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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.

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 letter, we investigate the amalgamation between the reconfigurable intelligent surface (RIS) technology and the joint transmission coordinated multipoint (JT-CoMP) in order to enhance the performance of a cell-edge user equipment (UE) in a two-user non-orthogonal multiple access (NOMA) group without deteriorating the performance of the NOMA cell-center UE. The RIS is adopted to construct a strong combined channel gain at the cell-edge UE, while JT-CoMP is used to mitigate the effects of inter-cell interference (ICI). In this proposed framework, we derive first a closed-form expression for the ergodic rate of the cell-edge UE, and then we evaluate the network spectral efficiency. We validate the derived expression through Monte-Carlo simulations, where we demonstrate the efficacy of the proposed framework compared to other multiple access techniques proposed in the literature.

Unmanned aerial vehicles (UAVs) are envisioned to play a key role in intelligent transportation systems to complement the communication infrastructure in future smart cities. UAV-assisted vehicular networking research typically adopts throughput and latency as the main performance metrics. These conventional metrics, however, are not adequate to reflect the freshness of the information, an attribute that has been recently identified as a critical requirement to enable services such as autonomous driving and accident prevention. In this paper, we consider a UAV-assisted single-hop vehicular network, wherein sensors (e.g., LiDARs and cameras) on vehicles generate time sensitive data streams, and UAVs are used to collect and process this data while maintaining a minimum age of information (AoI). We aim to jointly optimize the trajectories of UAVs and find scheduling policies to keep the information fresh under minimum throughput constraints. The formulated optimization problem is shown to be mixed integer non-linear program (MINLP) and generally hard to be solved. Motivated by the success of machine learning (ML) techniques particularly deep learning in solving complex problems with low complexity, we reformulate the trajectories and scheduling policies problem as a Markov decision process (MDP) where the system state space considers the vehicular network dynamics. Then, we develop deep reinforcement learning (DRL) to learn the vehicular environment and its dynamics in order to handle UAVs’ trajectory and scheduling policy. In particular, we leverage Deep Deterministic Policy Gradient (DDPG) for learning the trajectories of the deployed UAVs to efficiently minimize the Expected Weighted Sum AoI (EWSA). Simulations results demonstrate the effectiveness of the proposed design and show the deployed UAVs adapt their velocities during the data collection mission in order to minimize the AoI.

The proliferation of Internet of Things (IoT) has brought an array of different services, from smart health-care, to smart transportation, all the way to smart cities. For a truly connected environment, different sectors need to collaborate. One use case of such overlap is between smart grids and Intelligent Transportation System (ITS) giving rise to Electric Vehicles and their charging infrastructure. Being such a lucrative opportunity for investors and the research community, many efforts have been made toward providing the end-user with an extraordinary Quality of Service (QoS). However, given the current protocols and deployment of the Electric Vehicle (EV) charging infrastructure, some key challenges still need to be addressed. In particular, we identify two main EV challenges: (1) vulnerable charging stations and EVs, and (2) non-optimal charging schedules. With these issues in mind, we evaluate the integration of Blockchain and AI with the EV charging infrastructure. Specifically, we discuss the current AI and Blockchain charging solutions available in the market. In addition, we propose a couple of use cases where both technologies complement each other for a secure, efficient and decentralized charging ecosystem. This article serves as starting point for stakeholders and policymakers to help identify potential directions and implementations of better charging systems for EVs.

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.

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.

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.

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*10-2 kWh 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.

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.

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.

Due to the dramatic increase in high data rate services and in order to meet the demands of the fifth-generation (5G) networks, researchers from both academia and industry are exploring advanced transmission techniques, new network architectures and new frequency spectrum such as the visible light and the millimeter wave (mmWave) spectra. Visible light communication (VLC) particularly is an emerging technology that has been introduced as a promising solution for 5G and beyond, owing to the large unexploited spectrum, which translates to significantly high data rates. Although VLC systems are more immune against interference and less susceptible to security vulnerabilities since light does not penetrate through walls, security issues arise naturally in VLC channels due to their open and broadcasting nature, compared to fiber-optic systems. In addition, since VLC is considered to be an enabling technology for 5G, and security is one of the 5G fundamental requirements, security issues should be carefully addressed and resolved in the VLC context. On the other hand, due to the success of physical layer security (PLS) in improving the security of radio-frequency (RF) wireless networks, extending such PLS techniques to VLC systems has been of great interest. Only two survey papers on security in VLC have been published in the literature. However, a comparative and unified survey on PLS for VLC from information theoretic and signal processing point of views is still missing. This paper covers almost all aspects of PLS for VLC, including different channel models, input distributions, network configurations, precoding/signaling strategies, and secrecy capacity and information rates. Furthermore, we propose a number of timely and open research directions for PLS-VLC systems, including the application of measurement-based indoor and outdoor channel models, incorporating user mobility and device orientation into the channel model, and combining VLC and RF systems to realize the potential of such technologies.

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.

Unmanned aerial vehicles (UAVs) have recently been envisaged as an enabling technology of 5G. UAVs act as an intermediate relay node to facilitate uninterrupted, high quality communication between information sources and their destination. However, UAV energy management has been a major issue of consideration due to limited power supply, affecting flight duration. Thus, we introduce in this paper a unified energy management framework by resorting to wireless power transfer (WPT), simultaneous wireless information and power transfer (SWIPT) and self-interference (SI) energy harvesting (EH) schemes, in cooperative relay communications. In our new technique, UAVs are deployed as relays equipped with a decode and forward protocol and EH capability operating in a full-duplex (FD) mode. The UAV assists information transmission between a terrestrial base station and a user. The UAV's transmission capability is powered exclusively by the energy harvested from WPT, radio frequency signal transmitted from the source via time-switching SWIPT protocol and SI exploitation. We improve the overall system throughput by the use of FD based UAV-assisted cooperative system. In this proposed system, we formulate two optimization problems to minimize end-to-end outage probability, subject to UAV's power profile and trajectory for a DF relay scheme, respectively. The KKT conditions have been used to obtain closed-form solutions for the two formulated problems. Numerical simulation results validate all the theoretical results. We demonstrate that the performance of our proposed unified EH scheme outperforms that of existing techniques in the literature. Ministry of Human Resource Development Scopus

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.

This paper investigates the performance of coop- erative non-orthogonal multiple access (C-NOMA) in cellulardownlink 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 thecell edge, undergo severe fading and suffer from poor channelquality and low achievable rates. To overcome this problem, C-NOMA is proposed as the system design methodology, in whichusers that have the capability of full-duplex (FD) communicationcan assist the transmissions between the BS and users withpoor channel quality through device-to-device (D2D) communi-cations. To harness both the multiplexing gain from NOMA andthe diversity gain from FD-D2D communications, we formulateand solve a novel optimization problem that jointly deter-mines D2D user pairing and power allocation. The formulatedproblem is a mixed-integer non-linear program (MINLP) withprohibitively high complexity. To overcome this issue, a two-steppolicy is proposed to solve the problem in polynomial time. Oursimulation results show that with reasonable assumptions, theproposed scheme always outperforms some existing schemesin 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 conventionalNOMA.

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%.

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. Qatar Foundation; Qatar National Research Fund Scopus

In this paper, we study the achievable secure degrees-of-freedom (sdof) for the multiple-input single-output (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.

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.

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. Qatar Foundation;Qatar National Research Fund;Natural Sciences and Engineering Research Council of Canada;Concordia University Scopus

In this paper, we consider collaborative beamforming for a cooperative two-way relay spectrum-sharing system where 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 and use network coding to simultaneously transmit the weighted signals to the transceivers. Two practical two-way relaying strategies are investigated, namely, DF- xor ing and DF-superposition. The former is based on bitwise-level xor ing of the detected signals at the relays, whereas the latter is based on symbolwise-level addition at the relays. For each relaying strategy, we derive general optimal beamforming vectors at the relays to keep the interference inflicted on the primary receivers to a predefined threshold. Employing zero-forcing beamforming as a special case, we present an analytical framework of the performance of the secondary system considering the effect of the primary–secondary mutual cochannel interference (CCI). In particular, we derive closed-form expressions for the outage probability, bit error rate (BER), and achievable sum rates over independent and identically distributed Rayleigh fading channels. Numerical results demonstrate the effectiveness of beamforming in compensating for the cognitive system performance loss due to CCI in addition to mitigating the interference to the primary receivers. Simulation results also show that, when the received signals at the relays are equally weighted, DF- xor always outperforms both DF-superposition and amplify-and-forward (AF) relaying.

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.

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. Scopus

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 amplify-and-forward (AF) relays are employed to help a pair of secondary transceivers in the presence of multiple licensed (primary) users. The set of relays participate in the beamforming process, where the optimal beamformer weights are obtained via a linear optimization method. For this system, we investigate the transmission protocols over two and three time-slots. To study and compare the performance tradeoffs between the two transmission protocols, for both of them, we derive closed-form expressions for the cumulative distribution function (CDF) and the moment generating function (MGF) of the equivalent end-to-end signal-to-noise ratio (SNR) at the secondary receiver. We analyze the performance of the proposed methods where closed-form expressions for the user outage probability and the average error probability are derived for independent and identically distributed Rayleigh fading channels. Numerical results demonstrate the efficacy of beamforming in enhancing the secondary system performance in addition to mitigating the interference to the primary users. In addition, our results show that the three time-slot protocol outperforms the two time-slot protocol in certain scenarios where it offers a good compromise between bandwidth efficiency and system performance.

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.

We investigate the joint problem of optimal relay selection and power allocation in wireless cellular networks considering both unicast and multicast traffic. We first present mixed Boolean-convex optimization models for both unicast and multicast traffic scenarios to maximize the overall network capacity and solve these combinatorial problems optimally using the branch and bound technique. We then show that obtaining the optimal solution is computationally infeasible for large network sizes. For unicast, similar to [1], we present an efficient water-filling based technique to obtain near optimal solutions. We show that, unlike unicast traffic, water-filling does not yield near optimal solutions in multicast scenarios. We thus adopt an algorithm based on sequential fixing for the multicast case which substantially reduces the computation time and achieves near optimal solutions. Furthermore, in the above scenarios, we assume that the power levels are drawn from a continuous range. To make the proposed methods more practical, we also consider scenarios when the number of power levels is finite (i.e., discrete). We present optimal and sub-optimal methods for solving this optimization problem and compare their performance with previous methods.

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).

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.

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.

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 M 1 -QAM (quadrature amplitude modulation) and the other uses M 1 /M 2 -QAM hierarchical modulation where M 1 ≠ M 2 . 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. Scopus

We propose an iterative compressive channel estimation and decoding scheme for network-channel-coded two-way relay sparse intersymbol interference channels. The underlying system model comprises two source nodes and one relay node. In the first time slot, the two sources simultaneously transmit their independent training pilots along with the channel-coded data. In the second time slot, the relay applies analog network coding to the sum of the received signals and broadcasts the result to the two sources. Each source then uses iterative compressive sensing (CS) based estimation and decoding to estimate the composite channels and recover the transmitted signal. We analyze the performance of our proposed CS-based scheme and demonstrate its efficacy over existing ones Qatar Foundation; Qatar National Research Fund Scopus

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.

In this paper, we address the problem of asymmetric data rates in two-way communication systems. In particular, we propose an adaptive transmission scheme that combines network coding (NC) and opportunistic user selection (OUS) with a threshold that determines which transmission mode to use. The underlying system model comprises two source nodes communicating with each other through a relay node. The source nodes are assumed to have different data rate requirements; therefore, they employ different modulation schemes. As per the proposed scheme, if the end-to-end (E2E) signal-to-noise ratio (SNR) of both users are above a specified threshold, both sources transmit over orthogonal channels, and the relay node uses hierarchical modulation and NC to relay the combined signals to both sources in the third time slot. Otherwise, the user with the better E2E SNR transmits, whereas the other user remains silent. The advantage of the proposed scheme is that it compromises between throughput and reliability. That is, when both users transmit, the throughput improves, whereas when the better user transmits, multiuser diversity is achieved. Assuming asymmetric channels, we derive exact closed-form expressions for the E2E bit error rate (BER), access probability, and throughput for this scheme and compare its performance with existing schemes. We also investigate the asymptotic performance of the proposed scheme at high SNRs where we derive the achievable diversity order for both users. We show through analytical and simulation results that the proposed scheme improves the overall system throughput, the fairness between the two users, and the transmission reliability. This all comes while achieving full spatial diversity for both users. Scopus

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. This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) under Grant References EP/H044191/1 and EP/L026074/1, and in part by the NPRP Grant 4-1119-2-427 from the Qatar National Research Fund (a member of Qatar Foundation). Scopus

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. Scopus

This paper proposes two detection schemes for cooperative networks comprising a source, a relay and a destination. The relay is assumed to operate in a half-duplex mode and it employs decode-and-forward (DF) relaying. The proposed schemes involve combining threshold-based relaying and maximum likelihood (ML) detection at the destination. We consider both signal-to-noise ratio (SNR)-based and log-likelihood (LLR)-based thresholding. Assuming binary phase shift keying (BPSK), we first derive the ML detector as a function of the threshold used at the relay node. Then, we obtain the optimal thresholds by minimizing the end-to-end bit error rate performance. In deriving the ML performance, we follow an approach that is different from existing approaches and is more straightforward. We compare the performance of the proposed schemes and show that they significantly outperform all existing counterpart detection methods. Qatar Foundation; Qatar National Research Fund Scopus

We investigate the joint problem of relay selection and optimal sharing of relay power in wireless cellular networks with multicast traffic. We first present a mixed Boolean-convex optimization model and solve this combinatorial problem optimally using branch and bound technique. We then show that obtaining the optimal solution is computationally infeasible 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.

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.

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 trade-off in the selection of the power values, which is addressed in this work by using a chance-constrained optimization framework. Simulations are presented to demonstrate the efficacy of the proposed approach.

We consider L far-field terminals with one source and L-1 interferences that transmit to a wireless sensor network (WSN) with K uniformly distributed relaying nodes. Each relaying node receives a signal mixture from the L transmitters in the first phase, multiplies it with a properly selected beamforming weight and retransmits the resultant signal to a single receiving terminal in the second phase. The decentralized nature of the WSN dictates every node to compute its beamforming weight based only on its limited locally available information and without the knowledge of the locations and the channels of any other node in the network. Unfortunately, the optimal beamforming weights that maximize the signal-to-interference-plus-noise ratio (SINR) at the receiver cannot be computed locally. To circumvent this problem, we derive accurate local approximates of the SINR-optimal beamforming weights. Our proposed beamforming technique uses the so-obtained locally computable weights and, hence, can be implemented in a distributed fashion. The performance of the proposed distributed beamformer is analyzed both when the directions of the interferences are perfectly known and when they are imperfectly estimated. The advantages of the proposed distributed beamformer in comparison with a conventional distributed beamformer are analytically proved and are further verified by various simulation results.

Null-steering transmit beamformers aim to maximize the received signal power in the direction of the intended receiver while substantially reducing the power impinging on the unintended receivers located in other directions. The existing null-steering beamformers may not be directly applied in wireless sensor networks (WSNs) as they do not conform with the decentralized nature of WSNs and require every node to be aware of the locations of all other nodes in the network. This manuscript presents a novel collaborative null-steering beamformer 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 it is shown that the beampattern associated with any arbitrary realization of the nodes' locations converges with probability one to the so-obtained average beampattern as the number of collaborating nodes grows large. Properties of the average beampattern are analytically studied. In particular, it is proven that the average gain of the proposed beamformer is inversely proportional to the number of collaborating nodes in the directions of unintended receivers and further, if a mild condition is satisfied, it is approximately equal to that of the collaborative conventional beamformer in the directions with far angular distance from any unintended receiver. It is argued that if virtual unintended receivers are assumed at proper directions, then the proposed collaborative null-steering beamformer can form an average beampattern with sidelobe peaks substantially smaller than those of the average beampattern of the collaborative conventional beamformer. To substantiate this argument, the optimal direction of a virtual unintended receiver is obtained such that its associated collaborative null-steering beamformer forms an average beam-pattern with minimal largest side-lobe peak. Depending on the number of collaborating nodes, it is further shown that the largest average sidelobe peak of the latter beamformer is up to 6.6 (dB) less than that of the collaborative conventional beamformer.

As the nodes in wireless sensor networks (WSNs) are independent units, an intensive communication among them is required to generate a common signal and synchronize before entering a distributed beamforming (DBF) phase. Therefore, it is crucial to select the participating nodes in DBF such that not only the resulting beampattern meets the beamforming design requirements but also the internode connectivity is retained. We consider a DBF technique for WSNs with uniformly distributed nodes and derive an average beampattern expression for a general scenario wherein the participating nodes in DBF are located on a ring with arbitrary inner and outer radii. It is proved that increasing the ring inner radius from zero to a value close to the ring outer radius, the width of the average beampattern mainlobe continuously decreases. Further, it is shown that selecting the nodes from a neighborhood close to a disc perimeter, that is, choosing the nodes from the narrow ring adjacent to the inner side of the disc boundary, facilitates a substantial decrease in the network energy waste and the node isolation probability compared to the case that the nodes are randomly selected from the whole disc. A simple approximate expression for the average beampattern is obtained in the case where the nodes are selected from a narrow ring and is used to derive the sidelobes' null and peak positions as well as a tight lower bound on the average beampattern directivity. The proposed technique is then extended to the case where the nodes are located on multiple concentric rings and the set of rings' radii are derived that guarantee an average beampattern null at a required position while substantially decreasing the sidelobe peak levels compared to the single-ring case. Finally, an average beampattern expression is obtained in the case that the nodes' signals are contaminated by noise to show that most properties of the average beampattern in the noise-free signal case carry over to the noisy signal scenario.

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 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.

In this paper, the performance of the 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 unboundedly large with the same ratio. Assuming that each terminal is paired with another user which, in addition to transmitting its own data, estimates and relay the information transmitted from its partner, a simple approximate 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 and without any knowledge about the interfering users. 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 preassigned target SINR for both users. Based on the local information, the globally optimal amount of the relay power is obtained that maximizes the achieved SINR at the access point. It is shown that increasing the relay power does not necessarily result in improving the quality of reception at the access point and, to maximize each user's SINR, its relay power should be carefully adjusted based on the environmental parameters such as the interpartner channel link and the powers of MAI and the relay noise. The connection between the cooperative and the conventional multiuser systems is also studied and simulations are used to demonstrate the validity of the analytical results.

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.

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.