects of spectrum sensing errors on the performance of cognitive radio networks from a queueing theory point of view. In order to alleviate the negative effects of those errors, a novel design of spectrum access mechanism is proposed. This design is based on the observation that, in a binary hypothesis testing problem, the value of the test statistic can be used as a confidence measure for the test outcome. This value is hence used to specify a channel access probability for the secondary network. The access probabilities as a function of the sensing metric are obtained via solving an optimization problem designed to maximize the secondary service rate given a constraint on primary queue stability. The problem is shown to be convex and, hence, the global optimum can be obtained efficiently. Numerical results reveal a significant performance improvement in the maximum stable throughput of both primary and secondary networks over the conventional technique of making a hard binary decision and then transmitting with a certain probability if the primary is sensed to be inactive.

A key challenge in the design of real-time wireless multimedia systems is the presence of fading coupled with strict delay constraints. A very effective answer to this problem is the use of diversity achieving techniques to overcome the fading nature of the wireless channels caused by the mobility of the nodes. The mobility of the nodes gives rise to the need of cooperation among the nodes to enhance the system performance. This paper focuses on comparing systems that exhibit diversity of three forms: source coding diversity, channel coding diversity, and user cooperation diversity implemented through multihop or relay channels with amplify-and-forward or decode-and-forward protocols. Commonly used in multimedia communications, performance is measured in terms of the distortion exponent, which measures the rate of decay of the end-to-end distortion at asymptotically high signal-to-noise ratio (SNR). For the case of repetition coding at the relay nodes, we prove that having more relays is not always beneficial. For the general case of having a large number of relays that can help the source using repetition coding, the optimum number of relay nodes that maximizes the distortion exponent is determined in this paper. This optimum number of relay nodes will depend on the system bandwidth as well as the channel quality. The derived result shows a trade-off between the quality (resolution) of the source encoder and the amount of cooperation (number of relay nodes). Also, the performances of the channel coding diversity-based scheme and the source coding diversity-based scheme are compared. The results show that for both relay and multihop channels, channel coding diversity provides the best performance, followed by the source coding diversity.

In this paper, a binary hypothesis distributed detection problem in correlated wireless sensor networks with cooperative relays deployment is considered. In particular, the effect of correlation between sensor nodes is modeled and analyzed in Rayleigh flat fading channels in order to explore the natural tradeoffs between the number of sensor/relay nodes and the detection error performance in the network. Specifically, two communication protocols are utilized; in protocol I, each sensor node communicates its observation directly to the fusion center while in protocol II, amplify-and-forward (AF) cooperative relays are deployed and a fewer number of sensors is used. Based on the theoretical analysis and simulations, it is revealed that employing less sensor nodes and instead deploying relay nodes results in significant performance gains under strict network power constraint It is concluded that with cooperative distributed detection and exploitation of spatial diversity, better detection error performance is achieved as well as reduction in the required number of sensor nodes.

In this paper we address the network maintenance problem, in which we aim to maximize the lifetime of a sensor network by adding a set of relays to it. The network lifetime is defined as the time until the network becomes disconnected. The Fiedler value, which is the algebraic connectivity of a graph, is used as an indicator of the network health. The network maintenance problem is formulated as a semi-definite programming (SDP) optimization problem that can be solved efficiently in polynomial time. First, we propose a network maintenance algorithm that obtains the SDP-based locations for a given set of relays. Second we propose a routing algorithm, namely, Weighted Minimum Power Routing (WMPR) algorithm, that significantly increases the network lifetime due to the efficient utilization of the deployed relays. Third, we propose an adaptive network maintenance algorithm that relocates the deployed relays based on the network health indicator. Further, we study the effect of two different transmission scenarios, with and without interference, on the network maintenance algorithm. Finally, we consider the network repair problem, in which we find the minimum number of relays along with their SDPbased locations to reconnect a disconnected network. We propose an iterative network repair algorithm that utilizes the network maintenance algorithm.

In this paper, the problem of deploying relay nodes in wireless sensor networks will be considered. A system consisting of a set of sensor nodes communicating to a fusion center, where decisions are made, is considered. Based on our system model assumptions, some sensor nodes will provide "less- informative" measurements to the fusion center about the state of nature; we consider relay nodes deployment in the sensor network instead of the less-informative sensor nodes to forward the measurements of the other, "more-informative" sensor nodes. This introduces a new tradeoff in the system design between the number of measurements sent to the fusion center and the reliability of the more-informative measurements, which is enhanced by deploying more relay nodes in the network. We will analyze the performance of two protocols over Rayleigh flat-fading channels. In Protocol I, each sensor node directly transmits its measurement to the fusion center. In Protocol II, relay nodes will be used instead of the less-informative sensor nodes to forward the measurements of the more-informative sensor nodes. Hence, in Protocol II, the reliability of the more-informative measurements is enhanced at the expense of having fewer measurements sent to the fusion center and this creates the tradeoff between the number of measurements available at the fusion center and the reliability of the measurements.

In this paper, we address the network maintenance problem, in which we aim to maximize the lifetime of a sensor network by adding a set of relays to it. The network lifetime is defined as the time until the network becomes disconnected. The Fiedler value, which is the algebraic connectivity of a graph, is used as an indicator of the network health. The network maintenance problem is formulated as a standard semi- definite programming (SDP) optimization problem that can be solved efficiently in polynomial time. First, we present a network maintenance algorithm that obtains the near-optimum locations for a given set of relays. Second we propose a routing algorithm, namely, Weighted Minimum Power Routing (WMPR) algorithm, that significantly increases the network lifetime due to the efficient utilization of the deployed relays. Third, we propose an adaptive network maintenance algorithm that relocates the deployed relays based on the network health indicator. Finally, we consider the network repair problem, in which we find the minimum number of relays along with their near-optimum locations to reconnect a disconnected network. We propose an iterative network repair algorithm that utilizes the network maintenance algorithm.

Diversity techniques are an effective answer to the challenges presented by fading channels. This paper focuses on studying the performance of systems with diversity of three forms: source coding diversity, channel coding diversity, and user-cooperation diversity. To best reflect a focus on real-time multimedia communications, performance is measured through the distortion exponent, which measures the rate of decay of the end-to-end distortion at high SNRs. User-cooperation diversity takes the form of a relay channel implemented with amplify-and-forward processing at the relay. The results show that channel coding diversity provides the best performance, followed by source coding diversity. The results also show a tradeoff between the quality (resolution) of the source encoder and the amount of cooperation (number of relay nodes).

In this paper, the design of distributed space- frequency codes (DSFCs) for wireless relay networks employing the amplify-and-forward (AAF) protocol is considered. The term distributed comes from the fact that the space-frequency code is distributed among randomly located relay nodes. DSFCs are designed to achieve the multi-path (frequency) and cooperative diversities of the wireless relay channels. We derive sufficient conditions for the proposed code design to achieve full diversity based on minimizing the pairwise error probability (PEP). We prove that the proposed DSFC can achieve full diversity of order LN, where L is the number of paths of the channel and N is the number of relay nodes, for any N and for the cases of L = 1 (flat, frequency-nonselective fading channel) and L = 2 (two-ray fading channel).

Designing diversity achieving schemes over the wireless broadband fading relay channels is crucial to achieve higher diversity gains. These gains are achieved by exploiting the multipath (frequency) and cooperative diversities to combat the fading nature of wireless channels. The challenge is how to design space frequency codes, distributed among randomly located nodes that can exploit the frequency diversity of the wireless broadband channels. In this paper, the design of distributed space-frequency codes (DSFCs) for wireless relay networks is considered. The proposed DSFCs are designed to achieve the frequency and cooperative diversities of the wireless relay channels. The use of DSFCs with the decode-and-forward (DAF) and amplify-and forward (AAF) protocols is considered. The code design criteria to achieve full diversity, based on the pairwise error probability (PEP) analysis, are derived. For DSFC with the DAF protocol, a two-stage coding scheme, with source node coding and relay nodes coding, is proposed. We derive sufficient conditions for the proposed code structures at the source and relay nodes to achieve full diversity of order NL, where N is the number of relay nodes and L is the number of paths per channel. For the case of DSFC with the AAF protocol, a structure for distributed space-frequency coding is proposed.

Enhancing the connectivity of wireless sensor networks is necessary to avoid the occurrence of coverage gaps. In this paper, we aim at improving the network connectivity of a given network by adding a set of relays to it. We characterize the network connectivity by the Fiedler value, which is the second smallest eigenvalue of the Laplacian matrix representing the network graph. We propose a network-maintenance algorithm, which finds the best locations for a given set of relays. The proposed algorithm obtains the best relays' locations through a multi-level approach. In each level, the search problem can be formulated as a standard semi-definite programming (SDP) optimization problem. We show that the proposed algorithm can increase the average Fiedler value by 35% by adding one relay only.

In this paper, the design of distributed space- frequency codes (DSFCs) implementing the decode-and-forward (DAF) protocol for wireless relay channels is considered. The proposed DSFCs are designed to achieve the frequency and cooperative diversities of the wireless relay channels. A two-hop system model, where there is no direct link from the source node to the destination node, is considered. We propose to use two stages of coding, namely, source node coding and relay nodes coding. The proposed DSFCs are proved to achieve full diversity of order NL where N is the number of relay nodes and L is the number of paths per channel.

In this paper, we consider the design of synchronization-aware distributed space-time codes, which we denote as diagonal distributed space-time codes (DDSTC), for N relay nodes helping the source. We impose the diagonal structure of the distributed space-time code to simplify synchronization among the different relay nodes because it is very difficult to synchronize simultaneous transmissions of randomly located relay nodes. We derive an upper bound on the outage probability of the system, which shows that a diversity of order N can be achieved using the diagonal structure of the code. Then, we derive the code design criterion for the DDSTC based on minimizing the pairwise error probability (PEP) to achieve full diversity.

The performance limits of multimedia systems combining source (multiple description) coding and channel coding with user cooperation diversity over multi-hop channels is studied. Performance is measured through the distortion exponent, which measures the rate of decay of the end-to-end distortion at asymptotic high SNRs. Two implementations for user cooperation are considered: amplify-and-forward and decode-and-forward. Results comparing different source and channel coding schemes show that optimum channel coding diversity provides the best performance, followed by source coding diversity. The results also show that at low bandwidth expansion factor, source encoding distortion is the main limiting factor. As the bandwidth expansion factor increases, user cooperation diversity is the main limiting factor, thus, the distortion exponent could be improved by increasing the number of relays.

In this letter, a novel approach for outage probability analysis of the multinode amplify-and-forward relay network is provided. It is shown that the harmonic mean of two exponential random variables can be approximated, at high signal-to-noise ratio (SNR), to be an exponential random variable. The single relay case considered before is a special case of our analysis. Based on that approximation, an outage probability bound is derived which proves to be tight at high SNR. Based on the derived outage probability bound, optimal power allocation is studied. Simulation results show a performance improvement, in terms of symbol error rate, of the optimal power allocation compared to the equal power-allocation scheme

In this paper, we consider the design of distributed space-time codes for wireless networks. Distributed space-time coding (DSTC) can be achieved through node cooperation to emulate multiple transmit antennas. We derive the distributed space-time codes design criteria for different scenarios based on the pairwise error probability (PEP) analysis. First, we consider the decode-and-forward (DAF) protocol and prove that space-time codes, designed to achieve full diversity and maximum coding gain in the MIMO channels, will achieve full diversity but not necessarily maximizing the coding gain if used with the DAF protocol. Next, we consider the amplify-and-forward (AAF) protocol and prove that a space-time code designed to achieve full diversity and maximum coding gain in MIMO channels will achieve the same if used with the AAF protocol.

In this paper, we consider the outage probability analysis of multi-node amplify-and-forward relay network with N relay nodes helping the source. We consider a system in which each relay node amplifies the source signal only. We obtain an approximation for the outage probability which is tight at high signal-to-noise ratio (SNR). This tight outage approximation shows that the system can achieve a maximum diversity of order N+1. For the case of N = 1, our approach gives the same result obtained previously by Laneman et al. for the single relay scenario.