Short channel coherence time and oscillator phase noise are two major impairments in millimeter-wave (mmWave) communication systems. Several studies indicate that a substantial fraction of the available bandwidth may be required as overhead to compensate for these impairments, potentially exceeding one third of the total capacity. In this paper, we study Direction-Shift Keying (DSK), a variant of Spatial Modulation (SM), which encodes information in the Direction-of-Arrival (DoA) rather than in the signal amplitude or phase. DSK is implemented over a Distributed Antenna System (DAS), enabling angular resolvability of the transmitted signals. We first derive the structure of the optimal detector for a mobile device equipped withMantennas. We then introduce and characterize the Direction Coherence Time (DCT), defined as the temporal interval over which the DoA remains approximately invariant. Our analysis shows that DCT scales withd/v(transmitter-receiver distance over velocity), whereas the conventional Channel Coherence Time (CCT) scales with λ/v, revealing a coherence-time gain proportional tod/λ, which can exceed several orders of magnitude in mmWave systems. Furthermore, we show that the proposed detector inherently cancels receiver phase noise, eliminating the need for explicit phase-noise tracking. Simulation results validate the analytical findings and demonstrate the robustness of DSK in mobile mmWave environments in the presence of phase noise.

Future wireless networks are anticipated to evolve by aerial communication platforms. Nonetheless, the operational lifespan and efficacy of transceivers such as unmanned aerial vehicle (UAVs) and Internet of Things (IoT) devices are strictly prohibited by their constrained onboard power sources. This paper focuses on an aerial network configuration where a UAV harvests solar power to serve energy-limited IoT devices through simultaneous wireless information and power transfer. In this setup, the UAV and the IoT devices, each are equipped with energy and data buffers. This system also benefits from rate splitting multiple access for efficient interference management. Upon optimizing the system efficacy, we formulate a long-term resource allocation problem to maximize the time-averaged energy efficiency. To address this stochastic and non-convex optimization problem, we propose a multi-stage solution strategy. Firstly, by leveraging Lyapunov optimization theory, we transform the long-term stochastic problem into an equivalent deterministic short-term form. Next, by recasting this equivalent problem into Markov decision process, we propose a resource allocation mechanism based on actor-critic hindsight experience replay (AC-HER), tailored to capture the problem dynamics and optimize its variables. Moreover, given the UAV high mobility and the system reconfigurations, we fortify the trained AC-HER model with meta-learning strategy, enhancing its adaptability to system variations. Simulations verified that the proposed resource allocation strategy considerably outperforms its counterparts.

Integrated sensing and communication (ISAC) has recently emerged as a viable technique for establishing sensing and communication using the same resources. Nonetheless, the operation of ISAC networks is often challenged by the absence of a direct link between the sensing node and the targets, and by the risk of disclosing confidential data to malicious targets when using the same signal for both tasks. In this paper, a robust reconfigurable intelligent surface (RIS)-aided scheme for securing a full-duplex (FD) ISAC network is proposed. The considered network consists of uplink and downlink users served in FD through a multi-antenna dual-functional radar communication base station (BS), which employs co-located multi-antenna communication-radar arrays to detect multiple malicious targets while preserving communication secrecy in their presence. Additionally, the BS utilizes an optimized artificial noise (AN) that serves to disrupt the malicious targets’ reception and increase the sensing illumination power. By optimally designing the RIS phase shifts, transmit beamforming matrix, AN covariance matrix, and uplink users’ transmit power and combining vectors using an alternating optimization-based algorithm, the network’s sensing performance is maximized under secrecy and total power constraints. Numerical results present the proposed scheme’s efficacy, particularly when a direct link between the BS and the various nodes/targets is absent.

In this paper, we present a novel mixed-signal flash-based Finite Impulse Response (FFIR) filter architecture for IoT applications. The FFIR filter is scalable in that it can implement any filter with up to a provisioned maximum number of taps. Our FFIR filter utilizes flash transistors, a type of non-volatile memory (NVM) device, to perform analog computations in the current domain, achieving low power, energy, and area requirements. This design is well-suited for the Internet of Things (IoT) applications and other scenarios where resources are highly constrained. Our FFIR filter consists of several Flash-based Coefficient Multipliers (FCMs). The FIR coefficients of each FCM are stored in its constituent flash transistors, with the threshold voltage (Vt) of the flash transistors serving as a proxy for the filter coefficients. Furthermore, the impact of process or voltage variations is mitigated by precisely tuning the Vt of the flash transistors. The tuning of the Vt of the flash transistors can be performed either in the factory by the manufacturer (to negate process variations), or by the user in the field (to negate voltage variations or aging effects). We evaluate the tolerance of FFIR filters to manufacturing variations through Monte Carlo analysis, demonstrating robustness to process and VDD variations. Our FFIR design achieves a significant improvement over previous approaches. Compared to Digital FIR (DFIR) filters operating at the fastest frequency, we reduce the average of power, energy, and area by 4.05×, 1.95×, and 6.06×, while achieving an average peak signal-to-noise ratio (PSNR) of 38.04 dB and an average effective number of bits (ENOB) of 8.87 bits. In addition, we compare our FFIR filter with state-of-the-art Analog FIR (AFIR) filters as well. Our designs demonstrate significantly improved performance of at least 1.3×, 5.3×, and 18.5× in terms of energy per tap, area, and latency, respectively, when compared with the best among 4 recently published AFIR works.

The emergence of movable antenna (MA) technology has marked a significant advancement in the field of wireless communication research, paving the way for enhanced connectivity, improved signal quality, and adaptability across diverse environments. By allowing antennas to adjust positions dynamically within a finite area at transceivers, this technology enables more favourable channel conditions, optimizing performance across applications like mobile telecommunications and remote sensing. However, throughout history, the introduction of every new technology has presented opportunities for misuse by malicious individuals. Just as MAs can enhance connectivity, they may also be exploited for disruptive purposes such as jamming. In this paper, we examine the impact of an MA-enhanced jamming system equipped with$M$movable antennas in a downlink multi-user communication scenario, where a base station (BS) with$N$antennas transmits data to$K$single-antenna users. We formulate an optimization problem where the jammer determines both the antenna locations and beamforming vectors to minimize the total system sum rate. Given the non-convex nature of the problem, it is decomposed into two sub-problems, which are solved alternately until convergence. Simulation results show that an adversary equipped with MAs reduce the system sum rate by 30 % more effectively than fixed-position antennas (FPAs). Additionally, MAs increase the outage probability by 25 % over FPAs, leading to a 20 % increase in the number of users experiencing outages. The highlighted risks posed by unauthorized use of this technology, underscore the urgent need for effective regulations and countermeasures to ensure its secure application.

Model Predictive Control (MPC) has become a widely adopted control technique for Multilevel Inverters due to its ability to manage multi-objective optimization problems under system constraints. However, a key challenge in MPC implementation lies in selecting appropriate weighting factors for the cost function, as fixed values often lead to suboptimal performance under dynamic operating conditions. Thus, this paper presents a lightweight auto-tuning method for the voltage weighting factor in Finite Control Set MPC (FCS-MPC), applied to a single-phase grid-connected 9-level Crossover Switches Cell inverter. The proposed approach employs low computational complexity machine learning models, Linear Regression and Support Vector Machine, trained offline on a minimal dataset comprising the DC link voltage and reference current. These models are embedded into the control loop to enable real-time adjustment of the voltage weighting factor. The presented comparative simulation results confirm the effectiveness of the proposed technique across a wide range of operating conditions. Compared to more complex AI-based solutions, this work contributes a simple yet effective ML-based tuning strategy that improves control performance with minimal computational overhead.

The increasing deployment of renewable energy systems, electric vehicles, and high-voltage direct current transmission infrastructures has intensified interest in Modular Multilevel Converters (MMCs), which offer superior scalability, waveform quality, and fault tolerance. However, MMC control remains challenging due to its nonlinear dynamics and multi-objective requirements, including output current tracking, capacitor-voltage balancing, and circulating current suppression. This paper presents a model-free control strategy based on Deep Reinforcement Learning (DRL), employing the Proximal Policy Optimization algorithm to achieve these control objectives in a 3-level single-phase MMC. The proposed DRL-based controller learns an optimal switching policy directly from interaction data, eliminating the need for an accurate system model or manual tuning. Simulation results from MATLAB/Simulink confirm that the trained agent achieves low total harmonic distortion, maintains capacitor voltages around the desired values, and minimizes the circulating current, while demonstrating robustness under load variations and dynamic transients. These findings highlight the effectiveness of the proposed DRL approach as a scalable and adaptive solution for complex multilevel inverter control problems.

Transient Stability (TS) remains a critical concern in ensuring secure operation of modern power systems, particularly with the growing complexity introduced by renewable energy integration, power electronic devices, and hybrid AC/DC infrastructures. Traditional methods for transient stability assessment (TSA) and transient stability-constrained optimal power flow (TSC-OPF), such as time-domain simulations and energy function-based techniques, face limitations in scalability, computational efficiency, and real-time applicability. This paper presents a holistic review of Machine Learning (ML) approaches adopted for TSA and TSC-OPF, covering a range of models from traditional learners (e.g., support vector machines, decision trees) to DL (e.g., convolutional neural networks, long short-term memory networks, graph neural networks) and Reinforcement Learning (RL) techniques. For each domain, methodologies will be categorized, highlighting key advancements, and discussing trade-offs in performance. Furthermore, existing challenges are identified and future research directions are proposed, emphasizing on hybrid modeling, uncertainty handling, real-time assessment and RL challenges. This review aims to serve as a timely reference for researchers and practitioners working on data-driven solutions for power system TS.

Frequency multipliers are increasingly utilized for signal up-conversion in modern wireless communication systems, particularly in millimeter-wave (mmWave) and sub-terahertz (sub-THz) bands, owning to their simplicity and ease of integration. However, their inherent nonlinearity causes distortions, fundamentally altering the temporal and spectral characteristics of transmitted signals. This distortion transforms well-defined baseband pulses (e.g., sinc, raised cosine) into complex, hardware-dependent waveforms, where the matched-filter depends both on the multiplication order and the specific hardware implementation. Notably, the spectral occupancy of the transmitted signal expands after frequency multiplication. Without accurate knowledge of the multiplier-induced distortions at the receiver, applying a mismatched filter can cause severe inter-symbol interference and loss of critical frequency components, signal-to-noise ratio degradation thereby degrading detection performance. In this paper, we propose a blind, adaptive matched-filter estimation approach leveraging a Long Short-Term Memory (LSTM) neural network. Our method directly estimates the matched filter from sampled segments of the noisy modulated received signal without requiring pilot symbols. The proposed model adapts to dynamic pulse shapes and amplitudes by implicitly learning the spectral transformations introduced by hardware-induced nonlinearities. Simulation results demonstrate high accuracy of the matched filter estimation, with a mean-square error precision of four decimal places.

Unmanned aerial vehicles (UAVs) with multiple antennas have recently been explored to improve capacity in wireless networks. However, their strict energy constraint for simultaneously flying and communication tasks renders the exploration of energy-efficient multi-antenna techniques indispensable. Meanwhile, lens antenna subarrays (LASs) emerge as a promising energy-efficient multi-antenna structure that have not been previously harnessed for this purpose. In this paper, we propose a LAS-aided UAV to serve ground users in downlink transmission. We formulate a resource allocation problem aimed at initiating a trade-off between aggregate data rate of ground users and the power consumption of the UAV (energy efficiency) by optimizing the lens-based beamforming and flight trajectory of the UAV. To address this non-convex problem, we recast it in Markov decision process that captures its dynamic features and provides a framework to train an actor-critic agent. This agent is fine-tuned via hindsight experience replay for enhanced stabilization. As well, given the frequent mobility of the UAV, we fortify the trained agent with a meta-learning strategy, enhancing its adaptability to system variations. Numerically, more than 20% energy efficiency gain is achieved by incorporating a 4lens LAS for UAV, compared to its single-lens architecture in literature. Simulations also demonstrate that the proposed resource allocation strategy achieves significant superiority over counterparts in literature.

Composed of an array of low-cost radiating elements, flexible intelligent metasurfaces (FIMs) can adaptively morph their surface shapes by adjusting the positions of elements along the direction perpendicular to the surface. This adaptive morphing, not only enhances wireless channel conditions, but also significantly curtails power consumption. This paper conducts an adaptive performance analysis of a wireless system, in which a FIM-equipped base station (BS) leverages the presence of a reconfigurable intelligent surface (RIS) to facilitate downlink transmission. To rigorously evaluate the system’s efficiency, we formulate an optimization problem centered on resource allocation, with the primary objective of maximizing the network achievable data rate. This maximization is subject to multiple constraints, especially on stringent quality-of-service (QoS) requirements of users and the BS finite power budget. Due to the highly intricate interdependencies among optimization variables and the inherent non-convexity of the problem, we strategically reformulate it as a Markov decision process (MDP), encapsulating its dynamic characteristics. To derive an optimal solution, we train a deep deterministic policy gradient (DDPG) agent, relying on MDP, which simultaneously optimizes the decision variables: the BS transmit beamforming, the morphology of the FIM and the reflection coefficient matrix of the RIS. Furthermore, to accommodate the real-world challenges imposed by user mobility, we enhance the generalization of the DDPG model through the integration of meta-learning technique, thereby significantly improving its adaptability to system variations. Numerical evaluations affirm that incorporating an RIS yields a pronounced improvement in achievable network data rate, particularly when the BS transmit power budget is maintained within a moderate operational range.

With the emergence of the Open Radio Access Network (ORAN) concept and related standardization efforts, future radio access networks are anticipated to feature elements from diverse vendors. Although the ORAN elements can authenticate as legitimate, the system may fail to meet service requirements if some network components do not adhere to their respective agreements, i.e., moral hazard. This issue raises concerns about the network's end-to-end performance, complicating fault attribution and conflict resolution. Therefore, there is a need for an automated zero-trust framework capable of continuously detecting instances of moral hazard. The complexity is exacerbated by the dynamic nature of network elements or artificial intelligence (AI) model performance, which may degrade over time intentionally (e.g., malicious tampering) or unintentionally (e.g., model obsolescence or device performance decline), limiting the effectiveness of offline testing. To address this, we develop a mechanism based on subjective logic principles, incorporating a logic-based argumentation framework that explicitly accommodates argument schemes, argument accrual, and burden of proof. Building upon this framework, we apply contract theory to incentivize compliant devices to participate truthfully in the ORAN ecosystem, thereby enhancing system performance. The simulation results show improved system efficiency and reduced operational costs.

Transient instability poses a critical challenge to the reliable operation of modern power systems, often leading to large-scale blackouts. Despite the success of data-driven Transient Stability Assessment (TSA), its practical implementation remains limited by challenges in processing high-speed real-time data streams and preserving data privacy. To address these limitations, this article develops a novel Federated Adaptive Random Forest (FedARF) method that integrates federated learning with the Adaptive Random Forest (ARF) model. The proposed decentralized framework incorporates concept drift adaptation mechanisms to accommodate the stochastic and dynamic characteristics of modern power systems. FedARF facilitates distributed knowledge aggregation learned from various heterogeneous local data sensors (clients) to predict and evaluate the TSA status with minimal communication overhead. Comprehensive experiments on the New England 39-Bus system, the IEEE 68-Bus system, and the large-scale ACTIVIgs 25k-Bus system demonstrate the efficiency of the proposed method with an overall accuracy of 99.65%. Compared to traditional centralized forecasting methods, and state-of-the-art models, the proposed approach not only maintains high prediction accuracy but also enhances data privacy preservation while substantially reducing communication bandwidth requirements.

This paper explores downlink Cooperative Rate-Splitting Multiple Access (C-RSMA) in a multi-cell wireless network with the assistance of Joint-Transmission Coordinated Multipoint (JT-CoMP). In this network, each cell consists of a base station (BS) equipped with multiple antennas, a cell-center user (CCU), and a cell-edge user (CEU) located at the edge of adjacent cells. Through JT-CoMP, all BSs collaborate to simultaneously transmit the data to all users including the CCUs and CEU. To enhance the signal quality for the CEU, CCUs relay the common stream to the CEU by operating in half-duplex (HD) relaying mode. We aim to jointly optimize the beamforming vectors at the BS, the allocation of common stream rates, the transmit power at relaying users, i.e., CCU s, and the time slot fraction aiming to maximize the minimum achievable data rate. The formulated problem is non-convex and challenging to solve directly. To address this, we employ change-of-variables, first-order Taylor approximations and a low-complexity algorithm based on Successive Convex Approximation (SCA). We demonstrate the efficacy of the proposed scheme, in terms of average achievable data rate, and we compare its performance to that of four baseline schemes, including HD cooperative non-orthogonal multiple access (C-NOMA), NOMA, and RSMA without user cooperation. The results show improvements of 12% and 41 % over RSMA and HD C-NOMA, respectively in high channel disparity between the BS and UEs.

This paper proposes a self adaptive physics informed neural network (SAPINN) model to predict the degree of polymerization (DP) of oil-impregnated paper insulation to quantify the level of degradation and the remaining useful lifetime. The prediction is performed based on historical DP values and the corresponding prediction time step, which are used as input data points to the proposed model. The DP mathematical model is used to constrain the training phase of the AI-model through a weighted sum loss function. The weights of this loss function are adjusted for each epoch through a self-adaptive weighting method to determine the relative importance of the data component and the mathematical model throughout the training by defining these weights as trainable parameters. The trained model is then tested using different datasets which are not part of the training phase. The training and testing datasets are generated synthetically through an algorithm that considers the deviation from the ideal DP degradation curve and incorporates actual measurement noise. The performance of the proposed SAPINN is compared to the baseline PINN and NN (in the absence of physics) to highlight the importance of embedding the mathematical model and the self adaptation algorithm, and theses experiments demonstrate that SAPINN significantly enhances the DP prediction.

In this paper, a Reinforcement Learning controller (RLC) is designed and implemented on a 5-level Packed U-Cell (PUC5) grid-connected inverter to control the injected current flowing into the electric network. The RL agent is trained using a Proportional-Integral (PI) reward function to optimize its control strategy. Moreover, the voltage balancing of the auxiliary capacitor in PUC5 is separated from the RL controller and integrated into the switching algorithm to reduce the training burden. This modification reduces the observation inputs required for RL training, significantly shorten the training time. Simulation studies conducted in Matlab/Simulink evaluate the performance of the proposed RL controller, demonstrating robust dynamic response and accurate tracking of reference signals across different operational conditions.

The integration of the large language model (LLM) technology in electric vehicles (EVs) has sparked a significant leap forward in the evolution of intelligent transportation. LLM technology enables real-time, context-aware communication, thereby elevating the safety and convenience of driving experiences. LLMs play a pivotal role in refining human-vehicle interactions, offering an intuitive and responsive interface for vehicle controls and navigation systems. In addition, LLMs contribute to the sustainable development of EV technology by optimizing energy consumption patterns and supporting the integration of EVs into smart grid systems. To this end, this paper aims to review the essential elements of LLM-based EVs to emphasize their current capabilities toward smart transportation and infrastructure services. This paper explores the multifaceted contributions of LLMs in enhancing functionality, user experience, and technological development of EVs. This tutorial also addresses the challenges and future prospects of LLM applications in EVs, emphasizing their potential to transform EVs into intelligent companions on the road and pave the way for a more sustainable and user-centered future for personal transportation.

The recent surge in distributed energy resources has made voltage fluctuations more complex and unpredictable. Consequently, traditional voltage control (VC) methods such as stochastic programming and robust optimization may struggle to manage rapid and significant fluctuations. Facing this challenge, this paper proposes an efficient dueling deep Q network (Dueling DQN)-based autonomous VC method. This study formulates the VC as a Markov decision process and develops an agent that learns optimal operational strategies to maintain voltage levels within safe limits, ensuring grid stability and reliability. The proposed agent operates within the power system environment, designed to mimic real-world grid conditions, including voltage variability and load fluctuations. The Dueling DQN model processes comprehensive observations, including production levels, loads, and voltage measurements, to predict action values that ensure effective VC. The Dueling DQN architecture, training process, and operational mechanisms based on VC are thoroughly detailed. Extensive case studies performed on the modified IEEE 14-bus system and a reduced IEEE 118-bus system and conducted over numerous episodes, demonstrate that the Dueling DQN agent consistently outperforms deep Q networks derivatives and deep deterministic policy gradient approach.

The efficient and secure operation of power grids is essential for ensuring reliable electricity supply and supporting the integration of renewable energy sources. Yet, the landscape is marred by burgeoning adversarial attacks, particularly targeting power systems employing cutting-edge deep reinforcement learning (DRL) methodologies. This study proposes a proximal policy optimization (PPO) agent against a randomized adversarial opponent aiming to disrupt grid operations. The performance of the PPO agent is assessed across various power grid environments alongside several baseline agents, including the do-nothing agent, the random agent, the topology greedy agent, and the power line switch agent with adversarial training. Over multiple epochs of adversarial training, the average rewards, number of steps to resolution, and computational time are recorded. The simulation results on the IEEE 14-bus system and the reduced IEEE 118-bus system demonstrate a nuanced supremacy and applicability of the PPO algorithm compared to heuristic and randomized approaches. The main contributions of this paper include 1) Introducing an optimized PPO algorithm assessed using two IEEE bus system environments; and 2) Applying an adversarial-training-based DRL to improve the robustness of PPO alorthim’s policies in the electrical grid environment.

The modern power system dominated by renewable energy resources such as Type-4 wind farms (WF) has recently seen significant increase in cases of sustained oscillations at the sub-synchronous frequency range that is typically referred to sub-synchronous oscillations (SSOs) in the existing literature. Although thorough studies have been conducted on prior types of WFs to understand the triggering factors for SSO, however, in the case of Type-4 WFs the triggering factors and causes still remain unclear. Therefore, this paper studies the SSO characteristics exhibited by Type-4 WFs operating within a weak grid. To emulate real-world grid conditions, an extensive AC network modeled after the IEEE 39-bus system is employed. Through simulations incorporating the integration of Type-4 WFs at various locations within the grid, the resulting effects on SSO triggering factors are analyzed. The investigation is structured around three distinct case studies, conducted at buses 31, 37, and 38. These studies involve varying network reactance to explore and establish the relationship between SSO events and the short-circuit ratio (SCR) of the network. The consistent findings across diverse case studies underscore the general relationship between SCR, grid strength, and SSOs. The analysis reaffirms the significant impact of increased reactance on SSO characteristics and confirms that weaker grids are more susceptible to SSOs.

In this paper, we tackle the issue of moral hazard within the realm of the vehicular Metaverse. A pivotal facilitator of the vehicular Metaverse is the effective orchestration of its market elements, primarily comprised of sensing internet of things (SIoT) devices. These SIoT devices play a critical role by furnishing the virtual service provider (VSP) with real-time sensing data, allowing for the faithful replication of the physical environment within the virtual realm. However, SIoT devices with intentional misbehavior can identify a loophole in the system post-payment and proceeds to deliver falsified content, which cause the whole vehicular Metaverse to collapse. To combat this significant problem, we propose an incentive mechanism centered around a reputation-based strategy. Specifically, the concept involves maintaining reputation scores for participants based on their interactions with the VSP. These scores are derived from feedback received by the VSP from Metaverse users regarding the content delivered by the VSP and are managed using a subjective logic model. Nevertheless, to prevent “good” SIoT devices with false positive ratings to leave the Metaverse market, we build a vanishing-like system of previous ratings so that the VSP can make informed decisions based on the most recent and accurate data available. Finally, we validate our proposed model through extensive simulations. Our primary results show that our mechanism can efficiently prevent malicious devices from starting their poisoning attacks. At the same time, trustworthy SIoT devices that had a previous miss-classification are not banned from the market.

This paper investigates the channel aging problem of mobile light-fidelity (LiFi) systems. In the LiFi physical layer, the majority of the optimization problems for mobile users are non-convex and require the use of dual decomposition or heuristics techniques. Such techniques are based on iterative algorithms, and often cause a high processing delay at the physical layer. Hence, the obtained solutions are rendered sub-optimal since the LiFi channels are evolving. In this paper, a proactive-optimization (PO) approach that can alleviate the LiFi channel aging problem is proposed. The core idea is to design a long-short-term-memory (LSTM) network that is capable of predicting posterior positions and orientations of mobile users, which can be then used to predict their channel coefficients. Consequently, the obtained channel coefficients can be exploited to derive near-optimal transmission-schemes prior to the intended service-time, which enables real-time service. Through various simulations, the performance of the designed LSTM model is evaluated in terms of prediction error and inference complexity, as well as its application in a practical LiFi optimization problem.

Photovoltaic (PV) power is progressively being subsumed into power grids. Consequently, reliable PV power forecasting (PVPF) has become essential to avoid ramp events that can adversely affect the operations of integrated power systems. This article presents a deep-learning-based algorithm for PVPF. The gated recurrent units (GRU) network was implemented to predict the non-linear spatiotemporal correlations of the weather data, leading to higher reliability of the PV stations. Experimental results obtained from actual testing demonstrate the validity of the GRU networks for accurate PVPF, contributing to the efficient operation and management of smart grids and renewable energy systems. The conducted case study shows that the proposed model outperforms bidirectional long short term memory (BiLSTM) and long short term memory (LSTM) models in terms of computation power, root-mean-square error, and mean absolute error metrics.

Condition monitoring of electrical Rotating Machines (RM) serves in structural changes detection during machine’s operation. However, the frequent fault occurrence reduces the RM remaining useful life and accelerates their deterioration. Therefore, this paper proposes an effective multi-fault classification system for the faults in electric rotating machines. The proposed method employs an Artificial Neural Network (ANN) and Synthetic Minority Over-sampling (SMOTE) technique for automatically detecting rotating machines failures. This model's efficacy stems from the use of the relief feature selection approach to identify the most affecting features and improve the model's performance. A case study analysis uses the Machinery Fault Dataset (MAFAULDA) to test the models' performance. Simulation results are obtained to demonstrate that the proposed paradigm provides outstanding performance based on a fair assessment using the MAFAULDA dataset and shows that the proposed model has a high potential to detect rotating machine state.

This paper investigates the integration of reconfigurable intelligent surface (RIS) with downlink non-orthogonal-multiple-access (NOMA) in a multi-user two-cell network assisted by the joint-transmission coordinated multipoint (JT-CoMP). Specifically, the RIS is deployed at the edge of two adjacent cells to assist the JT-CoMP from these two cells to multiple far NOMA users located at their edges. Under this setup, we jointly optimize the power allocation (PA) coefficients at the base stations (BSs), the user clustering (UC) policy, and the phase-shift (PS) matrix of the RIS with the objective of maximizing the network sum-rate subject to a target quality-of-service, defined in terms of the minimum required data rate at each cellular user, and the successive interference cancellation (SIC) constraints. The formulated problem ends to be a non-convex mixed-integer non-linear program that is difficult to be solved in a straightforward manner. To alleviate this issue, and with the aid of alternating optimization (AO), the original optimization problem is decomposed into two sub-problems, a joint PA and UC sub-problem and a PS sub-problem, that are solved in an alternating way. For the first sub-problem, we invoke the bi-level optimization approach to decouple the PA sub-problem from the UC sub-problem. For the PA sub-problem, closed-form expressions for the optimal PA coefficients are derived. On the other hand, the UC problem is projected to multiple 2-dimensional assignment problems, each of which is solved using the Hungarian method. Finally, the PS sub-problem is formulated as a difference-of-convex problem and an efficient solution is obtained using the successive convex approximation technique. The numerical results reveal that the network sum-rate of the proposed RIS-assisted CoMP NOMA networks outperforms the conventional CoMP NOMA scheme without the assistance of the RIS, the RIS-assisted CoMP orthogonal multiple access (OMA) scheme, and RIS-assisted NOMA scheme, especially for low transmit power from the BSs.

Ultra-Reliable and Low Latency Communications (URLLC) is one of the essential services in 5G networks and beyond. The coexistence of URLLC alongside other services, namely, enhanced Mobile BroadBand (eMBB) and massive Machine-Type Communications (mMTC), calls for developing spectrally efficient multiplexing techniques. In this work, we study the problem of scheduling URLLC traffic in a downlink system in the presence of eMBB traffic. Based on the proposed superposition/puncturing scheme, a resource allocation problem is formulated with the objective to minimize the rate loss of the eMBB service and URLLC packet segmentation loss while satisfying the eMBB and URLLC quality of service (QoS) constraints. The resulting problem is formulated as a mixed-integer non-linear program (MINLP) which is generally very hard to solve in polynomial time. Hence, we reformulate the problem as a one-to-one pairing problem and we derive its feasibility region as well as the optimal solutions for the power and spectral resource allocation. Subsequently, we propose a low complexity algorithm to support the many-to-many pairing. Simulation results show that the proposed algorithm achieves higher URLLC packet admission rate and lower rate loss for eMBB. For instance, the URLLC packet admission rate, unlike baseline methods, is shown to be preserved under the proposed method even at higher URLLC load. It is shown that at least 30% more URLLC users can be served without degrading their QoS, while keeping the impact on eMBB rate minimal. Detailed numerical evaluation is presented to quantify the benefits of the proposed method.

This paper studies the coexistence of enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communication (URLLC) services in a cellular network that is assisted by a reconfigurable intelligent surface (RIS). The system model consists of one base station (BS) and one RIS that is deployed to enhance the performance of both eMBB and URLLC in terms of the achievable data rate and reliability, respectively. We formulate two optimization problems, a time slot basis eMBB allocation problem and a mini-time slot basis URLLC allocation problem. The eMBB allocation problem aims at maximizing the eMBB sum rate by jointly optimizing the power allocation at the BS and the RIS phase-shift matrix while satisfying the eMBB rate constraint. On the other hand, the URLLC allocation problem is formulated as a multi-objective problem with the goal of maximizing the URLLC admitted packets and minimizing the eMBB rate loss. This is achieved by jointly optimizing the power and frequency allocations along with the RIS phase-shift matrix. In order to avoid the violation in the URLLC latency requirements, we propose a novel framework in which the RIS phase-shift matrix that enhances the URLLC reliability is proactively designed at the beginning of the time slot. For the sake of solving the URLLC allocation problem, two algorithms are proposed, namely, an optimization-based URLLC allocation algorithm and a heuristic algorithm. The simulation results show that the heuristic algorithm has a low time complexity, which makes it practical for real-time and efficient multiplexing between eMBB and URLLC traffic. In addition, using only 60 RIS elements, we observe that the proposed scheme achieves around 99.99% URLLC packets admission rate compared to 95.6% when there is no RIS, while also achieving up to 70% enhancement on the eMBB sum rate.

In this paper, we investigate the dynamic power allocation for a visible light communication (VLC) cellular system consisting of two coordinating attocells, each equipped with one access-point (AP). The coordinated multipoint (CoMP) between the two cells is introduced to assist users experiencing high inter-cell-interference (ICI). Specifically, the coordinated zero-forcing (ZF) precoding is used to cancel the ICI at the users located near the centers of the cells, whereas the joint transmission (JT) is employed to eliminate the ICI at the users located at the edge of both cells and to improve their receptions as well. Furthermore, two multiple access techniques are invoked within each cell, namely, non-orthogonal-multiple-access (NOMA) and cooperative non-orthogonal-multiple-access (C-NOMA). Hence, two multiple access techniques are proposed for the considered multi-user multi-cell system, namely, the CoMP-assisted NOMA scheme and the CoMP-assisted C-NOMA scheme. For each scheme, two power allocation frameworks are formulated each as an optimization problem, where the objective of the former is maximizing the network sum data rate while guaranteeing a certain quality-of-service (QoS) for each user, whereas the goal of the latter is to maximize the minimum data rate among all coexisting users. The formulated optimization problems are not convex, and hence, difficult to be solved directly unless using heuristic methods, which comes at the expense of high computational complexity. To overcome this issue, optimal and low complexity power allocation schemes are derived. In the simulation results, the performance of the proposed CoMP-assisted NOMA and CoMP-assisted C-NOMA schemes are compared with those of the CoMP-assisted orthogonal-multiple-access (OMA) scheme, the C-NOMA scheme and the NOMA scheme, where the superiority of the proposed schemes are demonstrated. Finally, the performance of the proposed schemes and the considered baselines is evaluated while varying various system parameters.

This article considers unmanned aerial vehicle (UAV) assisted Internet of Things (IoT) networks, where low resource IoT devices periodically sample a stochastic process and need to upload more recent information to a Base Station (BS). Among the myriad of applications, there is a need for timely delivery of data (for example, status-updates) before the data becomes outdated and loses its value. Since transmission capabilities of IoT devices are limited, it may not always be feasible to transmit over one hop transmission to the BS. To address this challenge, UAVs with virtual queues are deployed as middle layer between IoT devices and the BS to relay recent information over unreliable channels. In the absence of channel conditions, the optimal online scheduling policy is investigated as well as dynamic UAV altitude control that maintains a fresh status of information at the BS. The objective of this paper is to minimize the Expected Weighted Sum Age of Information (EWSA) for IoT devices. First, the problem is formulated as an optimization problem that is however generally hard to solve. Second, an online model free Deep Reinforcement Learning (DRL) is proposed, where the deployed UAV obtains instantaneous channel state information (CSI) in real time along with any adjustment to its deployment altitude. Third, we formulate the online problem as a Markov Decision Process (MDP) and Proximal Policy Optimization (PPO) algorithm, which is a highly stable state-of-the-art DRL algorithm, is leveraged to solve the formulated problem. Finally, extensive simulations are conducted to verify findings and comprehensive comparisons with other baseline approaches are provided to demonstrate the effectiveness of the proposed design.

This paper investigates the downlink transmission of reconfigurable intelligent surface (RIS)-aided cooperative non-orthogonal-multiple-access (C-NOMA), where both half-duplex (HD) and full-duplex (FD) relaying modes are considered. The system model consists of one base station (BS), two users and one RIS. The goal is to minimize the total transmit power at both the BS and at the user-cooperating relay for each relaying mode by jointly optimizing the power allocation coefficients at the BS, the transmit power coefficient at the relay user, and the passive beamforming at the RIS, subject to power budget constraints, the successive interference cancellation constraint and the minimum required quality-of-service at both cellular users. To address the high-coupled optimization variables, an efficient algorithm is proposed by invoking an alternating optimization approach that decomposes the original problem into a power allocation sub-problem and a passive beamforming sub-problem, which are solved alternately. For the power allocation sub-problem, the optimal closed-form expressions for the power allocation coefficients are derived. Meanwhile, with the aid of difference-of-convex rank-one representation and successive convex approximation, an efficient solution for the passive beamforming is obtained. The simulation results validate the accuracy of the derived power control closed-form expressions and demonstrate the gain in the total transmit power brought by integrating the RIS in C-NOMA networks.

Ultra-Reliable and Low Latency Communications (URLLC) is one of the essential services in 5G networks and beyond. The coexistence of URLLC alongside other service classes, namely, enhanced Mobile BroadBand (eMBB) and massive Machine-Type Communications (mMTC), calls for developing spectrally efficient multiplexing techniques. In this work, we study the problem of scheduling URLLC traffic in a downlink system with the presence of eMBB traffic class. Based on the superposition/puncturing scheme, a resource allocation problem is formulated with the objective to minimize the eMBB data rate loss while satisfying eMBB and URLLC quality of service (QoS) constraints. The resulting problem is formulated as a mixed integer non-linear programming (MINLP) which is generally NP hard and hence complex to solve. Hence, we derive its feasibility region as well as the optimal solutions for the power and spectral resource allocation. Subsequently, we propose a low complexity algorithm to serve URLLC traffic. Simulation results show that the proposed algorithm achieves higher reliability for URLLC and higher eMBB data rate compared to the puncturing schemes. The results also show that the eMBB QoS requirements, which are represented by the eMBB rate loss threshold, has a negative effect on the URLLC reliability for high URLLC load. Therefore, the eMBB rate and the eMBB loss threshold should be jointly optimized considering QoS of both eMBB and URLLC. Index Terms—eMBB, multiplexing, puncturing, superposition, URLLC, 6G.

Light-fidelity (LiFi) is a fully-networked bidirectional optical wireless communication (OWC) technology that is considered as a promising solution for high-speed indoor connectivity aimed for future sixth generation (6G) wireless networks. In the LiFi physical layer, the majority of the power allocation problems for mobile users investigated and reported in the literature are non-convex. These problems may be solved using dual decomposition techniques or heuristics that require iterative algorithms, and often, cannot be computed in real time due to the high computational load. In this paper, a proactive power allocation (PPA) approach that can alleviate the aforementioned issues is proposed. The core of the PPA approach is two cascaded neural networks consisting of one convolution neural network (CNN) and one long-short-term-memory (LSTM) network that are jointly capable of predicting posterior positions and orientations of mobile users following random trajectories in indoor environments. Afterwards, the predicted parameters are fed into the expression of the channel coefficients of the mobile users. Finally, the resulting predicted channel coefficients are exploited for deriving near-optimal power allocation schemes prior to the intended service time, which enables near-optimal and real-time service for mobile LiFi users.

This paper proposes a novel framework to emerge the reconfigurable intelligent surface (RIS) in cellular networks wherein enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communication (URLLC) services coexist. In order to avoid the violation in the URLLC latency requirements, the framework proposes RIS phase shift matrix that enhances the URLLC reliability is proactively designed at the beginning of the time slot. The system model consists of a single base station (BS) and a single RIS which deployed to enhance the channel environments of the eMBB and the URLLC users. To allocates the eMBB users, we formulate a time-slot basis eMBB allocation problem which has the goal of maximizing the eMBB sum-rate by jointly optimizing the power allocation at the BS and the RIS phase shift matrix while satisfying the eMBB rate constraint. Since the formulated problem is a non-convex problem which hard to be solved directly, we adopt the alternating optimization approach to decompose the eMBB allocation problem optimization problem into a power allocation and a RIS phase shift matrix sub-problems. Then, the URLLC allocation problem is formulated as a multi-objective problem with the goal of maximizing the URLLC admitted packets and minimizing the eMBB rate loss by jointly optimizing the power and frequency allocation. Then, we proposed a heuristic algorithm to allocate the URLLC load. The proposed algorithm has a low time complexity which makes it a efficient method for multiplexing URLLC and eMBB traffics. Finally, simulation results show that using only 60 RIS elements, we observe that the proposed scheme achieves around 99.99% URLLC packets admission rate compared to 95.6% when there is no RIS, while also achieving up to 70% enhancement on the eMBB rates.

Quadratic cost function such as Mean Squared Error (MSE) has been a widely used objective function for training deep neural networks to develop energy forecasting models in Smart Grids. In this work, Penalizing Underestimation Logarithmic Squared Error (PULSE), a novel objective function is proposed with the aim of reducing the tendency of deep learning models to underestimate the target variable. Stacked Long Short-Term Memory (LSTM) networks are adopted on the time series load demand data to investigate the performance of the proposed cost function against the widely used MSE cost function. The evaluation is performed using open-source real-world electricity load diagrams dataset covering a period of three years. The performance of the proposed scheme is examined with deep learning models through several experiments. The results demonstrate that the proposed scheme is able to eliminate the tendency to underestimate and provides competitively accurate load demand forecasting results. The results are additionally compared against the state-of-the-art machine learning models developed in the literature. The proposed cost function maintains the RMSE around 4*10-2kWh which is also the RMSE for deep learning models with MSE cost function and delivers 25% improvement in MAPE while also eliminating the underestimation of load demand.

Light-fidelity (LiFi) is a fully-networked bidirectional optical wireless communication (OWC) technology that is considered as a promising solution for high-speed indoor connectivity. In this paper, the joint estimation of user 3D position and user equipment (UE) orientation in indoor LiFi systems with unknown emission power is investigated. Existing solutions for this problem assume either ideal LiFi system settings or perfect knowledge of the UE states, rendering them unsuitable for realistic LiFi systems. In addition, these solutions consider the non-line-of-sight (NLOS) links of the LiFi channel gain as a source of deterioration for the estimation performance instead of harnessing these components in improving the position and the orientation estimation performance. This is mainly due to the lack of appropriate estimation techniques that can extract the position and orientation information hidden in these components. In this paper, and against the above limitations, the UE is assumed to be connected with at least one access point (AP), i.e., at least one active LiFi link. Fingerprinting is employed as an estimation technique and the received signal-to-noise ratio (SNR) is used as an estimation metric, where both the line-of-sight (LOS) and NLOS components of the LiFi channel are considered. Motivated by the success of deep learning techniques in solving several complex estimation and prediction problems, we employ two deep artificial neural network (ANN) models, one based on the multilayer perceptron (MLP) and the second on the convolutional neural network (CNN), that can map efficiently the instantaneous received SNR with the user 3D position and the UE orientation. Through numerous examples, we investigate the performance of the proposed schemes in terms of the average estimation error, precision, computational time, and the bit error rate. We also compare this performance to that of the k-nearest neighbours (KNN) scheme, which is widely used in solving wireless localization problems. It is demonstrated that the proposed schemes achieve significant gains and are superior to the KNN scheme.

With the growing market of Electric Vehicles (EV), the procurement of their charging infrastructure plays a crucial role in their adoption. Within the revolution of Internet of Things, the EV charging infrastructure is getting on board with the introduction of smart Electric Vehicle Charging Stations (EVCS), a myriad set of communication protocols, and different entities. We provide in this article an overview of this infrastructure detailing the participating entities and the communication protocols. Further, we contextualize the current deployment of EVCSs through the use of available public data. In the light of such a survey, we identify two key concerns, the lack of standardization and multiple points of failures, which renders the current deployment of EV charging infrastructure vulnerable to an array of different attacks. Moreover, we propose a novel attack scenario that exploits the unique characteristics of the EVCSs and their protocol (such as high power wattage and support for reverse power flow) to cause disturbances to the power grid. We investigate three different attack variations; sudden surge in power demand, sudden surge in power supply, and a switching attack. To support our claims, we showcase using a real-world example how an adversary can compromise an EVCS and create a traffic bottleneck by tampering with the charging schedules of EVs. Further, we perform a simulation-based study of the impact of our proposed attack variations on the WSCC 9 bus system. Our simulations show that an adversary can cause devastating effects on the power grid, which might result in blackout and cascading failure by comprising a small number of EVCSs.

The success in transitioning towards smart cities relies on the availability of information and communication technologies that meet the demands of this transformation. The terrestrial infrastructure presents itself as a preeminent component in this change. Unmanned aerial vehicles (UAVs) empowered with artificial intelligence (AI) are expected to become an integral component of future smart cities that provide seamless coverage for vehicles on highways with poor cellular infrastructure. Motivated by the above, in this paper, we introduce UAVs cell-free network for providing coverage to vehicles entering a highway that is not covered by other infrastructure. However, UAVs have limited energy resources and cannot serve the entire highway all the time. Furthermore, the deployed UAVs have insufficient knowledge about the environment (e.g., the vehicles' instantaneous location). Therefore, it is challenging to control a swarm of UAVs to achieve efficient communication coverage. To address these challenges, we formulate the trajectories decisions making as a Markov decision process (MDP) where the system state space considers the vehicular network dynamics. Then, we leverage deep reinforcement learning (DRL) to propose an approach for learning the optimal trajectories of the deployed UAVs to efficiently maximize the vehicular coverage, where we adopt Actor-Critic algorithm to learn the vehicular environment and its dynamics to handle the complex continuous action space. Finally, simulations results are provided to verify our findings and demonstrate the effectiveness of the proposed design and show that during the mission time, the deployed UAVs adapt their velocities in order to cover the vehicles.

Light-fidelity (LiFi) is a fully-networked bidirectional optical wireless communication (OWC) technology that is considered as a promising solution for high-speed indoor connectivity. Unlike in conventional radio frequency wireless systems, the OWC channel is not isotropic, meaning that the device orientation affects the channel gain significantly. However, due to the lack of proper channel models for LiFi systems, many studies have assumed that the receiver is vertically upward and randomly located within the coverage area, which is not a realistic assumption from a practical point of view. In this paper, novel realistic and measurement-based channel models for indoor LiFi systems are proposed. Precisely, the statistics of the channel gain are derived for the case of randomly oriented stationary and mobile users. For stationary users, two channel models are proposed, namely, the modified truncated Laplace (MTL) model and the modified Beta (MB) model. For mobile users, two channel models are proposed, namely, the sum of modified truncated Gaussian (SMTG) model and the sum of modified Beta (SMB) model. Based on the derived models, the impact of random orientation and spatial distribution of users is investigated, where we show that the aforementioned factors can strongly affect the channel gain and the system performance.

In this paper machine learning (ML) models have been developed for the application of big data load forecasting using parallel computation. The load forecasting models' performance is directly linked to system execution capacity, memory, thread count, balancing the load, and available resources. This paper is focused on two main challenges. The first challenge is to reduce the execution time of the ML models and the second one is to choose the suitable tree-based model for effective load forecasting. The paper conducts a comprehensive evaluation of the load forecasting using real-world data on energy consumption. Comprehensive results are obtained to show that the performance of random search to tune the ML models exhibits competitive performances whilst not losing the accuracy of the models and gaining a competitive advantage on the run time.

The wireless industry is moving towards developing smart cellular architectures that dynamically adjust the use of the network elements according to the service demand, and automating their operations in order to minimize both capital expenditure (CAPEX) and operation expenditure (OPEX). This involves developing efficient and unsupervised radio access network (RAN) planning, which has a direct impact on the system performance and CAPEX. This intelligent cellular planning aims at providing the base stations (BSs) configurations (e.g., coverage, user associations and antenna radiation pattern) that minimize the number of deployed BSs and meet the requirements in terms of coverage and capacity. The cellular planning optimization problem has been shown to be complex and non-scalable. Moreover, most of the existing cellular planning techniques result in an over or under provisioning architecture. Motivated by the above, we propose in this paper a novel and efficient unsupervised planning process. We make use of statistical machine learning (SML) to solve the problem at hand. The core idea of SML is that the planning parameters are treated as random variables. The parameters that maximize the corresponding joint probability distribution, conditioned on observations of users' positions, are learned or inferred using Gibbs sampling theory and Bayes' theory. To apply this theory to the planning problem, we make significant efforts to properly formulate the problem to be able to incorporate the constraints into the inference process and extract the planning parameters from the inferred model. Through several numerical examples, we compare the performance of the proposed approach to clustering-based and optimization-based existing planning approaches, and demonstrate the efficacy of our approach. We also demonstrate how our approach can leverage existing cellular infrastructures into the new design.

This paper investigates the performance of cooperative non-orthogonal multiple access (C-NOMA) in downlink communication systems. Using C-NOMA, users with more favorable channel conditions can assist communication between the base station (BS) and the users with less favorable channel conditions using either full-duplex (FD) or half-duplex (HD) device-to-device (D2D) relaying and successive interference cancellation (SIC). To maximize the benefits of C-NOMA, we formulate and solve a novel optimization problem that jointly determines the optimal D2D user pairing and the optimal power control scheme in a downlink cellular system consisting of a BS that communicates with a set of spatially dispersed users. The formulated problem is a non-convex mixed-integer non-linear program (MINLP) which is difficult to solve due to the dependency between power control and user pairing. Thus, we decompose the problem into an inner power control problem and an outer pairing problem. For the inner problem, we derive the optimal closed-form expressions for both HD and FD relaying modes, while the outer problem of user pairing can be solved using the well-known Hungarian method. The simulation results show that the proposed framework outperforms a variety of proposed schemes in the literature and that it can obtain the optimal pairing and power control policies for a network with 100 users in negligible computational time.

We study in this paper the secrecy performance of the multiple-input multiple-output (MIMO) visible light communication (VLC) wiretap channel. The underlying system model comprises three nodes: one transmitter, equipped with multiple fixtures of LEDs, one legitimate receiver and one eavesdropper, each equipped with multiple photo-diodes (PDs). The VLC channel is modeled as a real-valued amplitude-constrained Gaussian channel and the eavesdropper is assumed to be randomly located in the coverage area. We propose a low-complexity precoding scheme that aims at enhancing the secrecy performance of the system. Specifically, assuming discrete input signaling, we derive an average achievable secrecy rate for the underlying system in a closed-form, and the derived expression is a function of the precoding matrix and the input distribution using stochastic geometry. Then, we propose a low-complexity design of the precoding matrix based on the generalized singular value decomposition (GSVD) of the channel matrices of the system. We examine the resulting average achievable secrecy rate using the truncated discrete generalized normal (TDGN) distribution, which is the best-known discrete distribution available in the literature. Finally, we validate the proposed scheme through extensive simulations and we demonstrate its superiority when compared to other schemes reported in the literature.

The global evolution of wireless technologies and intelligent sensing devices are transforming the realization of smart cities. Among the myriad of use cases, there is a need to support applications whereby low-resource IoT devices need to upload their sensor data to a remote control centre by target hard deadlines; otherwise, the data becomes outdated and loses its value, for example, in emergency or industrial control scenarios. In addition, the IoT devices can be either located in remote areas with limited wireless coverage or in dense areas with relatively low quality of service. This motivates the utilization of UAVs to offload traffic from existing wireless networks by collecting data from time-constrained IoT devices with performance guarantees. To this end, we jointly optimize the trajectory of a UAV and the radio resource allocation to maximize the number of served IoT devices, where each device has its own target data upload deadline. The formulated optimization problem is shown to be mixed integer non-convex and generally NP-hard. To solve it, we first propose the high-complexity branch, reduce and bound (BRB) algorithm to find the global optimal solution for relatively small scale scenarios. Then, we develop an effective sub-optimal algorithm based on successive convex approximation in order to obtain results for larger networks. Next, we propose an extension algorithm to further minimize the UAV's flight distance for cases where the initial and final UAV locations are known a priori. We demonstrate the favourable characteristics of the algorithms via extensive simulations and analysis as a function of various system parameters, with benchmarking against two greedy algorithms based on distance and deadline metrics.

In this paper, a data-driven approach has been used to identify and categorize fault in the electrical power system. The proposed methodology involves efficient analysis of the data with feature vectors including the area or zone of the bus. The training is done on machine learning models to classify and identify the location of the fault. Three-phase, line to ground, line-to-line to ground, line-to-line, loss of line with no fault and loss of load at bus faults are simulated to generate labeled data with type of fault and location of fault. Two algorithms have been proposed to choose the measurements selection strategy, and results have been stated. The proposed methodology proves its validity for identification of the fault without necessary measurement of the voltage of each node. The proposed approach works with a minimum number of buses required to be as few as 5-7% of the measured buses. The accuracy, capabilities, and limitations of the proposed algorithm are verified on IEEE 68 bus model. The highest classification accuracy attained on one of the test cases is 91%.

This paper investigates the performance of cooperative non-orthogonal multiple access (C-NOMA) in cellular downlink systems. The system model consists of a base station (BS) that needs to serve multiple users within a region of service. A subset of the users, especially those located close to the cell edge, undergo severe fading and suffer from poor channel quality and low achievable rates. To overcome this problem, CNOMA is proposed as the system design methodology, in which users that have the capability of full-duplex (FD) communication can assist the transmissions between the BS and users with poor channel quality through device-to-device (D2D) communications. To harness both the multiplexing gain from NOMA and the diversity gain from FD-D2D communications, we formulate and solve a novel optimization problem that jointly determines D2D user pairing and power allocation. The formulated problem is a mixed-integer non-linear program (MINLP) with prohibitively high complexity. To overcome this issue, a two-step policy is proposed to solve the problem in polynomial time. Our simulation results show that with reasonable assumptions, the proposed scheme always outperforms some existing schemes in the literature, and that, under undesirable conditions, e.g., poor D2D channel conditions or imperfect self-interference (SI) cancellation, the proposed scheme is reduced to conventional NOMA.

In recent years, providing connectivity to fast-moving vehicles on highways has been the focus of the wireless research community. In this paper, in the context of V2X, we propose using unmanned aerial vehicles (UAVs) to serve vehicles on a highway, where a UAV is dispatched in disaster situations (such as floods or earthquakes) to serve these vehicles, or to provide better coverage when vehicles are out of reach of road side units. We consider free flow scenario where vehicles moving between two road-side units and where the infrastructure is partially or totally unavailable. Our goal is to guarantee a certain Quality of Service (QoS) for each vehicle on the highway by jointly optimizing the UAV trajectory and the radio resource allocation. We show that during the UAV flight time, the UAV adapts its velocity to the velocities of the vehicles in the served cluster, to maximize the minimum average rate for each vehicle. Our findings are verified through Monte-Carlo simulation where we demonstrate the effectiveness of our proposed design under different UAVs types.

Among the challenges of realizing the full potential of light-fidelity (LiFi) cellular networks are user mobility, random device orientation, and blockage. In this paper, we study the impact of those challenges on the performance of LiFi networks in an indoor environment using measurement-based channel models, unlike existing studies that rely on theoretical channel models. In our paper, we adopt spatial modulation (SM) and consider two configurations for the user equipment (TIE). A multidirectional receiver (MDR) structure is proposed, in which the PDs are located on different sides of the TIE, e.g., a smartphone. This configuration is motivated by the fact that conventional structures exhibit poor performance in the presence of random device orientation and blockage. In fact, we show that the MDR outperforms the benchmark structure by over 10 dB at bit-error ratio (BER) of 3.8 × 10-3. Moreover, an adaptive access point (AP) selection scheme for the SM is considered, where the number of APs is chosen adaptively in an effort to achieve the lowest energy requirement for a target BER and spectral efficiency. The user performance with random orientation and blockage in the entire room is evaluated for sitting and walking activities, for which the orientation-based random waypoint (ORWP) mobility model is invoked. Furthermore, we demonstrate that the proposed adaptive technique with SM outperforms the conventional spatial multiplexing system. We also study the performance of the underlying system on the uplink channel where we apply the same techniques used for the downlink channel. It is shown analytically that the multidirectional transmitter (MDT) with adaptive SM is highly energy efficient.

This paper investigates the secrecy performance of the multiple-input single-output visible light communication (VLC) wiretap channel. The considered system model comprises three nodes: a transmitter (Alice) equipped with multiple fixtures of LEDs, a legitimate receiver (Bob), and an eavesdropper (Eve), each equipped with one photo-diode. The VLC channel is modeled as a real-valued amplitude-constrained Gaussian channel. Eve is assumed to be randomly located in the same area as Bob. Due to this, artificial noise-based beamforming is adopted as a transmission strategy in order to degrade Eve's signal-to-noise ratio. Assuming discrete input signaling, we derive an achievable secrecy rate in a closed-form expression as a function of the beamforming vectors and the input distribution. We investigate the average secrecy performance of the system using stochastic geometry to account for the location randomness of Eve. We also adopt the truncated discrete generalized normal (TDGN) as a discrete input distribution. We present several examples through which we confirm the accuracy of the analytical results via Monte Carlo simulations. The results also demonstrate that the TDGN distribution, albeit being not optimal, yields performance close to the secrecy capacity.

Next-generation cellular systems are anticipated to support 100 times higher data rates (ultra-high rate) compared with the fourth generation (4G) of cellular systems. It is, therefore, necessary to develop novel spectrally efficient uplink/downlink techniques. Multiple techniques have been proposed, including the so-called non-orthogonal multiple access (NOMA) technique. However, the spectral efficiency gains achieved by NOMA over OMA techniques have been shown to be modest. Recently, we proposed a spectrally efficient technique for the downlink channel, which involves exploiting similarities among users' short bit blocks, where we showed that spectral efficiency gains of up to three times that of OMA schemes can be achieved. However, the technique cannot be extended to the uplink scenario because users are not aware of each other's bit block. To this end, we propose in this paper a spectrally efficient scheme for the uplink channel, where we exploit the similarity between the short bit blocks of the uplink and downlink sequences corresponding to one user. The downlink bit sequences are those received by a user from the base station (BS). It is assumed that the BS keeps track of the bit sequences transmitted on the downlink channel to different users. The uplink and downlink bit sequences, which are assumed to be uncorrelated, are divided into bit blocks of short lengths, and then, the similarity between those blocks is extracted. Once each user determines its similarity index (i.e., the number of similar bit blocks) between its own bit sequence and its respective downlink bit sequence, this information is communicated with the BS, which will, in turn, select the user with the largest similarity index to transmit during that resource block. The same process repeats every resource block where the user with the maximum similarity index is always selected. We propose a simple overhead exchange algorithm that facilitates the exchange of the information on the similarity indexes between the users and the BS, where we assume that this exchange of information is done through a control channel. The performance of the proposed scheme and the overhead exchange algorithm is investigated analytically and by Monte Carlo simulations. Among the parameters that we incorporate into the analysis are the user density, the length of bit blocks used to check the similarity index, and the channel correlation. We show that spectral efficiency gains of approximately two times that of OMA schemes can be achieved.

In this paper, we tackle the problem of joint angle and delays estimation (JADE) of multiple reflections of a known signal impinging on multiple receiving antennae. Based on the importance sampling (IS) concept, we propose a new non-iterative maximum likelihood (ML) estimator that enjoys guaranteed global optimality and enhanced high-resolution capabilities for both single- and multi-carrier models. The new ML approach succeeds in transforming the original multi-dimensional optimization problem into multiple two-dimensional ones thereby resulting in huge computational savings. Moreover, it does not suffer from the off-grid problems that are inherent to most existing JADE techniques. By exploiting the sparsity feature of a carefully designed pseudo-pdf that is intrinsic to the new estimator, we also propose a novel approach that enables the accurate detection of the unknown number of paths over a wide range of practical signal-to-noise ratios (SNRs). Computer simulations show the distinct advantage of the new ML estimator over state-of-the art JADE techniques both in the single- and multi-carrier scenarios. Most remarkably, they suggest that the proposed IS-based ML JADE is statistically efficient as it almost reaches the Camér-Rao lower bound (CRLB) even in the adverse conditions of low SNR levels. Using real-world channel measurements collected from four access points (APs) with IEEE 802.11ac standard's setup parameters in an indoor environment, we also show that the proposed ML estimator achieves a localization performance below 15 cm accuracy.

The proliferation of smart connected Internet of Things (IoT) devices is bringing tremendous challenges in meeting the performance requirement of their supported real-time applications due to their limited resources in terms of computing, storage, and battery life. In addition, the considerable amount of data they generate brings extra burden to the existing wireless network infrastructure. By enabling distributed computing and storage capabilities at the edge of the network, multi-access edge computing (MEC) serves delay sensitive, computationally intensive applications. Managing the heterogeneity of the workload generated by IoT devices, especially in terms of computing and delay requirements, while being cognizant of the cost to network operators, requires an efficient dimensioning of the MEC-enabled network infrastructure. Hence, in this paper, we study and formulate the problem of MEC resource provisioning and workload assignment for IoT services (RPWA) as a mixed integer program to jointly decide on the number and the location of edge servers and applications to deploy, in addition to the workload assignment. Given its complexity, we propose a decomposition approach to solve it which consists of decomposing RPWA into the delay aware load assignment sub-problem and the mobile edge servers dimensioning sub-problem. We analyze the effectiveness of the proposed algorithm through extensive simulations and highlight valuable performance trends and trade-offs as a function of various system parameters.

Along with the dramatic increase in the number of IoT devices, different IoT services with heterogeneous QoS requirements are evolving with the aim of making the current society smarter and more connected. In order to deliver such services to the end users, the network infrastructure has to accommodate the tremendous workload generated by the smart devices and their heterogeneous and stringent latency and reliability requirements. This would only be possible with the emergence of ultra reliable low latency communications (uRLLC) promised by 5G. Mobile Edge Computing (MEC) has emerged as an enabling technology to help with the realization of such services by bringing the remote computing and storage capabilities of the cloud closer to the users. However, integrating uRLLC with MEC would require the network operator to efficiently map the generated workloads to MEC nodes along with resolving the trade-off between the latency and reliability requirements. Thus, we study in this paper the problem of Workload Assignment (WA) and formulate it as a Mixed Integer Program (MIP) to decide on the assignment of the workloads to the available MEC nodes. Due to the complexity of the WA problem, we decompose the problem into two subproblems; Reliability Aware Candidate Selection (RACS) and Latency Aware Workload Assignment (LAWA-MIP). We evaluate the performance of the decomposition approach and propose a more scalable approach; Tabu meta-heuristic (WA-Tabu). Through extensive numerical evaluation, we analyze the performance and show the efficiency of our proposed approach under different system parameters.

We study in this paper the achievable secrecy rate of the Gaussian multiple-input single-output (MISO) visible light communication (VLC) in the presence of randomly located eavesdroppers. We consider a system model comprising a transmitter (Alice) equipped with multiple fixtures of LEDs, one legitimate receiver (Bob) equipped with a single photo-diode (PD) and a group of randomly located eavesdroppers, each equipped with a single PD. The channel is modeled as deterministic, real- valued and subject to an amplitude constraint, through which Alice wants to communicate privately with Bob. We consider the case where the eavesdroppers are colluding together, i.e., they act jointly in eavesdropping on the communication between Alice and Bob. We adopt the truncated generalized normal (TGN) as input signaling and beamforming as transmission strategy. At first, we derive a closed-form expression of the average achievable secrecy rate as a function of the density of eavesdroppers using stochastic geometry. Then, we investigate the optimal beamformer that maximizes the average achievable secrecy rate of the system. Finally, the analysis is verified by Monte Carlo simulations.

We study in this paper the secrecy performance of the multiple-input single-output (MISO) visible light communication (VLC) channel in the presence of randomly located eavesdroppers. We consider a system model comprising a transmitter, equipped with multiple fixtures of LEDs, one legitimate receiver equipped with a single photo-diode (PD) and a group of randomly located (and colluding) eavesdroppers, each equipped with a single PD. We derive a closed-form expression for the average achievable secrecy rate as a function of the beamforming vector, the input distribution, and the location density of the eavesdroppers using stochastic geometry. We then investigate the optimal beamformer that maximizes the average achievable secrecy rate. We corroborate the analytical results through Monte Carlo simulations and we demonstrate substantial improvements provided by the proposed scheme over existing ones.

Non-orthogonal multiple access (NOMA) has been proposed as an alternative to orthogonal multiple access (OMA) in an effort to enhance the spectral efficiency of 5G cellular systems. However, the NOMA throughput gain relative to that of OMA has been shown to be modest. In this paper, we propose a novel NOMA scheme that exploits the partial overlap (i.e., similarity) among users bit sequences at the base station (BS). Specifically, users bit sequences are divided into blocks of short lengths, i.e., short bit sequences. Then, one user is selected and served during a given transmission time interval (TTI). Users whose bit sequences partially overlap with the bit sequence of the served user are also (partially) served during the same TTI. At the receiving end, the receiver corresponding to the served user recovers its entire bit sequence, whereas the partially served users recover their corresponding overlapping bit blocks and ignore the rest of the sequence. The performance of the proposed scheme is analyzed in terms of the overall throughput. We show that a throughput gain of up to three times that of existing OMA schemes can be achieved. Moreover, we show that the average rate per user decreases slightly as the number of users increases, whereas it linearly decreases with the number of users in existing NOMA schemes. We stress here that the proposed scheme completely differs from existing NOMA schemes as the latter schemes are based on power allocation at the BS where successive interference cancellation is normally used. The implication of this is that the proposed scheme provides substantial throughput gains without causing interference among users and without adopting a specific power allocation at the BS.

Cooperative relaying is often deployed to enhance the communication reliability (i.e., diversity order) and consequently the end-to-end achievable rate. However, this raises several security concerns when the relays are untrusted, since they may have access to the relayed message. In this paper, we study the achievable secrecy diversity order of cooperative networks with untrusted relays. In particular, we consider a network with an N-antenna transmitter (Alice), K single-antenna relays, and a single-antenna destination (Bob). We consider the general scenario, where there is no relation between N and K, and therefore, K can be larger than N. Alice and Bob are assumed to be far away from each other, and all communication is done through the relays, i.e., there is no direct link. Providing secure communication while enhancing the diversity order has been shown to be very challenging. In fact, it has been shown in the literature that the maximum achievable secrecy diversity order for the adopted system model is one (while using artificial noise jamming). In this paper, we adopt a nonlinear interference alignment scheme that we have proposed recently to transmit the signals from Alice to Bob. We analyze the proposed scheme in terms of the achievable secrecy rate and secrecy diversity order. Assuming Gaussian inputs, we derive an explicit expression for the achievable secrecy rate and show analytically that a secrecy diversity order of up to min(N, K) - 1 can be achieved using the proposed technique. We provide several numerical examples to validate the obtained analytical results and demonstrate the superiority of the proposed technique to its counterparts that exist in the literature.

We study, in this paper, the secrecy performance of a multi-user (MU) multiple-input single-output visible light communication broadcast channel with confidential messages. The underlying system model comprises K +1 nodes: a transmitter (Alice) equipped with N fixtures of LEDs and K spatially dispersed users, each equipped with a single photo-diode. The MU channel is modeled as deterministic and real-valued and assumed to be perfectly known to Alice, since all users are assumed to be active. We consider typical secrecy performance measures, namely, the max-min fairness, the harmonic mean, the proportional fairness, and the weighted fairness. For each performance measure, we derive an achievable secrecy rate for the system as a function of the precoding matrix. As such, we propose algorithms that yield the best precoding matrix for the derived secrecy rates, where we analyze their convergence and computational complexity. In contrast, what has been considered in the literature so far is zero-forcing (ZF) precoding, which is suboptimal. We present several numerical examples through which we demonstrate the substantial improvements in the secrecy performance achieved by the proposed techniques compared with those achieved by the conventional ZF. However, this comes at a slight increase in the complexity of the proposed techniques compared with ZF.

The notion of non-orthogonal multiple access (NOMA) for 5G essentially relies on the availability of the channel state information at the transmitter (CSIT). Such knowledge is used to judiciously allocate power among users to make their signals separable at their respective receivers while employing successive interference cancellation (SIC). Feeding back the CSI from the users to the BS (transmitter) is obviously bandwidth consuming. Reducing such an overhead is of great importance and has been of interest in recent years. Furthermore, existing NOMA techniques become inapplicable when the CSI is unavailable at the BS. In this case, the BS has only the option of allocating power among users blindly, including equal power splitting, which has been shown to yield poor performance in terms of outage probability and error probability. This motivates us to develop a NOMA scheme that does not require CSI knowledge at the BS. We make use of a nonlinear interference alignment technique that we have proposed recently, namely, interference dissolution, to develop the proposed NOMA scheme, which allows the BS to communicate with two users simultaneously while keeping signals perfectly separable at their respective receivers. We develop the proposed scheme for multiple-input single-output and single-input single-output downlink channels. We analyze the proposed technique analytically in terms of the achievable degrees-of-freedom and achievable rate per user. We show that the proposed NOMA scheme outperforms existing NOMA techniques in terms of the outage probability and error probability.

We propose in this paper a physical‐layer security (PLS) scheme for dual‐hop cooperative networks in an effort to enhance the communications secrecy. The underlying model comprises a transmitting node (Alice), a legitimate node (Bob), and an eavesdropper (Eve). It is assumed that there is no direct link between Alice and Bob, and the communication between them is done through trusted relays over two phases. In the first phase, precoding‐aided spatial modulation (PSM) is employed, owing to its low interception probability, while simultaneously transmitting a jamming signal from Bob. In the second phase, the selected relay detects and transmits the intended signal, whereas the remaining relays transmit the jamming signal received from Bob. We analyze the performance of the proposed scheme in terms of the ergodic secrecy capacity (ESC), the secrecy outage probability (SOP), and the bit error rate (BER) at Bob and Eve. We obtain closed‐form expressions for the ESC and SOP and we derive very tight upper‐bounds for the BER. We also optimize the performance with respect to the power allocation among the participating relays in the second phase. We provide examples with numerical and simulation results through which we demonstrate the effectiveness of the proposed scheme.

We investigate in this paper the achievable secrecy sum-rate of the multiple-input single-output (MISO) visible light communication (VLC) broadcast Gaussian channel with confidential messages. We consider a system model comprising (K +1) nodes: a transmitter (Alice) equipped with N fixtures of LEDs and K (K ≤ N) spatially dispersed users, each equipped with a single photo-diode (PD). The channel is modeled as deterministic and real-valued, subject to amplitude and power constraints, through which Alice wants to transmit in one channel use K confidential messages. We propose a new precoding scheme based on the eigenvalues of the pencils of the different K MISO VLC channels in order to maximize the achievable secrecy sum-rate of the overall system. The motivation behind this scheme is based on the fact that the optimal precoding scheme maximizing the achievable secrecy rate of the single user MISO VLC wiretap channel is expressed through the eigenvector associated to the largest eigenvalue of the wiretap channel's pencil. We investigate both cases, namely, when the locations of users are known, i.e., perfect channel state information (CSI) is available to Alice, or unknown, i.e., imperfect CSI. Furthermore, we adopt the truncated generalized normal (TGN) distribution as input signaling. We present several examples which demonstrate the substantial improvements in the secrecy sum-rates achieved by the proposed techniques compared to those achieved by zero-forcing (ZF) precoding.

Power-domain non-orthogonal multiple access (NOMA) for 5G essentially relies on the availability of the channel state information (CSI) at the transmitter. Feeding back the CSI from the users to the transmitter is clearly bandwidth consuming. In addition, when the CSI is unavailable at the transmitter (CSIT), NOMA techniques become inapplicable, and in such scenario, allocating power among users blindly, including equal power splitting, has been shown to yield poor probability of error performance. To this end, we develop a NOMA technique that does not require CSI knowledge at the transmitter, i.e., with unknown CSIT. The proposed technique allows the transmitter to communicate with multiple users simultaneously while keeping signals perfectly separable at their respective receivers. We apply the proposed technique to a two-user multiple-input single-output (MISO) two-user downlink channel with unknown CSI where the available degree of freedom (DoF) is one. We show that it is possible to allocate 1/2 DoF to each user, implying that the received signals can be perfectly separated at the receiver, i.e., without interference. We analyze the performance of the proposed scheme in terms of the achievable DoF per user and the symbol error rate (SER). We present numerical examples to validate the efficacy of the proposed scheme as a NOMA technique for 5G systems, and compare its performance in terms of the SER to that of existing schemes and demonstrate its superiority.

Real interference alignment is efficient in breaking-up a one-dimensional space over time-invariant channels into fractional dimensions. As such, multiple symbols can be simultaneously transmitted with fractional degrees-of-freedom (DoF). Of particular interest is when the one dimensional space is partitioned into two fractional dimensions. In such scenario, the interfering signals are confined to one sub-space and the intended signal is confined to the other sub-space. Existing real interference alignment schemes yield poor achievable rate at finite signal-to-noise ratio (SNR), which is of interest from a practical point of view. In this paper, we propose a radically novel nonlinear interference alignment technique, which we refer to as Interference Dissolution (ID). ID allows to break-up a one dimensional space into two fractional dimensions while achieving near-capacity performance for the entire SNR range. This is achieved by aligning signals by signals, as opposed to aligning signals by the channel. We introduce ID by considering a timeinvariant, point-to-point multiple-input single-output (MISO) channel. This channel has a one-dimensional space and offers one DoF. We show that, by breaking-up the one dimensional space into two sub-spaces, ID achieves a rate of two symbols per channel use while providing \ DoF for each symbol. We also propose a decoder and prove its optimality. We compare numerically the performance of ID in terms of the achievable rate performance to that of existing schemes and demonstrate ID's superiority.

This paper presents two new implementations of the maximum likelihood (ML) time delay estimation (TDE) from multi-carrier (MC) Direct-Sequence Spread Spectrum (DSSS) in multipath MIMO transmissions that will characterize future 5G radio interface technologies (RITs). The first TDE, based on expectation maximization (EM), provides accurate estimates of the delays when a good initialisation of the parameters is available. The second TDE returns the global maximum of the compressed likelihood function (CLF) using the importance sampling (IS) technique without requiring any initialization. Interestingly, in the non-data-aided (NDA) case, temporal, spatial (transmit and receive), and frequency samples have the same impact on estimation accuracy and performance bound which depends on the product of these dimensions regardless of the channel correlation type. Furthermore, we cope with such channel correlations that arise in practice and, hence, become very challenging both in estimation and CRLB derivation in the data-aided (DA) case, but that have been so far overlooked in previous works.

We investigate in this paper the achievable secrecy rate of the multiple-input multiple-output (MIMO) visible light communication (VLC) Gaussian wiretap channel. We consider a system model comprising three nodes: one transmitter (Alice) equipped with multiple fixtures of LEDs, one legitimate receiver (Bob) and one eavesdropper (Eve), each equipped with multiple photo-diodes (PDs). We study at first the problem of optimal signaling scheme that maximizes the achievable secrecy rate of the MIMO VLC wiretap channel. We consider the cases where the location of Eve is known (i.e. perfect channel state information (CSI)) or unknown (i.e. imperfect CSI). Finally, we derive an upper bound on the secrecy capacity that we used to assess the closeness of the achievable secrecy rate to the derived bound.

In this paper, we study the achievable secrecy rate of visible light communication (VLC) links for discrete input distributions. We consider single user single eavesdropper multiple-input single-output (MISO) links. In addition, both beamforming and robust beamforming are considered. In the former case, the location of the eavesdropper is assumed to be known, whereas in the latter case, the location of the eavesdropper is unknown. We compare the obtained results with those achieved by some continuous distributions including the truncated generalized normal (TGN) distribution and the uniform distribution. We numerically show that the secrecy rate achieved by the discrete input distribution with a finite support is significantly improved as compared to those achieved by the TGN and the uniform distributions.

We propose in this paper a physical layer security (PLS) scheme for dual-hop cooperative networks in an effort to enhance the communications secrecy. The underlying model comprises a transmitting node (Alice), a legitimate node (Bob) and an eavesdropper (Eve). It is assumed that there is no direct link between Alice and Bob, and the communication between them is done through trusted relays over two phases. In the first phase, precoded spatial modulation (PSM) is employed, owing to its low interception probability, while simultaneously transmitting a jamming signal from Bob. In the second phase, the selected relay detects and transmits the intended signal, whereas the remaining relays transmit just the jamming signal received from Bob. We analyze the performance of the proposed scheme in terms of the ergodic secrecy capacity (ESC) and secrecy outage probability (SOP) where we obtain closed form expressions for those metrics. We also optimize the performance with respect to the power allocation among the participating relays in the second phase. We provide examples with numerical and simulations results through which we demonstrate the effectiveness of the proposed scheme. We also provide a comparison of the bit error rate (BER) at Bob and Eve by simulation.

This paper addresses the problem of Doppler spread and carrier frequency offset (CFO) estimation under flat-fading Rayleigh channels. We develop a new low-cost and robust approximate maximum likelihood (ML) estimator for these two key parameters that builds upon an elegant two-ray approximation model of the channel's covariance matrix. The latter is then inverted analytically thereby yielding a closed-form expression for the underlying log-likelihood function that is prone to easy evaluation by the fast Fourier transform. Computer simulations show that the new estimator is accurate over wide ranges of the Doppler spread and CFO parameters. Moreover, it outperforms many state-of-the-art techniques under the adverse conditions of short data records and/or low SNR thresholds. Most prominently, it exhibits an unprecedented robustness to the Doppler spectrum shape of the channel since it does not require its a priori knowledge.

In this paper, we study the achievable secure degrees-of-freedom (sdof) for the multiple-input singleoutput (MISO) wiretap channel with an unknown eavesdropper. It is assumed that the eavesdropper's (Eve's) channel state information (CSI) is unknown to the transmitter (Alice) and legitimate receiver (Bob). Recent studies have shown that the achievable sdof in the sense of strong secrecy is zero when Eve's number of antennas is equal to or more than Bob's number of antennas, which is the scenario considered in this paper. To this end, we propose a novel precoding technique and a coding strategy that together achieve full sdof in the sense of strong secrecy without knowing Eve's CSI and without using artificial noise. The proposed precoding method uses the CSI of the Alice-Bob channel in a nonlinear fashion, which makes the transmitted symbols undecodable at Eve. The proposed coding scheme is based on the channel resolvability concept and ensures strong secrecy. Achieving full sdof with an unknown Eve's CSI is significant, because it is contrary to what is believed about the achievable sdof for the MISO wiretap channel in the sense of strong secrecy. We also show that the proposed scheme achieves near Alice-Bob's channel capacity in the sense of strong secrecy with a probability approaching one at finite signal-to-noise ratio.

Device-to-device (D2D) communications are now considered an integral part of future 5G networks, which will enable direct communication between user equipments and achieve higher throughputs than conventional cellular networks, but with the increased potential for co-channel interference. The physical channels, which constitute D2D communications, can be expected to be complex in nature, experiencing both line-ofsight (LOS) and non-LOS conditions across closely located D2D pairs. In addition to this, given the diverse range of operating environments, they may also be subject to clustering of the scattered multipath contribution, i.e., propagation characteristics which are quite dissimilar to conventional Rayleigh fading environments. To address these challenges, we consider two recently proposed generalized fading models, namely κ-μ and η-μ, to characterize the fading behavior in D2D communications. Together, these models encompass many of the most widely utilized fading models in the literature such as Rayleigh, Rice (Nakagami-n), Nakagami-m, Hoyt (Nakagami-q), and One-sided Gaussian. Using stochastic geometry, we evaluate the spectral efficiency and outage probability of D2D networks under generalized fading conditions and present new insights into the tradeoffs between the reliability, rate, and mode selection. Through numerical evaluations, we also investigate the performance gains of D2D networks and demonstrate their superiority over traditional cellular networks.

In this paper, we address the problem of time delay estimation (TDE) from single-carrier (SC) or multi-carrier (MC) direct-sequence spread spectrum (DSSS) multipath transmissions in the presence of multiple transmit and/or receive antennas that will characterize future 5G radio interface technologies (RITs), such as coded-domain nonorthogonal multiple access. We derive for the first time a closed-form expression for the Cramer-Rao lower bound (CRLB) and develop two maximum likelihood (ML) multipath TDEs for SC DSSS single-input multiple-output (SIMO) in the non-data-aided (NDA) case. The first TDE, based on iterative expectation maximization (EM), provides accurate estimates whenever a good initial guess of the parameters is available at the receiver. The second TDE implements the ML criterion in a non-iterative way and finds the global maximum of the compressed likelihood function using the importance sampling (IS) technique without requiring any initialization. We also extend both the SC DSSS SIMO CRLB and the two new SC DSSS SIMO ML NDA TDEs to MC DSSS RITs and to multiple-input multiple-output structures with any diversity versus multiplexing pre-coding type before generalizing them all to the data-aided (DA) case. Simulations suggest that the EM TDE is suitable for large observation in space, time, and/or frequency, whereas the IS TDE is preferred in the opposite case of very short data records. Moreover, we show in the NDA case, both analytically and by simulations, that spatial (transmit and receive), temporal, and frequency samples interchangeably have the same impact on estimation accuracy and performance bound regardless of the channel correlation type and amount present in each dimension. Furthermore, we are able to properly cope with such channel correlations that do indeed arise in practice and, hence, become very challenging both in estimation and CRLB derivation in the DA case, but that have been so far overlooked in previous works.

We study the secrecy capacity of the multiple- input single-output (MISO) Gaussian wiretap visible light communication (VLC) channel. We study a typical VLC scenario with one transmitter, one legitimate receiver, and one eavesdropper. Specifically, we compute the achievable secrecy rate for various input signaling distributions, including the truncated generalized normal (TGN) and uniform distributions. The transmitter is equipped with multiple light sources, while the legitimate and unauthorized receivers are each equipped with a single photodetector. We analyze the achievable secrecy rates via transmit beamforming and artificial noise. In addition, both zero-forcing beamforming and robust beamforming are considered. In the former case, the location of the eavesdropper is assumed to be known, whereas in the latter case, the location of the eavesdropper is unknown. Our numerical results show that the secrecy rate achieved by the TGN distribution is significantly improved as compared to those achieved by the truncated Gaussian and uniform distributions, for both zero-forcing beamforming and robust beamforming. We also derive an upper bound on the achievable secrecy capacity that we used to assess the closeness of the achievable secrecy rates to the derived bound.

Based on the concept of reconfigurable antennas (RAs), SSK-RA has been recently proposed as a novel transmission scheme to improve the performance of space shift keying (SSK). In this context, it has been shown that RAs' reconfigurable properties can be used as additional degrees of freedom to enhance the throughput, implementation complexity, and error performance of SSK. In this paper, we extend SSK-RA to cognitive radio (CR) systems in an effort to improve the secondary system's performance in a spectrum sharing scenario. Taking advantage of the interplay between RAs and the propagation channels for both the secondary and interference links, we propose a RA-based scheme with pointing-direction reconfiguration aiming at improving the secondary system's performance while verifying an outage interference constraint to the primary user (PU). In this paper, we analyse the performance of the proposed scheme in Rician fading channels and provide simulation examples confirming these analytical results. The proposed schemes are shown to offer better bit error rate (BER) performance and lower implementation complexity when compared to conventional antenna-based spectrum sharing systems.

Future wireless networks are expected to achieve higher data rates and ubiquitous coverage by seamless cooperation among diverse network technologies. However, it also increases the risk of co-channel interference and introduces the possibility of correlation in the aggregated interference. To address this problem, we adopt a stochastic geometry based approach by assuming that the interfering nodes are randomly distributed according to a Poisson point process (PPP). Using this approach, we derive closed-form expressions for the successful transmission probability and local delay in relay networks with correlated interference. Additionally, we find the optimal transmission probability p that jointly maximizes the successful transmission probability and minimizes the local delay. Numerical results are provided to confirm that the proposed joint optimization strategy achieves significant performance gains compared to conventional schemes.

Based on the concept of reconfigurable antennas (RAs), space-shift keying (SSK)-RA has been recently proposed as a novel transmission scheme to improve the performance of SSK. In this context, it has been shown that RAs' reconfigurable properties can be used as additional degrees of freedom to enhance the throughput, implementation complexity, and error performance of SSK. In this paper, we study the implementation of SSK-RA within underlay cognitive radio systems in an effort to improve the performance of the secondary user while verifying the constraints set by the primary user (PU). Taking advantage of the interplay between RAs and the propagation channels for both the secondary and interference links, we propose an RA-based scheme with beam-direction reconfiguration aiming at improving the secondary system's performance while verifying an outage interference constraint to the PU. In this paper, we analyze the performance of the proposed scheme in Rician fading channels and provide simulation examples confirming these analytical results. The proposed schemes are shown to offer enhanced bit error rate performance and lower implementation complexity when compared with conventional antenna-based spectrum sharing systems.

In this work, we use the concept of the new emerging technology of reconfigurable antennas (RAs) in an effort to improve the performance of space shift keying (SSK). Indeed, the reconfigurable properties of RAs can be used as additional degrees of freedom to enhance the performance of SSK in terms of throughput, system complexity, and error performance. In this context, considering correlated and nonidentically distributed Rician fading channels, we propose a number of SSK-RA schemes using SSK with antenna-state selection while taking advantage of the effect of RAs on the multipath propagation channel. More specifically, based on the variation of the Rician K-factor and the correlation coefficients with different antenna states, the proposed schemes jointly optimize the fading and correlation parameters to enhance SSK's performance. In this paper, we analyze the performance of the proposed SSK-RA schemes over Rician fading channels in terms of average spectral efficiency (ASE) and bit error rate (BER). We also provide several simulation examples through which we corroborate these analytical results. The proposed schemes are shown to enhance the performance of SSK both in terms of ASE and BER. For the same ASE, compared to that of SSK, not only do the proposed SSK-RA schemes provide much better error performance, they also reduce the implementation cost and the overall system complexity considerably.

In this work, we use the concept of reconfigurable antennas (RAs) in an effort to improve the performance of space shift keying (SSK). Indeed, the reconfigurable properties of RAs can be used as additional degrees of freedom in order to enhance the performance of SSK in terms of throughput, system complexity, and error performance. In this context, we propose a number of SSK-RA schemes using SSK with antenna-state selection while taking advantage of the interplay between RAs and the propagation channel. In this paper, we analyse the performance of the proposed SSK-RA schemes in Rician fading channels in terms of average spectral efficiency (ASE) and average bit error rate (ABER).We also provide several simulation examples through which we corroborate these analytical results. The proposed schemes are shown to enhance the performance of SSK both in terms of ASE and ABER. For the same ASE as SSK, SSK-RA schemes not only provide much better error performance but also considerably reduce the implementation cost and the overall system complexity.

We have recently proposed adaptive spatial modulation (ASM) for multiple antenna systems with the aim of improving the energy efficiency through spatial modulation (SM) and improving the average spectral efficiency (ASE) through adaptive modulation. In this paper, we extend ASM to spectrum sharing systems in an effort to improve the secondary system's performance in terms of energy efficiency and ASE. Based on a limited feedback from the primary user (PU), the secondary transmitter (ST) uses power adaptation in order to maximize its ASE while respecting a peak interference constraint to the PU. When compared to the recently-proposed fixed power scheme (FPS), the adaptive power scheme (APS) proposed in this paper offers lower transmission delays, higher effective throughput, and comparable error performance. In this work, we analyze the performance of APS in terms of ASE, effective throughput, and average bit error rate (ABER). We also provide several simulations through which we corroborate these analytical results.

Future mobile networks are converging toward heterogeneous multitier networks, where macro-, pico-, and femtocells are randomly deployed based on user demand. A popular approach for analyzing heterogeneous networks (HetNets) is to use stochastic geometry and treat the location of BSs as points distributed according to a homogeneous Poisson point process (PPP). However, a PPP model does not provide an accurate model for the interference when nodes are clustered around highly populated areas. This motivates us to find better ways to characterize the aggregate interference when transmitting nodes are clustered following a Poisson cluster process (PCP) while taking into consideration the fact that BSs belonging to different tiers may differ in terms of transmit power, node densities, and link reliabilities. To this end, we consider K-tier HetNets and investigate the outage probability, the coverage probability, and the average achievable rate for such networks. We compare the performance of HetNets when nodes are clustered and otherwise. By comparing these two types of networks, we conclude that the fundamental difference between a PPP and a PCP is that, for a PPP, the number of simultaneously covered mobiles and the network capacity linearly increase with K. However, for a PCP, the improvements in the coverage and the capacity diminish as K grows larger, where the curves saturate at some point. Based on these observations, we determine the scenarios that jointly maximize the average achievable rate and minimize the outage probability.

We have recently introduced adaptive spatial modulation (ASM) for multiple antenna systems, with the aim of improving the energy efficiency through spatial modulation (SM) and improving the average spectral efficiency (ASE) through adaptive modulation (AM). In this paper, we extend ASM to cognitive radio (CR) systems in an effort to improve the secondary system's performance in terms of energy efficiency and ASE. To this end, we propose two ASM schemes, one referred to as fixed power scheme (FPS) and the other as adaptive power scheme (APS). In both schemes, the secondary transmitter (ST) has limited knowledge of the channel state information (CSI) of the interference link. The difference between the two schemes, however, lies in the way the limited CSI is used to adapt the transmit power and/or modulation. As a benchmark, we also consider the scenario where the ST has perfect knowledge of the CSI of the interference link. For all cases, we analyze the performance in terms of ASE, average delay, and bit error rate (BER). We show that the proposed schemes offer tradeoffs in terms of the previously mentioned performance metrics, thus offering different options for applying ASM to CR systems. We also provide several simulation examples through which we corroborate the analytical results.

This paper tackles the problem of minimum-energy cooperative transmission in wireless networks under the assumption of limited channel state information at the transmitters. It is assumed that only the average statistics of the fading channels are known to the transmitters. The objective here is to jointly optimize the set of relays and the transmission powers for both the broadcasting and cooperative transmission phases while satisfying probabilistic signal-to-noise ratio (SNR) constraints at the relays and at the destination node. Increasing the broadcasting power expands the set of potential relays and decreases the required power for cooperative transmission. Hence, there is a compromise in the selection of the power values, which is addressed in this work using a chance-constrained optimization framework. A closed-form approximate solution is also presented, which provides a low-complexity transmission scheme for energy-harvesting wireless networks. Simulations are presented to demonstrate the efficacy of the proposed approach and the approximate solution.

In this paper, we propose an adaptive network coding scheme for cognitive relay networks comprising multiple secondary sources communicating with a common destination in the presence of multiple primary users. Conventional network coding schemes developed for cognitive radio networks normally use global encoding kernels to achieve the minimum end-to-end outage probability. Finding the global kernel is computationally inefficient especially when the number of nodes in a network changes. To this end, we propose a network coding scheme that evenly groups the codewords into multiple subsets, linearly combines the network encoded codewords over the reduced subset, and dynamically adjusts the encoding set size to minimize the end-to-end outage probability. An advantage of the proposed network coding scheme is that it achieves lower end-to-end outage probability as compared to the conventional network coding scheme over the whole signal-to-noise ratio (SNR) range with a small additional overhead. We derive closed-form expressions for the link outage probability while taking the interference constraints into consideration. We also derive the exact end-to-end outage probability of the proposed scheme and compare its performance to that of conventional fixed network coding. We show that the proposed scheme provides a trade-off between the probability of relay cooperation and network coding gain. We demonstrate through numerical examples that the proposed adaptive network coding scheme achieves gains of more than 4 dB at a target outage probability of 10-2as compared to conventional fixed network coding schemes.

This paper is concerned with energy-efficient routing in wireless sensor networks. Most of the existing routing schemes assign energy-related costs to network links and obtain the shortest paths for the nodes to balance the flowing traffic within the network and increase its lifetime. However, the optimal link cost values and the maximum achievable lifetime are not known for the majority of the existing schemes. A framework is provided in this work to analytically derive the best achievable performance that can be obtained by any distributed routing algorithm based on the shortest-path approach. Given a network configuration and an energy consumption model, the presented framework provides the optimal link cost assignment which yields the maximum lifetime in a distributed shortest-path routing strategy. The results are extended to the case of variable link cost assignment as well. A heuristic algorithm is also developed to obtain approximate solutions to the best performance problem with limited computational complexity. In particular, the proposed framework provides the optimal route selection as a benchmark to evaluate the energy efficiency of existing routing algorithms.

We consider in this paper relay assignment for cooperative systems with mutiple source-destination pairs. The objective here is to assign the relays to the source-destination pairs in a such way that all pairs would achieve the maximum diversity. Normally, for a network with mutiple source-destination pairs, none of the destinations can acquire the channel state information (CSI) of the entire network without feedback. To this end, we design a practical limited feedback strategy in conjunction with two relay assignment schemes, i.e., fullset selection and subset selection, which are based on maximizing the minimum end to end (E2E) signal to noise ratio (SNR) among all pairs. In this strategy, each destination acquires its signal-to-noise ratios (SNRs), quantizes them, and feeds them back to the relays. The relays construct the end-to-end (E2E) SNR matrix and select the relay assignment choice from all possible relay assignment permutations or only a subset of these permutations. We analyze the performance of these schemes over independent Rayleigh fading channels in terms of the worst E2E SNR. We investigate the asymptotic performance of the proposed schemes at high SNR. We show that relay assignment with quantized CSI can achieves the same first-order diversity as that of full CSI, but there is a second-order diversity loss. We also demonstrate that increasing the quantization levels yields performance that is close to that of having full knowledge of the CSI.

We have recently introduced adaptive spatial modulation (ASM) [1], which comprises both adaptive modulation (AM) and spatial modulation (SM), with the aim of enhancing the average spectral efficiency (ASE) of multiple antenna systems. This technique was shown to offer high energy efficiency and low system complexity thanks to the use of SM while achieving high data rates thanks to the use of AM. Motivated by this technique and the need of such performance in a cognitive radio (CR) scenario, we extend in this paper the concept of ASM to a spectrum sharing system. In this context, we propose the ASM-CR scheme as an energy-efficient, spectrally-efficient, and low-complexity scheme for CR systems. Compared to selected existing spectrum sharing schemes, ASM-CR is shown to offer a considerable ASE improvement while experiencing a slight degradation in the average bit error rate (ABER) performance. In order to address this, a hybrid scheme compromising between ASE and ABER is proposed. The performance of both proposed schemes is analyzed in terms of ASE and ABER and confirmed with selected numerical results using Monte-Carlo simulations.

Future mobile networks are converging towards being heterogeneous, owing to the co-existence of multi-tier networks within the same geographical area, including macro, pico- and femto-cells. The deployment of such networks is generally based on user demand, which is irregular and random, implying that the deployment of base stations (BSs) is random as well. As a result, analyzing the communication protocols over heterogeneous networks (HetNets) is very challenging. A popular approach is to use stochastic geometry and treat the location of the BSs as points distributed according to a spatial Point Process. Most of the related work on the interference modeling normally assumes homogeneous Poisson point process (PPP). This assumption holds when the nodes are uniformly distributed in space, such as sensor networks or ad-hoc networks. Due to geographical factors, it may be the case for mobile users to cluster around highly populated cities and the PPP assumption does not provide an accurate model for the interference in these conditions. This motivates us to find better ways to characterize the aggregate interference when the transmitting nodes are clustered following a Poisson Cluster Process (PCP). Furthermore, the BSs belonging to different tiers may differ in terms of the transmit power, the node densities, and their link reliabilities. To this end, we consider K-tier HetNets, where, by using the Laplace transform approach, we characterize the aggregate interference at a given destination as a heavy-tailed distribution. Using the derived distribution, we investigate the probability of outage and coverage for such networks. Due to some difficulty in obtaining closed-form expressions for these measures, we derive tight bounds and verify that through numerical examples. We also compare the performance of HetNets when the nodes are clustered and otherwise. We observe that using the PPP results in larger success probability, but using the clustered process results in a larger coverage probability. We also observe that there is an optimal intensity, i.e., number of nodes, that achieves the maximum coverage probability for the given SINR (signal-to-interference-plus-noise ratio) threshold.

In this paper we study the performance of two-way relaying (TWR) over underwater acoustic (UWA) channels in conjunction with distributed space-time block coding (D-STBC). In particular, we consider the communication between two sources via relay nodes. The underlying channels are characterized as doubly selective channels. Orthogonal frequency division multiplexing (OFDM) is used to combat frequency selectivity of the channels, while front-end multiple resampling (MR) combined with frequency-domain equalization is used to combat intercarrier interference (ICI) resulting from time selectivity of the channel caused by the relative motion between the transceivers. Simulation results show the superiority of MR over its single resampling (SR) counterpart. Also, under total power constraint, AF-D-STBC (when only one source is activated at a time) outperforms AF-TWR-D-STBC, however, at the expense of less bandwidth efficiency. Also, AF-TWR-D-STBC outperforms AF-TWR (when one relay is activated) even though the former contributes more interference. Finally, to further boost the performance, successive interference cancellation (SIC) is used to extract the spatial diversity offered by the relays.

In this paper, we consider distributed beamforming for spectrum sharing networks comprising two secondary transceivers, multiple secondary relays and multiple primary transceivers. The aim of this work is to improve the secondary system performance. We assume that the relays that reliably decode the secondary signals participate in the beamforming process. We also assume the presence of mutual interference between the primary and secondary systems, while beamforming is used to suppress the interference inflicted on the primary system. However, the interference inflicted on the secondary system is not mitigated. To examine the impact of this interference on the performance of the secondary system, we derive closed-form expressions for the outage probability and bit error rate (BER) over independent and identically distributed Rayleigh fading channels. Numerical results demonstrate the efficacy of beamforming in making the secondary system performance resilient against the interference caused by the primary system.

Using spatial modulation (SM) jointly with adaptive modulation (AM), we propose a low-complexity and spectrally-efficient transmission scheme in a multiple-input multiple-output (MIMO) system. While in the conventional SM technique a fixed data rate is achieved, the proposed adaptive spatial modulation (ASM) technique is throughput-optimized by taking advantage of the wireless channel variations in order to increase the spectral efficiency of SM. ASM has been previously studied in [1] in order to improve the average bit error rate (ABER) performance of SM while only providing a fixed data rate. On the other hand, the ASM technique is introduced in this paper in order to achieve high data rates while keeping the ABER below a certain threshold. We propose two variations of ASM compromising between the spectral efficiency and the error performance. The ABER and the average spectral performance results of both variations are presented via Monte-Carlo simulations and confirmed with analytical results including asymptotic performance bounds on the ABER. These results show that the proposed ASM techniques come with a considerable spectral efficiency gain compared to SM while only requiring a limited feedback from the receiver.

In this paper we investigate the performance of distributed space-time block coding (D-STBC) orthogonal frequency division multiplexing (OFDM) over underwater acoustic (UWA) channels. In particular, we consider a relaying system consisting of one source, two relays, and one destination. The relays operate in amplify-and-forward (AF) mode. The underlying channels are assumed to be time-varying frequency selective channels, where the only source of time variation is the relative motion between transceivers. Alamouti D-STBC scheme is used in the second hop, and a two stage receiver is adopted at the destination: in the first stage, multiple resampling (MR) preprocessing of the received signals is performed to minimize the effect of intercarrier interference (ICI), and in the second stage ICI equalization is performed in the frequency domain to further reduce the effect of the residual ICI. To further boost the performance, successive interference cancellation (SIC) is used, where the estimates of the signals at the output of the ICI equalizer are used as tentative decisions. Compared to the single resampling (SR) front end preprocessing, simulation results show the superiority of MR front-end receiver. Also, SIC further boosts the performance, but still, there is a significant gap with respect to the ICI-free limit, when the receiver has perfect knowledge of ICI coefficients and eliminates them completely.

In this paper, we consider Doppler compensation for time varying underwater acoustic channels. The underlying system model comprises two sources, S1 and S2, communicating with each other through a relay, R. The transmission process goes through two phases. In the first phase, the two sources transmit simultaneously and the relay receives. In the second phase, the relay processes the received signals and broadcasts the combined signal to the destinations, i.e., sources. The underlying channels are considered to be time varying frequency selective channels, where the only source of time variation is the relative motion between the transceivers. Orthogonal frequency division multiplexing (OFDM) is used as a means to combat frequency selectivity. Two cases are considered, namely, when R uses multiple resampling (MR) preprocessing to reduce the effect of intercarrier interference (ICI) resulting from the time variation, and when R uses single resampling (SR) preprocessing. In both cases, in the second phase of transmission, each source performs MR preprocessing and after subtracting its own signal ICI equalization is performed to further reduce the effect of residual ICI. Simulation results show that performing MR at R outperforms the case when SR is used at R, however, this comes at the expense of more hardware complexity.

A key challenge of future mobile communication research is to strike an attractive compromise between wireless network's area spectral efficiency and energy efficiency. This necessitates a clean-slate approach to wireless system design, embracing the rich body of existing knowledge, especially on multiple-input-multiple-ouput (MIMO) technologies. This motivates the proposal of an emerging wireless communications concept conceived for single-radio-frequency (RF) large-scale MIMO communications, which is termed as SM. The concept of SM has established itself as a beneficial transmission paradigm, subsuming numerous members of the MIMO system family. The research of SM has reached sufficient maturity to motivate its comparison to state-of-the-art MIMO communications, as well as to inspire its application to other emerging wireless systems such as relay-aided, cooperative, small-cell, optical wireless, and power-efficient communications. Furthermore, it has received sufficient research attention to be implemented in testbeds, and it holds the promise of stimulating further vigorous interdisciplinary research in the years to come. This tutorial paper is intended to offer a comprehensive state-of-the-art survey on SM-MIMO research, to provide a critical appraisal of its potential advantages, and to promote the discussion of its beneficial application areas and their research challenges leading to the analysis of the technological issues associated with the implementation of SM-MIMO. The paper is concluded with the description of the world's first experimental activities in this vibrant research field.

In this paper, we use joint distributed beamforming and cooperative relaying in cognitive radio relay networks in an effort to enhance the spectrum efficiency and improve the performance of the cognitive (secondary) system. In particular, we consider a spectrum sharing system where a set of potential relays are employed to help a pair of secondary users in the presence of a licensed (primary) user. Among the available relays, only the reliable ones participate in the beamforming process, where the beamformer weights are obtained based on a linear optimization method. We investigate two well-known strategies, namely, selection decode-and-forward (SDF) and amplify-and-forward (AF) relaying in conjunction with distributed optimal beamforming. However, given the complexity of the performance analysis with optimal beamforming, we use zero forcing beamforming (ZFB), and compare both approaches through simulations. In this context, for SDF, we derive expressions for the probability density function (PDF) of the received signal-to-interference noise ratio (SINR) at the relays as well as at the secondary destination. As for the AF scheme, we obtain the exact expression for the cumulative distribution function (CDF) and the moment generating function (MGF) of the equivalent end-to-end SNR at the secondary destination. For both schemes, we derive closed-form expressions for the outage probability and bit error rate (BER) over independent and identically distributed Rayleigh fading channels for binary phase shift keying (BPSK) and M-ary quadrature amplitude modulation (M-QAM) schemes. Numerical results demonstrate the efficacy of the proposed scheme in improving the outage and BER performance of the secondary system while limiting the interference to the primary system. In addition, the results show the effectiveness of the combination of the cooperative diversity and distributed beamforming in compensating for the loss in the secondary system's performance due to the primary user's co-channel interference (CCI).

Multiuser cooperative schemes usually rely on relay selection or channel selection to avoid deep fading and achieve diversity while maintaining acceptable spectral efficiency. In some applications such as underwater acoustic communications, the low speed of the acoustic wave results in a very long delay between the channel state information (CSI) measurement time and the relay assignment time, which leads to a severely outdated CSI. To remedy this, we propose distributed coding schemes that aim at achieving good diversity-multiplexing trade-off (DMT) for multiuser scenarios where CSI is not available for resource allocation. We consider a network with multiple source nodes, multiple relay nodes, and a single destination. We first introduce a distributed linear block coding scheme, including Reed-Solomon codes, where each relay implements a column of the generator matrix of the code, and soft decision decoding is employed to retrieve the information at the destination side. We derive the end-to-end error performance of this scheme and show that the achievable diversity equals the minimum Hamming distance of the underlying code, while its DMT outperforms that of existing schemes. We extend the proposed scheme to distributed convolutional codes, and show that achieving higher diversity orders is also possible.

This paper presents a concise yet comprehensive description of a multichannel vehicle-to-infrastructure communication system. Existing mathematical models for such a system overlook some of its essential behavioral characteristics such as the reneging, force termination, and, ultimately, blocking of service requests (SRs). Thus, the reported performance results obtained from these models seem to be unrealistically overoptimistic. Accordingly, in this paper, a multiserver queueing model is proposed for the purpose of accurately capturing the dynamics of the aforementioned communication system and evaluating its performance. The proposed model is renowned for its complexity and the nonexistence of closed-form analytical expressions that characterize its fundamental performance metrics. Hence, approximations were exploited as a means to enhance this model's mathematical tractability. Simulations are conducted in the context of a realistic scenario with the objective of validating the proposed approximate model, verifying its accuracy, and characterizing the system's performance in terms of several new metrics. The simulations' results indicate a cataclysmic SR blocking probability in the range of 65%-85%.

In this paper, we propose a number of adaptive transmission techniques in order to improve the performance of the secondary link in a spectrum sharing system. We first introduce the concept of minimum-selection maximum ratio transmission (MS-MRT) as an adaptive variation of the existing MRT (MRT) technique. While in MRT all available antennas are used for transmission, MS-MRT uses the minimum subset of antennas verifying both the interference constraint (IC) to the primary user and the bit error rate (BER) requirements. Similar to MRT, MS-MRT assumes that perfect channel state information (CSI) is available at the secondary transmitter (ST), which makes this scheme challenging from a practical point of view. To overcome this challenge, we propose another transmission technique based on orthogonal space-time block codes with transmit antenna selection (TAS). This technique uses the full-rate full-diversity Alamouti scheme in order to maximize the secondary's transmission rate. The performance of these techniques is analyzed in terms of the average spectral efficiency (ASE), average number of transmit antennas, average delay, average BER, and outage performance. In order to give the motivation behind these analytical results, the tradeoffs offered by the proposed schemes are summarized and then demonstrated through several numerical examples.

We consider in this paper relay assignment for cooperative systems with mutiple source-destination pairs. The objective here is to assign relays to the source-destination pairs in such a way that all pairs achieve the maximum diversity. In networks with mutiple source-destination pairs, it is normally difficult for destinations to acquire the channel state information (CSI) of the entire network without feedback. To this end, we design a practical limited feedback strategy in conjunction with two relay assignment schemes, i.e., fullset selection and subset selection, which are based on maximizing the minimum end-to-end (E2E) signal to noise ratio (SNR) among all pairs. In this strategy, each destination acquires its SNR, quantizes it, and feeds it back to the relays. The relays then construct the E2E SNR table and select the relay assignment permutation from all possible relay assignment permutations or only a subset of these permutations. We analyze the performance of these schemes over independent Rayleigh fading channels in terms of the worst E2E SNR. We derive closed-form expressions for the E2E bit error rate (BER) and investigate the asymptotic performance at high SNR. We show that relay assignment with quantized CSI can achieve the same first-order diversity as that of the full CSI case, but there is a second-order diversity loss. We also demonstrate that increasing the quantization levels yields performance that is close to that of having full knowledge of the CSI.

In this paper, we consider collaborative beamforming for spectrum-sharing two-way relay networks in an effort to improve the performance of the cognitive system and enhance the spectrum efficiency. In such a joint relaying/spectrum-sharing setting, a pair of secondary transceivers communicates via a set of secondary decode-and-forward (DF) relays in the presence of multiple primary transceivers. Among the available relays, only those that receive the signals reliably participate in the cooperative beamforming process to nullify the interference inflicted on primary receivers. Furthermore, the received signals at relays and at secondary transceivers are unavoidably interfered by the signals from primary transmitters. To study the performance of the cognitive system under the effects of these co-channel interferences (CCIs) from the primary transmitters, we derive closed-form expressions for the outage probability and bit error rate (BER) over independent and identically distributed (i.i.d.) Rayleigh fading channels. Numerical results demonstrate the effectiveness of beamforming in compensating the cognitive system performance loss due to the CCIs in addition to mitigating the interference to the primary users.

We consider network coded cooperation for cognitive relay networks. The primary system comprises multiple sources and multiple destinations, whereas the secondary system comprises multiple sources, multiple relays and a single destination. We derive a closed form expression for the end-to-end outage probability for the secondary system while assuming the presence of interference constraints between the two sub-systems. Based on the diversity order analysis, we propose a framework for adaptive network coding. The proposed scheme involves using a small encoding set size for low link quality and a large encoding set for good link quality. Having a small set size increases the probability of having relay cooperation, which comes at the expense of some loss in coding gain, whereas using a large encoding set size decreases the probability of having relay cooperation, but achieves some network coding gains. Therefore, there is a fundamental trade-off between the probability of relay cooperation and the achievable network coding gains. Using numerical results, we show that the proposed adaptive network coding achieves up to 5 dB gain at target outage 10-3as compared to conventional fixed network coding schemes.

Multiuser cooperative schemes usually rely on relay selection or channel selection to avoid deep fading and achieve diversity for all source nodes while maintaining acceptable spectral efficiency. This requires estimating the channel state information (CSI) and reporting it back to the resource allocator. Then the resource allocator needs to inform each node about its task. In some applications, it is impractical to collect the required CSI. For example, in underwater acoustic channels, the low speed of the acoustic wave (500 m/s) leads to a very long delay between the measured CSI and the reported CSI, making the provided CSI severely “outdated”. To remedy this, we propose a new distributed coding scheme to avoid deeply faded channels and achieve cooperative diversity for multiuser applications where CSI is not available. We consider a network consisting of multiple sources, multiple relays, and a single destination. The proposed scheme is based on a distributed implementation of linear block codes and achieves a better diversity-multiplexing tradeoff (DMT) compared to the competitive schemes. In this scheme, each relay node implements a column of the generator matrix of the code. Each relay receives the symbols from one or more source nodes and performs modulo-q addition on the decoded symbols and retransmits the result to the destination. Finally, soft decoding is employed at the destination to retrieve the transmitted information. We prove that the proposed scheme achieves diversity dminfor the end-to-end (E2E) performance, where dminis the minimum distance of the underlying code.

In non-cooperative multicell massive (or very large) multiple-input multiple-output (MIMO) systems, the pilot contamination effect in the uplink channel training and the intercell interference in the downlink severely degrade the overall network achievable rates. In this paper, we present a framework to mitigate those effects. Specifically, we propose in the uplink training a rank-q channel approximation method based on compressive sensing (CS) to estimate the most dominant singular subspaces of the global multicell MIMO channel matrix with a modest training length. Then, the estimate of the global channel information is used to design an intercell-interference-aware (IA) zero-forcing (ZF) multicell precoding method in the downlink to mitigate not only the intracell interference but also the intercell interference of the channel. The results obtained using the proposed scheme show a significant improvement in the achievable rates for all users in the cells, particularly the cell-edge users, as compared to the existing channel estimation and precoding methods.

We consider in this paper distributed beamforming for two-way cognitive radio networks in an effort to improve the spectrum efficiency and enhance the performance of the cognitive (secondary) system. In particular, we consider a spectrum sharing system where a set of decode-and-forward (DF) relays are employed to help a pair of secondary transceivers in the presence of multiple licensed (primary) users. Among the available relays, only those that receive the signals reliably participate in the beamforming process, where the optimal beamformer weights are obtained via a linear optimization method. We derive closed-form expression for the probability distribution function (PDF) of the total end-to-end signal-to-noise ratio (SNR) at the secondary transceiver. We also derive closed-form expressions for the outage and error probabilities over independent and identically distributed (i.i.d.) Rayleigh fading channels. Numerical results show the effect of beamforming in enhancing the secondary system performance in addition to mitigating the interference to the primary users.

We propose a new approach based on compressive sensing (CS) for the channel matrix estimation problem for “massive” (or large-scale) multiuser (MU) multiple-input multiple-output (MIMO) systems. The system model includes a base station (BS) equipped with a very large number of antennas communicating simultaneously with a large number of autonomous single-antenna user terminals (UTs), over a realistic physical channel with finite scattering model. Based on the idea that the degree of freedom of the channel matrix is smaller than its large number of free parameters, a low-rank matrix approximation based on CS is proposed and solved via a quadratic semidefine programming (SDP). Our analysis and experimental results suggest that the proposed method outperforms the existing ones in terms of estimation error performance or training transmit power, without requiring any knowledge about the statistical distribution or physical parameters of the propagation channel.

This paper presents an optimization framework for a wireless sensor network whereby, in a given route, the optimal relay selection and power allocation are performed subject to signal-to-noise ratio constraints. The proposed approach determines whether a direct transmission is preferred for a given configuration of nodes, or a cooperative transmission. In the latter case, for each node, data transmission to the destination node is performed in two consecutive phases: broadcasting and relaying. The proposed strategy provides the best set of relays, the optimal broadcasting power and the optimal power values for the cooperative transmission phase. Once the minimum-energy transmission policy is obtained, the optimal routes from every node to a sink node are built-up using cooperative transmission blocks. We also present a low-complexity implementation approach of the proposed framework and provide an explicit solution to the optimization problem at hand by invoking the theory of multi-parametric programming. This technique provides the optimal solution as a function of measurable parameters in an off-line manner, and hence the on-line computational tasks are reduced to finding the parameters and evaluating simple functions. The proposed efficient approach has many potential applications in real-world problems and, to the best of the authors' knowledge, it has not been applied to communication problems before. Simulations are presented to demonstrate the efficacy of the approach.

We address in this paper the challenge of coping with asymmetric data rates in two-way relay channels. We consider a relay network comprising two sources and one relay. The sources communicate at different rates through the relay. That is, we assume that one source uses M1-QAM (quadrature amplitude modulation) and the other uses M1/M2-QAM hierarchical modulation where M1≠ M2. For the underlying network, we consider two decode-and-forward (DF) relaying schemes. One scheme combines hierarchical zero padding and network coding (HZPNC) at the relay. The novelty of this scheme lies in the way the two signals (that have different lengths) are network-coded at the relay. The other scheme is referred to as opportunistic user selection (OUS) where the user with a better end-to-end channel quality is given priority for transmission. We analyze both schemes where we derive closedform expressions for the end-to-end (E2E) bit error rate (BER). Since the two schemes offer a trade-off between performance and throughput, we analyze and compare both schemes in terms of channel access probability and average throughput. We show that HZPNC offers better throughput and fairness for both users, whereas OUS offers better performance. We also compare the performance of HZPNC with existing schemes including the original zero padding, nesting constellation modulation and superposition modulation. We show through examples the superiority of the proposed HZPNC scheme in terms of performance and/or reduced complexity.

This paper presents an in-depth investigation on the feasibility of dynamic spectrum access (DSA) in vehicular environments. We present a comprehensive description of the DSA-based vehicle-to-infrastructure (V2I) communication as it takes place in the context of a scenario where spectral resources are limited. Founded on top of this description is a queueing model whose primary objectives are to capture and characterize the dynamics of this type of communication system and assess its performance in terms of several classical metrics. Simplicity and tractability distinguish the proposed model herein from existing models in the literature. Extensive simulations and numerical analysis are conducted for the purpose of validating the proposed model, evaluating the performance of DSA-based communication, and highlighting its limitations.

In this paper, we use a distributed beamforming method in cognitive radio relay networks in an effort to enhance the spectrum efficiency and improve the performance of the cognitive (secondary) system. In particular, we consider a spectrum sharing system where a set of potential relays are employed to help a pair of secondary users in the presence of a licensed (primary) user. A selection relaying scenario in an amplify and forward (AF) scheme is investigated. In this context, we obtain the exact expressions for the cumulative distribution function (CDF) and the moment generating function (MGF) of the equivalent end-to-end SNR at the secondary destination. Then, to analyze the performance, we derive closed-form expressions for the outage probability and bit error rate (BER) over independent and identically distributed (i.i.d.) Rayleigh fading channels. Numerical results demonstrate the efficacy of beamforming in improving the secondary system performance in addition to limiting the interference to the primary users.

We investigate the joint problem of relay selection and optimal sharing of relay power in wireless cellular networks with multicast traffic. We use two different performance metrics to maximize the network performance. We first present a mixed Boolean-convex optimization model to maximize the overall network capacity and solve this combinatorial problem optimally using branch and bound technique. We then show that obtaining the optimal solution is computationally not feasible for large network sizes and, unlike the case of unicast traffic, a water filling method does not yield near optimal solutions in multicast scenarios. We thus adopt an algorithm based on sequential fixing which substantially reduces the computation time and achieves near optimal solutions. In addition, we present a mixed integer linear programming model to maximize the capacity of the minimum capacity link and show that the model is very efficient to reach the optimal solutions.

We consider a dual-hop cooperative spectrum-sharing system with multiple relays that are available to opportunistically help the secondary communication system. In this primary/secondary cooperative system, we propose using a reactive relay selection (RRS) technique at the secondary system in which the best relay is chosen based on both the first and second hops transmission conditions. In fact, the best relay is selected as the relay node that not only satisfies a minimum required rate on the first-hop transmission, but can also achieve the highest signal-to-noise ratio (SNR) at the destination node. In this context, we first derive an expression for the cumulative distribution function (CDF) of the received SNR at the secondary destination node while assuming that the secondary transmission is limited by the appropriate interference constraints. Then, we use this CDF expression to obtain a closed-form expression for the end-to-end outage probability of the proposed cooperative system. Finally, illustrative numerical examples are shown and the benefits of using the proposed RRS technique in different channel propagation conditions are discussed.

In this paper, we address the relay assignment in cooperative networks based on order statistics. We consider a network comprising a cluster of transmitting sources, a cluster of relay nodes and a single destination. This is motivated by the fact that the existing relay assignment schemes don't achieve diversity for the network configuration under consideration. We assume that the cooperation is in the amplify-and-forward (AF) mode, but proposed scheme is applicable to the decode-and-forward (DF) mode. In the relay assignment process, the source nodes that have weaker source-destination links, have higher priority in relay selection. We analyze the probability density function (PDF) and the average bit error rate (BER) for the proposed scheme where we invoke important results that we first derive for simple two-hop networks. Specifically, we calculate the exact expression for the PDF of the end-to-end signal-to-noise ratio (SNR). Compared to other relay assignment schemes (like maximizing sum of SNR values or maximizing the minimum selected SNR), the proposed scheme has the advantages of simplicity, higher diversity order and fairness.

In this paper, we address the problem of joint optimal threshold-based relaying and maximum likelihood (ML) detection in network-coded cooperative systems. The purpose of using threshold-based relaying is to circumvent the impact of error propagation, which could lead to degrading the system diversity. For simplicity, we consider a simple network comprising two source nodes and one relay node. The relay operates in the decode-and-forward (DF) mode and employs binary network coding. The communication between the two source nodes is bidirectional, resulting in a two-way relay channel. We assume binary phase shift keying (BPSK) signaling. For a given log-likelihood ratio (LLR)-based threshold used at the relay, we derive the ML detector at the destination assuming that the destination has full knowledge of the locations of the blocked bits at the relay. We then derive an expression for the corresponding end-to-end (E2E) bit error rate (BER) performance, which is used to find the optimal threshold. We also derive two practical discontinuous transmission detectors at the destination for the purpose of identifying whether the relay is forwarding or not. The performance of one of the detectors is similar to that when the destination knows the locations of the blocked bits, whereas the performance of the other detector suffers some performance degradation. We present several numerical examples that illustrate the efficacy of the proposed scheme as compared to existing schemes.

We study three relay selection schemes for uniformly distributed wireless sensor networks: 1) optimal selection where the relays that maximize the signal-to-noise ratio (SNR) at the destination are selected, 2) geometry-based, which is based on selecting the closest nodes to the source, and 3) random selection in which the nodes are selected randomly from a certain neighborhood of the source. In all schemes, we assume that all relays operate in the amplify-and-forward mode and transmit with equal average powers and each relay has only access to its backward channel and location. For each relay selection strategy, we propose a decentralized protocol whereby proper nodes choose to act as relays without requiring any central coordinating entity or any inter-node information transfer. We derive expressions for the average SNR at the relays and destination while assuming that the source-node distances and the inter-terminal channel links are completely random. We show that, for all proposed schemes, the SNR variance at the destination converges to zero as the number of relays increases. While each selection scheme has its pros and cons, we derive a sufficient condition under which the average SNR at the destination becomes independent of the selection scheme employed.

In this paper, we consider two relay assignment schemes for cooperative networks comprising multiple source-destination pairs. Both schemes are based on the max-min criterion and aim at achieving the maximum spatial diversity for all pairs. One scheme is used as a performance benchmark since it considers all possible relay assignment permutations and selects the best one. The other scheme, on the other hand, considers only a subset of those permutations and selects the best one. The advantages of the latter one is that it reduces the complexity of the assignment process, in addition to making the performance analysis tractable. We examine these schemes over asymmetric channels using M-ary phase shift keying signaling. We consider both amplify-and-forward (AF) and decode-and-forward (DF) relaying where we derive expressions for the end-to-end (E2E) symbol error rate (SER). In both cases, we show that the full spatial diversity is achieved. To account for error propagation in DF relaying, we adopt a threshold-based relaying scheme whereby the relays forward only bits that are deemed reliable and remain silent otherwise. We compare this scheme to Genie-aided relaying where the relays forward only correctly decoded bits. We analyze these two schemes and derive expressions for the E2E SER performance. We present several numerical examples that validate the analytical results.

Cognitive radio (CR) has been recently proposed as a promising technology to improve the spectrum utilization. In this paper, we consider the spectrum sharing between CR users and a licensed primary user (PU) in order to enhance the spectrum efficiency. In the considered scenario, the PU is located at a far position from the primary base station (PBS) which makes it difficult to obtain a successful transmission. In this case, the cognitive base station (CBS) at the vicinity of the primary network, offers its help to transmit the primary data. As a reward, the CR system will be able to share the spectrum with the primary system. With the deployment of multiple antennas at the CBS, quality-of-service (QoS)-aware spectrum underlay CR network is considered. In particular, we formulate a "fair'' opportunistic spectrum sharing approach that determines the optimal beamforming weights in order to maximize the overall minimum throughput of the CRs while guaranteeing the QoS of the PU. The original optimization problem is non-convex and does not have any closed-form solution. However, using a convex optimization approach, we transform it to a convex form and find an approximate solution using standard numerical methods. Simulation results will demonstrate the effectiveness of the proposed approach.

We consider a multipoint-to-multipoint cognitive network wherein the communications from L sources to their designated destinations are carried out through the use of K relays in a dual-hop amplify-and-forward cooperative scheme. Aiming to maximize the worst signal-to-interference-plus-noise ratio of the L destinations, we develop a technique that jointly optimizes the sources' transmit powers and the relays' beamforming weights while satisfying the sources and the relays total transmit power constraints as well as the sources individual power constraints and further guaranteeing that the interference powers inflicted from the cognitive network on the M existing primary users are below acceptable thresholds.

This paper deals with relay assignment in cooperative networks based on the max-min criterion. A simple algorithm is proposed in order to find the optimum relay assignment permutation to avoid brute-force prohibitive search method. It is assumed that there are N source-destination pairs and M relays in the network. The problem is how to assign each relay to a source-destination pair in order to achieve the highest diversity order. The proposed algorithm achieves the maximum spatial diversity for all of the nodes and leads us to simplify the analysis and simulation of the optimal answer. It is shown that the Probability Density Function (PDF) of the end-to-end signal-to-noise ratio (SNR) after relay assignment can be expressed as a weighted sum of the order statistics of the PDF of individual end-to-end links. Since the analytical calculation of the weighting coefficients in the mentioned weighting sum becomes difficult, we propose an approximation in order to calculate the mentioned weighting coefficients. The validity of this fit is shown through some simulations.

We consider in this paper relay assignment for cooperative systems with multiple two-way relay channels. The nodes corresponding to one two-way relay channel (henceforth referred to as pair) communicate with each other through a relay. The relays use network coding to simultaneously transmit the signals corresponding to the pairs they are assigned to. We propose two relay assignment schemes. One scheme considers all possible relay assignment permutations and selects the one that yields the best performance, and the other one considers only a subset of these permutations and selects the best one. The advantage of the latter is that it results in a significant reduction in computational complexity, in addition to making the analysis more tractable. We analyze the performance of these schemes over asymmetric independent Rayleigh fading channels. We also consider semi-symmetric and symmetric channels as special cases. We derive closed-form expressions for the end-to-end bit error rate performance for all scenarios and show that the full diversity order is achieved, which is the number of available relays. We present several examples to verify the theoretical results.

In this paper, we propose a cooperative beamforming (CB) technique for dual-hop amplify-and-forward communication in WSNs with one source, N - 1 interferences, and K relay nodes. First, an optimal beamformer is obtained that maximizes the signal-to-interference-plus-noise ratio at the intended receiver in the far-field subject to the following constraints: 1) The relay nodes total transmit power is bounded; and 2) the received power from the N transmitters at L - 1 unintended receivers in the far-field is zero. It is shown that the optimal beamformer can only be implemented if each relay node knows the locations and the backward channels of all other relay nodes in the network. As this knowledge is not typically available at nodes in WSNs, we use a technique to approximate the optimal beamforming coefficients with quantities that depend only on the locally-available information at each individual relay node. The average beampattern expression of the proposed CB technique is then derived and its properties are analyzed. In particular, it is shown that the average gain of the beamformer linearly increases with K in the direction of the intended receiver while remaining fixed in the directions of the unintended receivers.

In this paper, we analyze the impact of carrier frequency offset (CFO) on the performance of orthogonal frequency division multiplexing (OFDM) transmission employing space-frequency coding over relay channels. The challenge in such systems lies in the difficulty of canceling the interference resulting from the different CFOs that correspond to the relays involved in the transmission. We first analyze the CFO correction schemes and examine their impact on the achievable information rates. Further, we analyze the interference cancellation (IC) technique based on the so-called turbo-principle, that is, which jointly detects and decodes the received data. The increase of the rates achievable thanks to IC is assessed via parametric description of the iterative process. We provide examples that demonstrate the efficacy of the proposed scheme and numerical results are contrasted with theoretical performance limits.

We consider a multi-relay amplify-and-forward cooperative communication scheme in wireless sensor networks with uniformly distributed nodes. Fixing the average total transmission power from the network and preserving fairness among the selected relays by constraining them to transmit with equal average powers, we aim to improve the signal reception quality at the far-field receiver by means of a proper choice of the relays. Assuming that the nodes' forward channels are not known, the following three relay selection schemes are proposed and their performances are analyzed. 1) Optimal relay selection scheme that maximizes the average SNR at the receiver by exploiting K nodes with the highest SNRs as relays; 2) geometry-based relay selection scheme that is energy-efficient and achieves a close-to-optimal average SNR performance at the receiver by using K closest nodes to the source as relays; and 3) random relay selection scheme that is energy-efficient and further guarantees a fair usage of all nodes by randomly selecting K relays from a specific area around the source. By minimizing an outage probability, a strategy to determine this area is also proposed. Finally, it is shown for all relay selection schemes that the SNR variance at the receiver converges to zero as K increases.

One of the main challenges in cognitive radio (CR) communications lies in the system robustness to uncertainties. In this paper, we examine the impact of the noise power uncertainty on the performance of various detectors in CR networks. We consider both single and multiple CR nodes. For the single CR case, we compare the performance of the energy and likelihood ratio test (LRT) detectors in the presence of noise uncertainties. It is shown that both detectors perform about the same. We shall also investigate cooperative spectrum sensing based on soft-information combining, where the cooperating CR nodes experience different noise power uncertainties. We then propose a simple detection scheme that is more robust to noise variations and uncertainties than the conventional detection schemes. Numerical results are presented to verify the theory and demonstrate the robustness improvement of the proposed detection scheme.

A network with L single-antenna source-destination pairs is considered that uses a half-duplex K-antenna relay to establish the end-to-end links in a dual-hop cooperative communication scheme. The sources share a common channel to concurrently transmit their signals and the relay multiplies its received signal vector with L relaying matrices and then forwards the resulting signal vectors to the destinations in dedicated channels. Under the relay's transmit power constraint at every channel as well as both sources' individual and total power constraints, the jointly optimal sources' powers and the relaying matrices are obtained that maximize the minimum of the normalized signal-to-interference-plus-noise ratios at the destinations. Numerical simulations are then used to verify the analytical results.

In this paper, the rate achievable in Multiple-Input-Multiple-Output (MIMO) systems with iterative receivers based on linear front-end (FE) processing is investigated. First, the communication with Gaussian input signal is assumed and the Extrinsic Information Transfer (EXIT) chart is applied to evaluate the achievable sum rate. Then, the method for deriving the EXIT chart for a more practical case has been introduced. As a specific model, which fits the real turbo receivers better, communication with large size uniform constellations is discussed where the information being exchanged between the receiver's iterative block are Log-Likelihood-Ratios (LLRs) of the transmitted bits. It is shown that, in this situation, the iterative process does not improve the performance from achievable sum rate point of view in both high and low SNR regimes. However, the iterative process is shown to help in the medium SNR range, which is the range of interest.

During the last few years, several authors have studied the performance of many different types of channel codes for cooperative communication systems. Some of these codes include Low Density Parity Check (LDPC), Turbo and convolutional codes. Published results show that these codes effectively improve both the data rate and system performance in cooperative schemes such as the decode and forward (DF). Reed-Solomon (RS) codes are one of the most widely used channel codes in wireless communication systems today. These codes have also been adopted by several 3G standards including several digital video broadcasting (DVB) standards and also by worldwide interoperability for microwave access (WiMAX) which is based on the IEEE 802.16 standard. However, published literature shows no specific study to investigate their performance for the cooperative communication environment. Using the component codes adopted in the IEEE 802.16 standard, this paper investigates the performance of the concatenated RS coding schemes for cooperative communication. This paper first analyses the standard RS-coded DF cooperative system. The results obtained for the standard RS-coded scheme show huge gains over the non-cooperative system. Some of the problems of the standard DF relay protocol is then discussed and an improved RS-coded DF cooperative scheme is proposed. The performance of the proposed scheme shows significant gains over the standard RS-coded DF system.

We consider in this paper relay assignment for cooperative systems with multiple two-way relay channels. The nodes corresponding to one two-way relay channel (henceforth referred to as pair) communicate with each other through a relay. The relays use network coding to simultaneously transmit the signals corresponding to the pairs they are assigned to. We propose two relay assignment schemes. One scheme considers all possible relay assignment permutations and selects the one yields the best performance, and the other one considers only a subset of these permutations and selects the best one. The advantage of the latter is that it results in a significant reduction in computational complexity, in addition to making the analysis more tractable. We consider the cases when a relay is assigned to a single pair and the case when a relay is assigned (simultaneously) to multiple pairs. To achieve the latter, we use higher order modulation schemes at the relay nodes. We analyze the performance of these schemes over symmetric and asymmetric independent Rayleigh fading channels, and derive closed-form expressions for the end-to-end bit error rate performance. We present several examples to verify the theoretical results.

A collaborative receive beamforming technique is proposed that lends itself to a distributed implementation in wireless sensor networks (WSNs). In the first time slot, the transmitter of interest along with several interfering terminals send their signals while in the second time slot K nodes multiply their received signals with properly selected beamforming weights and forward the resulting signals to the receiver. The beamforming weights are selected such that they depend only on the locally-available knowledge at the relaying nodes while aiming to maximize the signal-to-interference-plus-noise ratio (SINR) at the receiver. The beampattern expression is obtained and its analytical properties are studied.

In this paper, we study a special type of quasi-cyclic (QC) codes called skew QC codes. This set of codes is constructed using a noncommutative ring called the skew polynomial ringF[x;¿]. After a brief description of the skew polynomial ringF[x;¿], it is shown that skew QC codes are left submodules of the ringRsl=(F[x;¿]/(xs-1) )l. The notions of generator and parity-check polynomials are given. We also introduce the notion of similar polynomials in the ringF[x;¿] and show that parity-check polynomials for skew QC codes are unique up to similarity. Our search results lead to the construction of several new codes with Hamming distances exceeding the Hamming distances of the previously best known linear codes with comparable parameters.

In relay networks, error propagation at the relay nodes degrades the performance of the system. To combat that effect, it has been suggested to implement a reliability threshold at the relay to control error propagation. Specifically, the relay calculates log-likelihood ratio (LLR) values for the bits sent from the source. These values are subjected to a threshold to selectively forward bits that are most reliable and discard bits that are less so, resulting in less errors propagating to the destination. We investigate the application of this technique to a network-coded two-way relay channel where the relay is assisting two sources simultaneously. We first consider network-coded systems without channel coding, and then consider network-channel coded systems. We examine two modes of thresholding, one based on the individual bits, and the other based on the combined bits. We provide the full analysis for the bit-error rate (BER) performance of both thresholding modes and optimize the thresholds accordingly. We demonstrate that the optimum thresholds based on both modes give similar performances and are far better than the case of no thresholding. We also consider the performance of the proposed thresholding techniques for network-channel coded systems. We present several numerical examples that illustrate the efficacy of employing thresholding at the relay nodes (for networks with and without channel coding).

Null-steering transmit beamformers aim to maximize the received signal power at the direction of the access point (AP) while inflicting minimal interfering effect on unintended receivers. However, existing null-steering beamformers may not be directly applied to wireless sensor networks (WSNs) as they require every node to be aware of the locations of other nodes in the network. In this paper, a novel null-steering beamformer is introduced that can be implemented in uniformly distributed WSNs in which each node is oblivious of other nodes' locations. The average beampattern expression of the proposed beamformer is derived and its properties are analytically studied. In particular, it is shown that the average beampattern of the proposed beamformer is similar to that of the conventional beamformer in directions with far angular distance from any unintended receiver. The analytical results are also adopted to design a null-steering beamformer with a single null and required average beampattern properties. The optimal null position is obtained which minimizes the maximum sidelobe of the average beampattern while having negligible deteriorating effect on the received power at the AP. Finally, computer simulations are used to validate the analytical results as well as to confirm the effectiveness of the design approach.

Due to the unsupervised nature of wireless sensor networks (WSNs), intensive communications are required among the selected nodes to reach a consensus and synchronize prior to entering a distributed beamforming (DBF) procedure. Therefore, a sensible approach to select the nodes should not only take into account the required beampattern, but also should aim to preserve the inter-node connectivity and the network energy. We show for a uniformly distributed WSN that when the nodes are selected from a ring of proper radii, the resulting beampattern mainlobe is narrower compared to that of the classical DBF technique proposed in [1]. At the same time, our proposed technique may preserve a substantial amount of network energy and reduce the probability of network disconnectivity. Directivity of the proposed DBF technique is analyzed and an extension of the technique to a multi-ring case is presented. It is shown that the sidelobe peaks can be considerably decreased if the nodes are selected from multiple concentric rings.

In cooperative communications, error propagation at relays degrades the diversity order of the system. To combat that effect, it has been suggested to implement a reliability threshold at the relay to control error propagation. The relay calculates log-likelihood ratio (LLR) values for the bits sent from the source. These values are subjected to a threshold to selectively forward bits that are most reliable and discard bits that are less so, resulting in less errors propagating to the destination. We investigate the application of this technique to a network- coded two-way relay channel, where the relay is assisting two sources simultaneously. We investigate two modes of thresholding at the relay: at the individual-bit level and at the combined- bit level. We analyze the bit-error rates of both thresholding modes and optimize the threshold for both. We show significant gains using thresholding over an unthresholded network-coded system. Based on system simulations, we conclude that utilizing separate thresholds yields better results than utilizing a combined threshold scheme.

The performance of a cooperative multiuser direct-sequence code-division multiple-access (DS-CDMA) system is analyzed in the asymptotic regime where both the spreading codes and the number of users grow large with the same ratio. A simple signal-to-interference-plus-noise ratio (SINR) expression is derived that is independent from the spreading codes and explicitly accounts for the effects of the multiple-access interference (MAI) and the relay noise. The so-obtained SINR expression is then computed based entirely on the available local information. The results obtained above are then used to optimally design the cooperative system. In particular, it is shown how the amount of cooperation between each collaborating pair can be adjusted to simultaneously achieve a pre-assigned target SINR for both users. Based on the local information, the globally optimal amount of the relay power is also obtained that maximizes the achieved SINR at the access point.

This paper proposes two blind carrier frequency offset (CFO) estimation schemes for differentially modulated orthogonal frequency division multiplexing (OFDM) systems. The proposed schemes estimate the fractional part of the CFO with only two consecutive OFDM blocks, and they exploit two implicit properties associated with differentially modulated OFDM (DOFDM) systems, i.e., the channel keeps constant over two consecutive OFDM blocks, and the DOFDM systems employ an M-ary phase-shift keying constellation. One of the schemes is based on the finite alphabet (FA) constraint and the other one is based on the constant modulus (CM) constraint. They provide a trade-off between the performance and computational complexity. Furthermore, the FA based scheme can achieve better performance at high signal-to-noise ratios at the expense of some additional computational complexity as compared to the existing CM based subspace scheme. The constrained Cramer-Rao lower bound is also derived. Several numerical examples are presented to validate the efficacy of the proposed schemes.

The relay selection problem in a large wireless sensor network (WSN) with uniformly distributed identical nodes is investigated for a two-phase cooperative protocol where the signal transmitted from a single source is overheard by the network and is then relayed by multiple selected nodes subject to a total average transmission power. First, a relay selection technique is considered that maximizes the average signal-to-noise ratio (SNR) at the access point (AC) while is applicable to a distributed WSN where only a limited information is available to the nodes. As this technique is shown to be energy inefficient, two other alternative relay selection techniques are also presented that substantially reduce the energy consumption of the network. One of these alternative techniques takes advantage of the static nature of the network topology and, as verified by simulations, achieves near-optimal SNR performance, while the other one randomly selects the relays from a neighborhood around the source and further guarantees a fair power consumption among the nodes at the cost of possible SNR performance drop at AC. Accounting for the randomness of the nodes locations as well as the inter-terminal fading channel coefficients, the SNR performances of the energy efficient relay selection schemes are analyzed and a condition is derived under which the average SNR at AC is independent from the technique used to select the relaying nodes. This condition clarifies when the energy efficient and fair random relay selection scheme may be used without compromising the quality of the received signal at AC.

In this paper, we consider antenna/relay selection for coded cooperative networks in an effort to improve their end-to-end performance by improving the detection reliability at the relay nodes. Considering amplify-and-forward (AF) relaying, we analyze a previously proposed distributed coded cooperation scheme in conjunction with antenna/relay selection. Specifically, we derive upper bounded expressions for the bit error rate assuming M-ary phase shift keying (M-PSK) transmission. Our analytical results show that the maximum diversity order of the system is maintained for the entire range of symbol error rate of interest, unlike the case without antenna/relay selection. Several numerical and simulation results are presented to demonstrate the efficacy of the proposed scheme.

In this paper, we propose a joint carrier frequency offset (CFO) and channel estimation method for the uplink channel of orthogonal frequency division multiple access (OFDMA) systems. The proposed method includes two steps. In the first step, the variable projection (VP) method based on the least squares (LS) criterion is used for CFO estimation. In each iteration of the VP method, the CFOs and channel coefficients are updated separately, which distinguishes the VP method from an existing non-separated minimum mean square error (MMSE) method in which the CFOs and channel coefficients are treated together as a whole. In the second step, the channel is estimated by employing the robust MMSE method. The simulation results show that, compared with the non-separated MMSE method, the proposed method has faster convergence rate in terms of CFO estimation and achieves better channel estimation performance in most of the cases. The computational complexity of the proposed scheme is also lower than that of the non-separated MMSE method for large number of users at high signal-to-noise ratios.

In this paper, we introduce a novel approach to interference cancellation (IC) for code division multiple access (CDMA) uplink transmission. Several models combining principles of serial (SIC) and parallel (PIC) interference cancellation are discussed. The proposed scheme is derived from the analysis of these hybrid models and applies a user configuration algorithm (termed ldquotricklerdquo) in order to provide improved bit-error-rate (BER) performance. The algorithm utilizes an adaptive matrix to compute the required configuration to be used for the subsequent interference cancellation stage. We demonstrate that significant performance improvements can be achieved over various hybrid schemes. A reduced-complexity version of the trickle algorithm is also introduced where the processing delay is greatly reduced while maintaining similar performance. We present several numerical examples through which we demonstrate the efficacy of the proposed algorithms relative to existing interference cancellation algorithms.

In this paper, we present space shift keying (SSK) as a new modulation scheme, which is based on spatial modulation (SM) concepts. Fading is exploited for multiple-input multiple-output(MIMO) channels to provide better performance over conventional amplitude/phase modulation (APM) techniques. In SSK, it is the antenna index used during transmission that relays information, rather than the transmitted symbols themselves. This absence of symbol information eliminates the transceiver elements necessary for APM transmission and detection (such as coherent detectors). As well, the simplicity involved in modulation reduces the detection complexity compared to that of SM, while achieving almost identical performance gains. Throughout the paper, we illustrate SSK's strength by studying its interaction with the fading channel. We obtain tight upper bounds on bit error probability, and discuss SSK's performance under some non-ideal channel conditions (estimation error and spatial correlation). Analytical and simulation results show performance gains over APM systems (3 dB at a bit error rate of 10-5), making SSK an interesting candidate for future wireless applications. We then extend SSK concepts to incorporate channel coding, where in particular, we consider a bit interleaved coded modulation (BICM) system using iterative decoding for both convolutional and turbo codes. Capacity results are derived, and improvements over APM are illustrated (up to 1 bits/s/Hz), with performance gains of up to 5 dB.

This paper proposes two blind carrier frequency offset (CFO) estimation schemes for differentially modulated orthogonal frequency division multiplexing (OFDM) systems. The proposed schemes estimate the fractional part of the CFO with only two consecutive OFDM blocks, and they exploit two implicit properties associated with differentially modulated OFDM (DOFDM) systems, i.e., the channel keeps constant over two consecutive OFDM blocks, and the DOFDM systems employ an M-ary phase-shift keying constellation. One of the schemes is based on the finite alphabet (FA) constraint and the other one is based on the constant modulus (CM) constraint. They provide a trade-off between the performance and computational complexity. The constrained Cramer-Rao lower bound is also derived. Several numerical examples are presented to validate the efficacy of the proposed schemes.

In this paper, we consider distributed coding for wireless cooperative networks with antenna/relay selection. The aim of this of work is to find ways to improve the reliability of the source-relay link in an effort to maintain the diversity order available in the system. To this end, we propose to use antenna selection at the relay node whereby the antenna with the best instantaneous received signal to noise ratio is selected. This assumes that the relay node is equipped with multiple antennas, but only one radio frequency (RF) chain is employed. The concept of antenna selection can be extended to relay selection. That is, among the available relay nodes, the one with the best source-relay link reliability is selected. Assuming decode-and-forward (DF) relaying, we analyze the antenna/relay selection in conjunction with a previously proposed distributed coded cooperation scheme based on convolutional codes. Specifically, we derive an upper bounded expression for the symbol error rate assuming M-ary phase shift keying (M-PSK) transmission. Our analytical results show that the maximum diversity order of the system is maintained for the entire range of bit error rate of interest, unlike the case without antenna selection. Several numerical and simulation results are presented to demonstrate the efficiency of the proposed scheme.

We introduce coded cooperation diversity using relay nodes in the decode-and-forward (DF) mode, in which the codewords of the source node are partitioned and transmitted through independent fading channels to both relay and destination nodes, to achieve remarkable gains over a noncooperative system. The main difference between the distributed and the noncooperative space-time coding is that the link between the source and the relay node is not error-free in the distributed case, as opposed to the noncooperative case. In this work, we take into account the errors in the source-relay link and derive upper bounded expression for the symbol error rate on DF relaying in the case of M-ary phase shift keying (M-PSK) transmission. Our analytical results show that the maximum diversity order is achieved provided that the source-relay link is more reliable than the other links. Otherwise, the diversity degrades. This is unlike the case when the relay nodes operating in the DF mode are based on symbol-by-symbol decoding and forwarding. We present several numerical examples to support our analytical expression.

This paper proposes an extended Kalman filter (EKF)-based and training symbol aided carrier frequency offset (CFO) estimation scheme for the uplink OFDM systems. Typically, in EKF-based estimation scheme, the measurement equation is a function of the CFO and channel coefficients. Therefore, a vector EKF has to be employed to estimate all the unknowns, which may sometime result in convergence problems. To avoid these problems, the proposed scheme uses a scalar EKF. The user signals are first separated by using multiple-access interference cancellation. Then, the unknown channel coefficients in the measurement equation are replaced with a non-linear function of the CFO so that the scalar EKF can be employed. The observation noise power is analyzed and its approximation is used in the EKF algorithm. Several numerical examples are presented to validate the efficacy of the proposed scheme. It is shown that the proposed scheme can achieve the Cramer-Rao lower bound (CRLB) when the number of users is small, whereas it degrades when the number of users increases. We also compare its computational complexity with several existing schemes.

A fundamental component of spatial modulation (SM), termed generalized space shift keying (GSSK), is presented. GSSK modulation inherently exploits fading in wireless communication to provide better performance over conventional amplitude/phase modulation (APM) techniques. In GSSK, only the antenna indices, and not the symbols themselves (as in the case of SM and APM), relay information. We exploit GSSKpsilas degrees of freedom to achieve better performance, which is done by formulating its constellation in an optimal manner. To support our results, we also derive upper bounds on GSSKpsilas bit error probability, where the source of GSSKpsilas strength is made clear. Analytical and simulation results show performance gains (1.5-3 dB) over popular multiple antenna APM systems (including Bell Laboratories layered space time (BLAST) and maximum ratio combining (MRC) schemes), making GSSK an excellent candidate for future wireless applications.

In this paper, we propose a joint carrier frequency offset (CFO) and channel estimation scheme based on the well known variable projection (VP) method for the uplink channel of orthogonal frequency division multiplexing (OFDM) systems. The CFOs and channel coefficients are estimated by minimizing the least-squares (LS) cost function through a numerical optimization technique. In each iteration, the CFOs and channel coefficients are updated separately, which distinguishes the VP method from an existing method in which the CFOs and channel coefficients are treated together as a whole. We demonstrate through several numerical examples the superiority of the proposed scheme in terms of convergence speed, computational complexity and estimation performance with regard to CFO estimation. However, the proposed scheme does not provide reliable estimation for the channel coefficients. We will investigate this problem further in our future research work.

This paper presents a new blind carrier frequency offset (CFO) estimation scheme for orthogonal frequency division multiplexing (OFDM) systems with constant modulus (CM) signaling. Both single-input single-output (SISO) systems and multiple-input multiple-output (MIMO) systems with orthogonal space-time block coding are considered. The proposed scheme is based on the reasonable assumption that the channel frequency response changes slowly in the frequency domain, which implies that the channel frequency response on two consecutive sub- carriers is approximately the same. Based on this assumption, cost functions are derived in closed-form, which minimize the difference between the signal power of two neighboring subcarriers. The identifiability of the proposed scheme is mathematically proved, which implies that minimizing the derived cost function gives an approximate estimate of the CFO. We demonstrate that the proposed scheme provides an excellent trade-off between complexity and performance as compared to prominent existing estimation schemes.

In this letter, we propose a structure-oriented multidirectional Wiener filter to reduce additive white Gaussian noise in image and video signals. A local activity profile based on second derivatives is used to restrict filtering to homogeneous directions to combat blurring. The proposed filter improves the Wiener estimate of denoised pixels to reduce the residual blurring of the conventional Wiener filter while achieving higher noise-reduction gains of up to 5.6 dB peak signal-to-noise-ratio (PSNR). The parameters of the proposed filter (block size, shape and coefficients) are adapted to image structure and noise level for optimization with respect to noise-reduction gain and structure preservation. The effectiveness of the proposed method is shown using both the PSNR and the modulation transfer function calculated for a range of spatial frequencies to measure the degradation in contrast due to blurring. Our results show that the proposed method achieves a higher contrast transfer ratio than the conventional Wiener filter indicating improved preservation of high frequency content. We also show the performance of the proposed filter relative to reference anisotropic diffusion and wavelet methods.

In this paper, we propose a new transceiver architecture for multiple-input multiple-output (MIMO) systems. The proposed scheme borrows ideas from the multilayered space-time coding (MLSTC) and threaded space-time coding (TSTC) schemes in an effort to maximize the diversity order while maintaining a low complexity detection. Specifically, the proposed scheme has a structure similar to that of the MLSTC while it employs a spatial interleaver (SI) in front of the MLSTC encoder. We also consider a low-complexity decoder that is based on the minimum mean squared error (MMSE) criterion and the QR decomposition (QRD). We provide analytical and simulation results through which we demonstrate that the proposed scheme provides improved diversity-multiplexing gain trade-off, as well as improved performance, all relative to the MLSTC scheme. In addition, with the proposed scheme, all layers achieve the same performance, which makes it attractive in multiuser environments.

In this paper, we investigate the performance of a coded cooperation diversity using relay nodes in the decode-and-forward (DF) mode, in which the codewords of the source node are partitioned and transmitted through independent fading channels to both the relay and destination nodes. The application of such a cooperative coding scheme is shown to offer remarkable gains over a noncooperative system. Unlike noncooperative space-time coding, in distributed cooperative coding, the link between the source and the relay node is not error-free. In this work, we take into account the errors in the source-relay link and derive the outage probability of DF relaying for binary phase shift keying (BPSK) transmission. Our analytical results show that the proposed scheme achieves full diversity. This is different from conventional relay systems where the relay nodes operating in the DF mode are based on symbol-by-symbol decoding and forwarding. Numerical results are shown to support our analytical expression.

This paper proposes a spatio-temporal technique for estimating the noise variance in noisy video signals, where the noise is assumed to be additive white Gaussian noise. The proposed technique utilizes domain-wise (spatial, temporal, and spatio-temporal) video information independently for improved reliability. It divides the video signal into cubes and measures their homogeneity using Laplacian of Gaussian based operators. Then, the variances of homogeneous cubes are selected to estimate the noise variance. A least median of squares robust estimator is used to reject outliers and produce domain-wise noise variance estimates which are adaptively integrated to obtain the final frame-wise estimate. The proposed technique estimates the noise variance reliably in video sequences with both low and high video activities (e.g., fast motion or high spatial structure) and it produces a maximum estimation error of 1.7-dB peak signal-to-noise ratio. The proposed method is fast when compared to referenced methods.

In conventional CDMA receivers, the detection of multipath components and RAKE finger management is normally based on the received signal energy per path. These schemes essentially overlook the interference component contaminating the total received power. Consequently, they exhibit poor multipath detection capability especially at low signal-to-interference-plus-noise ratio (SINR). In this paper, we present a new scheme for multipath detection that takes into consideration the interference level in each resolved path individually. Specifically, the proposed scheme is devised to estimate and cancel the interference per path before detection. To account for the hardware limitations of the receiver, we propose a low complexity version of the above scheme which can be easily incorporated into the receiver structure. Our results show that the proposed scheme provides significant improvements in the detection probability of multipath components over the energy-based schemes.

Taking into consideration the dominance of the multiple access interference (MAI) in spread spectrum based CDMA systems, power control plays a vital role in reducing the system interference and helps in increasing the capacity of the system while maintaining the quality of the received signal. Closed loop power control is mainly used to mitigate the near-far effect in the uplink direction. In this paper, we propose a bit error rate (BER)-based first order distributed power control algorithm. Being distributed, the proposed algorithm has the flexibility of using the benefits of fast closed loop power control and thus can be used to update the transmit power of a single user at a time. Signal-to-interference ratio (SIR) based algorithms are in extensive use. But considering that the SIR is time variant, the BER would be a better quality of service (QoS) parameter because it can be easily measured using already known pilot bits. We analyze the convergence performance of the proposed algorithm and show that its convergence is always guaranteed. We demonstrate that a considerable improvement in the convergence speed along with the required average signal-to-noise ratio (SNR) is achieved as compared with other BER-based algorithms under comparison. We also show that the proposed algorithm performs well at different mobile speeds.

Noise can highly impact the performance of video processing algorithms. This paper proposes a new real-time spatio-temporal method for estimating the noise variance in video signals. The proposed algorithm selects 3D regions or cubes in the video signal with high intensity uniformity. The noise variance is estimated from the selected set of spatially, temporally and spatio-temporally intensity-uniform cubes using local variances calculated along homogeneous plains. The proposed algorithm works well for sequences with high structure and motion activity and outperforms other methods with a worst-case estimation error of 2 dB. It works well for highly noisy and non-noisy sequences

This paper proposes a method for the reduction of white Gaussian video noise. The method achieves a maximum gain of 5.6 dB and is capable of preserving image content. It adapts window size, weighting and behavior to both image content and noise level in order to optimize the filtering. It starts by detecting the intensity-homogeneous direction from 8 different candidates. A variant of the Wiener filter is then applied directionally. The filtering is performed along homogeneous areas and not across edges. For noisy images, the filtering is increased automatically by using a larger kernel size. The proposed filter achieves better image preservation by turning off gradually for less noisy images. It works well for both highly noisy and good-quality images

In this paper, we present a comprehensive analysis of the outage probability for multiple-input multiple-output (MIMO) systems over spatially correlated Rayleigh fading chan-nels. In our analysis, we assume that 1) the channel state information (CSI) is perfectly known at the receiver but not at the transmitter, 2) the spatial correlation is present at both ends of the wireless communications link, 3) the transmit and receive correlation matrices may or may not be full rank, and 4) the underlying channel is quasi-static fading. With these assumptions, we derive explicit bounds for the outage probability and show that the diversity order is simply the product of the rank of the transmit correlation matrix and the rank of the receive correlation matrix. We also derive an expression that accurately quantifies the degradation in the signal-to-noise ratio (SNR) due to the presence of correlation. We present several numerical examples that validate our analysis.

This paper studies the performance of the concatenation of an outer channel code with an orthogonal spacetime block code (STBC), where the outer code can be a convolutional code (CC) or a trellis-coded modulation (TCM) code. In particular, we derive upper bounds on the bit error rate (BER) for this concatenation scheme with receive antenna selection. In our analysis, we assume that 1) the receiver uses only L out of the available M receive antennas, where, typically, L < M, 2) the selected antennas are those that maximize the instantaneous received signal-to-noise ratio (SNR), 3) the channel state information is perfectly known at the receiver, 4) the underlying channel is fully interleaved, and 5) the underlying orthogonal STBC is full-rate. We derive an explicit upper bound on the BER for the above concatenation scheme for any N, M and L, where N denotes the number of transmit antennas. We show that the diversity order, with antenna selection, is the same as that of the full complexity system, whereas the deterioration in SNR is upper bounded by 10 log10 (M/L) dB. We also derive a tighter upper bound on the BER for the Alamouti scheme when the receiver uses the best antenna, i.e., L = 1. These upper bounds can be extended in a straightforward manner to other types of outer codes and fading channels, including fast, block and slow fading channels. Finally, we present simulation results that validate our analysis.

Power control has been an important issue in multiple access systems allowing more users to share the system resources. The capacity of the WCDMA system is limited by the total interference, called multiple access interference (MAI). An efficient power control algorithm is always required to improve the system performance. In this paper, we propose a distributed, first order power control algorithm. The proposed algorithm uses an exponential function with a modified form of the LMS algorithm in order to achieve a better speed of convergence. We examine the proposed algorithm for time varying multipath Rayleigh fading environment. We compare the performance of the proposed algorithm and the 3GPP standard algorithm. We demonstrate that the proposed algorithm achieves better convergence and saves on the signal-to-noise ratio (SNR) required to achieve a certain performance. We also demonstrate that the proposed algorithm works well for different mobile speeds with a continuously changing channel.

This paper examines the performance of space- time trellis codes (STTCs) over block Rayleigh fading channels with receive antenna selection. Antenna selection is performed based on maximizing the instantaneous received signal-to-noise ratio (SNR). We derive explicit upper bounds on the pairwise error probability and show that the resulting diversity order deteriorates with antenna selection and becomes a function of the number of selected antennas. We provide numerical examples and simulation results that validate these theoretical findings. We remark that the same result holds for fast fading channels, as well as for quasi-static fading channels when the underlying STTC is rank deficient. However, when the channel is quasi-static fading and the STTC is full rank, the diversity order is maintained with antenna selection. In contrast, when the underlying code is an orthogonal space-time block code (OSTBC), the diversity order is always maintained with antenna selection regardless of the type of fading involved. The same results hold when the OSTBC is concatenated with an outer channel code. These findings render STTCs when antenna selection is employed unattractive.

In this paper, we propose a novel and simple approach for dealing with the exaggerated extrinsic information produced by the soft-output Viterbi algorithm (SOVA). The proposed remedy is based on mathematical analysis and it involves using two attenuators, one applied to the immediate output of the SOVA and another applied to the extrinsic information before it is passed to the other decoder (assuming iterative decoding). The use of these attenuators aims at reducing the inherent strong correlation between the intrinsic information (input to the SOVA) and extrinsic information (output of the SOVA). We examine the modified SOVA (MSOVA) on additive white Gaussian noise (AWGN) and flat fading channels for parallel concatenated codes (PCCs) and serial concatenated codes (SCCs). We show that the MSOVA provides substantial performance improvements over both channels. For example, it provides improvements of about 0.8 to 1.0 dB at P/sub b/ = 10/sup -5/ in AWGN, and about 1.4 to 2.0 dB at P/sub b/ = 10/sup -5/ on fading channels. We also show that there are cases where the MSOVA is superior to the a posteriori probability (APP) algorithm. With this motivation, we extend the proposed modification to the APP algorithm with favorable results. We demonstrate that the modified APP (MAPP) provides performance improvements between 0.3 to 0.6 dB at P/sub b/ = 10/sup -5/ relative to the APP. We lastly mention that the proposed modifications, while they provide considerable performance improvements, keep the complexity of these decoders almost the same, which is remarkable.

In this paper, we study the performance of space-time trellis codes (STTCs) with receive antenna selection over fast fading channels. Specifically, we derive upper bounds on the pairwise-error probability (PEP) with antenna selection. In performing the selection, we adopt a criterion that is based on using L out of the available M receive antennas that result in maximizing the instantaneous signal-to-noise ratio (SNR) at the receiver, where L les M. We show that the diversity order resulting from antenna selection deteriorates significantly and is actually dictated by the number of selected antennas. The implication of this result is that adding more receive antennas, while maintaining the same number of selected ones, will have no impact on the diversity order, but it does, however, provide some additional coding gain. This is unlike the case for quasi-static fading channels in which the diversity order is always preserved with antenna selection when the underlying STTC is full-rank. We present numerical examples that support our analysis

Abstract In this paper, we present a semi‐analytical approach for analyzing the serial concatenation (SCC) of a high rate convolutional code and a precoded intersymbol interference (ISI) channel. The significance of the proposed approach is that it can be used to identify, for a given outer high rate code and fixed ISI channel, the precoder that results in the lowest error rate floor. In estimating the bit error performance in the floor region, we use analytical techniques developed for trellis codes to compute the overall system minimum squared Euclidean distance, and then use a semi‐analytical approach to find the corresponding multiplicity. We also demonstrate via simulations that the proposed technique may be extended to fading ISI channels with favorable results. We give examples that support our analysis. Copyright © 2006 John Wiley & Sons, Ltd.

In this paper, we present a comprehensive analysis of the outage probability for multiple-input multiple-output (MIMO) systems with receive antenna selection over spatially correlated fading channels. In our analysis, we assume that 1) the channel state information (CSI) is perfectly known at the receiver but not at the transmitter, 2) antenna selection is based on maximizing the channel capacity, 3) the spatial correlation is present at both ends of the wireless communications link, 4) the transmit and receive correlation matrices may or may not be full rank, and 5) the underlying channel is quasi-static fading. With these assumptions, we derive explicit bounds for the outage probability and show that the diversity order is the same as that of the full complexity system. We also derive an expression that quantifies the loss in signal-to-noise ratio (SNR) due to antenna selection We also present several numerical examples that validate our analysis.

This paper addresses the problem of multipath detection in CDMA systems. In conventional CDMA receivers, the detection of multipath components and RAKE finger management is normally based on the received signal energy per path. These energy-based schemes essentially overlook the interference component contaminating the total received power. Consequently, they exhibit poor detection capability especially at low signal-to-interference-plus-noise ratio (SINR). In this paper, we present a new scheme for multipath detection and RAKE finger assignment that takes into consideration the interference level in each resolved path individually. The proposed scheme utilizes information provided by the pseudo random code acquisition circuit to estimate the interference power per path. To account for the hardware limitations of the receiver, a low complexity version of the proposed scheme is designed and incorporated into the receiver structure. Analytical and simulation results show that the proposed scheme provides significant improvements in the detection probability of multipath components over the energy-based schemes. For instance, our results show that the proposed scheme can achieve the same detection probability of all multipath components as that of the energy-based scheme with a saving of at least 2 dB in E/sub b//N/sub 0/. In some cases, it is shown that the improvement can be as high as 3 dB.

This paper proposes a real-time method for the reduction of white Gaussian video noise. The method achieves a maximum gain of 4.8 dB and is capable of preserving image content. It adapts window size, weighting and behavior to both image content and noise level in order to optimize the filtering. It starts by detecting the intensity-homogeneous direction from 8 different candidates. A variant of the sigma filter is then applied directionally. The filtering is performed along homogeneous areas and not across edges. For noisy images, the filtering is increased automatically by using the two most homogeneous directions with a larger kernel size. The proposed filter achieves better image preservation by turning off gradually for less noisy images. It works well for both highly noisy and good-quality images without the introduction of speed or hardware implementation challenges.

The asymptotic behavior of the outage probability has been studied recently with the assumption that the receiver is equipped with a single antenna. In this paper, we extend this study to an arbitrary number of receive antennas. We use the Gaussian approximation for the distribution of the random mutual information to derive a threshold for the outage probability. We extend our results to the case when the receiver selects a subset of the available antennas where selection is based on maximizing capacity. We derive the pdf of the mutual information with antenna selection, and show that it can be approximated well with a Gaussian distribution. Based on this pdf, we derive a threshold for the outage probability with antenna selection. We also present several numerical examples that validate our analysis.

We propose a simple remedy for the exaggerated extrinsic information produced by the soft-output Viterbi algorithm (SOVA). We argue that what leads to these optimistic extrinsic values is the inherent strong correlation between the intrinsic information (input to the SOVA) and extrinsic information (output of the SOVA). The proposed remedy involves employing two attenuators, one applied to the immediate output of the SOVA and another applied to the extrinsic information before it is passed to the other decoder (assuming iterative decoding). We examine the modified SOVA (MSOVA) on additive white Gaussian noise (AWGN) and fading channels for serial concatenated codes (SCCs). We show that the MSOVA provides substantial performance improvements over both channels. For example, it provides improvements of about 0.8 to 1.0 dB at P/sub b/ = 10/sup -5/ in AWGN, and about 1.4 to 2.0 dB at P/sub b/ = 10/sup -5/ in fading channels, all relative to the SOVA. We extend the proposed modifications to the a posteriori probability (APP) algorithm with favorable results. We demonstrate that the modified APP (MAPP) provides performance improvements between 0.3 to 0.6 dB at P/sub b/ = 10/sup -5/ relative to the APP.

In this paper, we present a comprehensive performance analysis of combined convolutional coding and space-time block coding with receive antenna selection. In our analysis we assume that the receiver uses only L out of the available M receive antennas, where, typically, L /spl les/ M. The selected antennas are those that maximize the instantaneous received signal-to-noise ratio (SNR). We derive explicit upper bounds on the bit error rate (BER) performance of the above concatenation scheme for any N, M and L, where N denotes the number of transmit antennas. We show that the diversity order, with antenna selection, is maintained as the same as that of the full complexity system, whereas the deterioration in SNR is upper bounded by 10log/sub 10/(M/L) dB. We also derive two tighter upper bound on the BER for generalized orthogonal STBC and the Alamouti scheme when L = 1. Finally, we present simulation results that validate our analysis.

We deal with antenna selection at the receiver side for space-time coded systems over frequency-selective fading channels. We reveal that introducing antenna selection based on the signal-to-noise-ratio (SNR) observed can still achieve the full diversity available, if the underlying space-time code (STC) is full-rank (i.e., if it achieves full diversity without antenna selection over the frequency-selective fading channel). We also argue that if the code is not full-rank, antenna selection results in a loss in the diversity of the system.

In CDMA systems, assignment of the RAKE fingers to the correct multipath components is crucial for the receiver to combat fading and to take advantage of the multipath diversity. This is particularly important since the number of fingers available is normally limited in order to maintain low receiver complexity. In this paper, we introduce a new RAKE receiver finger assignment algorithm (FAA) based on estimates of the signal-to-interference ratio (SIR) per path, as opposed to signal strength in the conventional schemes. We also introduce a simple algorithm to produce these SIR estimates. A performance comparison between the proposed scheme and the already existing schemes is presented. We show that the proposed scheme provides a significant performance improvement relative to that of the conventional schemes. For instance, the proposed scheme provides gains of up to 3.0 dB at bit error rate 10/sup -4/, relative to the conventional scheme.

In this paper, we study cyclic codes of length n= 2/sup e/ over the ring R/sub 4/= /spl Zopf//sub 4/[x]/(x/sup n/-1). In particular, we derive a mass formula of these codes for a given length n. We also give an example in which we study codes of length 8.

In this paper, we derive upper bounds on the bit error rate performance of orthogonal space-time block codes (STBCs) with receive antenna selection. We show that the diversity order is maintained as that of the full complexity system, whereas the deterioration in SNR is upper bounded by 10 log10(M/L) dB where M is the number of available receive antennas and L is the number of selected antennas.

We present a modified soft-output Viterbi algorithm (MSOVA) that performs as good as the a posteriori probability (APP) algorithm with a complexity similar to that of the conventional SOVA algorithm. The idea behind the MSOVA centers around reducing the inherent correlation between the intrinsic information (input to the SOVA) and extrinsic information (output of the SOVA), where the latter is typically much higher than its APP counterpart. The proposed algorithm employs two attenuators, one applied directly to the output of the SOVA and another applied to the extrinsic information before it is passed to the other decoder (assuming iterative decoding). We examine the MSOVA on additive white Gaussian noise (AWGN) and fading channels. We show that the MSOVA provides improvements of about 0.8 to 1.0 dB at P/sub b/ = 10/sup -5/ in AWGN over the conventional SOVA, and is only about 0.1 dB away from the APP. It also provides improvements of 1.4 to 2.0 dB at P/sub b/ = /sup -5/ on fading channels.

We derive explicit upper bounds on the pairwise-error probability (PEP) for space-time trellis codes (STTCs) with receive antenna selection over fast fading channels. In performing antenna selection, we adopt a selection criterion that is based on selecting L out of the available M receive antennas that result in maximizing the instantaneous signal-to-noise ratio (SNR) at the receiver, where L/spl les/M. We show that the resulting diversity order deteriorates significantly and becomes a function of the number of selected antennas. The implication of this result is that adding more receive antennas, while maintaining the same number of selected ones, will have no impact on the diversity order, but it does, however, provide some additional coding gain. This is unlike the case for quasistatic fading channels in which the diversity order is always preserved with antenna selection when the underlying space-time code is full-rank. We also present simulation results that support our analysis.

In this correspondence, we study cyclic codes of length n=2/sup e/ over the ring R/sub 4/=/spl Zopf//sub 4/[x]/(x/sup n/-1). In particular, we derive a closed-form expression for the number of these codes for a given length n. We also study the rank of these codes and derive an expression for that. Furthermore, we give an example in which we study all cyclic codes of length 8. We also study all self-dual codes of length 8 and 16 and classify them according to their type.

In this correspondence, we present a comprehensive performance analysis of orthogonal space-time block codes (STBCs) with receive antenna selection. For a given number of receive antennas M, we assume that the receiver uses the best L of the available M antennas, where, typically, L/spl les/M. The selected antennas are those that maximize the instantaneous received signal-to-noise ratio (SNR). We derive explicit upper bounds on the bit-error rate (BER) performance of the above system for any M and L, and for any number of transmit antennas. We show that the diversity order, with antenna selection, is maintained as that of the full complexity system, whereas the deterioration in SNR is upper-bounded by 10log/sub 10/(M/L) decibels. Furthermore, we derive a tighter upper bound for the BER performance for any N and M when L=1, and derive an expression for the exact BER performance for the Alamouti scheme when L=1. We also present simulation results that validate our analysis.

In this letter, we propose a new tracking scheme that is robust against multipath fading for pseudonoise (PN) code tracking in direct-sequence-spread spectrum systems. The proposed scheme employs an adaptive filter whose taps are adapted using a block least-mean square algorithm and it results in minimizing the effect of multipath interference on the tracking performance. We show that the mean-squared tracking error performance of the proposed scheme is not affected by the presence of closely spaced paths (e.g., one to three chips), unlike that of conventional delay locked loops. We also show that the tap-weight distribution of the filter provides accurate estimates of the multipath delays. For example, at E/sub b//N/sub 0/=5 dB, 98% of the time the path estimates lie within one sample (1/5 of a chip) from the actual delays. Furthermore, simulation results suggest that multipath delays over a wide range of terminal speeds can be tracked successfully. The proposed scheme is well suited for wideband code-division multiple-access systems where a large number of closely-spaced multipath components need to be tracked and used in RAKE combining.

In this paper, we discuss an approach for analyzing the serial concatenation of a high rate convolutional code and a preceded intersymbol interference (ISl) channel. The proposed approach is simple and can be used to identify the preceder that results in the lowest error rate floor analytically without resorting to simulations. Also, the proposed approach can be used to enhance the interleaver design to achieve further performance improvement. We extend the proposed technique to fading ISI channels using statistical models of these channels. Simulation results suggest that the optimal preceder, when selected based on the statistical model of the channel, is optimal or somewhat optimal for the time varying channel. We finally remark that the proposed analytical approach is not limited to high rate convolutional codes, but it also applies to arbitrary high rate linear outer codes.

Multipath propagation is a major source of degradation in the performance of conventional PN code tracking loops. The mean-square tracking error of a conventional delay-locked loop (DLL) exhibits an irreducible floor due to the presence of closely spaced multipath components. We present a new PN code tracking algorithm that does not suffer from the presence of multipath components. This algorithm utilizes a finite impulse response (FIR) adaptive filter to mitigate the effect of multipath on delay estimation. It is shown that the tap-weight vector of the adaptive filter can be used to provide accurate estimation of the multipath delays. It is also shown that the mean-square tracking error of the proposed scheme is not affected by paths that are very close (within 1 to 2 chips apart). The effect of terminal mobility is also investigated by simulating the performance under frequency selective fading channel conditions with different Doppler frequencies. The proposed scheme is well suited for wideband CDMA systems where a large number of closely spaced multipath components need to be tracked and used in RAKE combining.

In this paper, we discuss an algorithm for mitigating the deleterious effects of nonlinear intersymbol interference (ISI) in nonlinear satellite channels. We call this algorithm the RAM-search/Viterbi detector (RS-VD) algorithm because it borrows the idea of employing a random-access memory (RAM) from RAM-DFE, it involves search, and it is incorporated into the Viterbi detector. The RS-VD algorithm has been devised to mitigate both precursor and postcursor (nonlinear) ISI. It also mitigates the error propagation problem that arises in making feedforward decisions (for precursor terms) in ISI cancellers used in combination with partial-response signaling. Simulation results indicate that the proposed algorithm achieves a significant performance improvement over the no-equalization case. Similar positive results are expected regardless of the nonlinearity involved as the RAM can model, via training, the nonlinearity of arbitrary nonlinear channels.

A number of papers have been published on the concatenation of an outer code with a partial response (PR) channel, where the outer code is a turbo code, a convolutional code, or a low-density parity-check code. This paper deals with the second case, assuming EPR4 and modified extended EPR4 (MEEPR4) partial response (PR) targets. The goals in this work include (1) the joint optimization of interleaver and precoder for a fixed outer convolutional code and PR target, (2) the choice of optimal code rate for both PR targets assuming a Lorentzian model, and (3) an assessment of the performance of these codes in the presence of thermal asperities. We introduce mathematical and algorithmic tools for accomplishing these goals and present simulation results that support our approach. Among the positive results is the ability to lower the well-known error rate floor of these concatenated schemes for arbitrary PR channels.

A number of papers have recently been published on the concatenation of an outer code with a partial response (PR) channel, where the outer code is a turbo code, a convolutional code, or a low density parity check code. This paper deals with the second case assuming EPR4 and modified extended EPR4 (MEEPR4) partial response targets. The goal in this work is the joint optimization of interleaver and precoder for a fixed outer convolutional code and PR target. We introduce mathematical and algorithmic tools for accomplishing this goal and present simulation results which support our approach. Among the positive results is the ability to lower the well-known error rate floor of these concatenated schemes for arbitrary partial response channels.

We modify the iterative soft-output Viterbi algorithm (SOVA) for partial response (PR) channel and examine its performance for parallel and serial concatenated codes on a precoded Class IV partial response (PR4) channel. We present a SOVA that differs slightly from what appears in the literature and compare the performance of the two versions. Code rates of the form k/sub 0//k/sub 0/+1 (k/sub 0/=4, 8, and 64) are considered. Our simulations indicate that the loss at P/sub b/=10/sup -5/ suffered by the SOVA, relative to the APP algorithm, is at most 1.2 dB for parallel concatenations and at most 1.6 dB for serial concatenations.

We consider the concatenation of a high rate punctured convolutional code and a precoded intersymbol interference (ISI) channel separated by an interleaver. The goal in this work is to develop design techniques for the interleaver and precoder for a given ISI channel where the design criterion is to obtain the lowest error rate floor. Our results show that an S-random interleaver is an appropriate choice for this system and that the optimal precoder may be selected through the use of what we call truncated conditional distance enumerators. Simulation results support the techniques developed.