Robust signal detection in colored noise with unknown covariance is essential in radar, cognitive radio, integrated sensing and communication (ISAC), and quantum sensing applications. This paper develops a unified analytical framework for the Standard Condition Number (SCN) detector, which employs the ratio of the largest to smallest eigenvalues of the whitened sample covariance matrix. The framework jointly covers both ideal conditions in which the training and sensing noise statistics are identical and disturbed conditions in which interference or jamming alters the sensing covariance. Despite the SCN's practical relevance, its finite-sample false-alarm and detection behavior has not been analytically characterized. Using random matrix theory (RMT), we derive general expressions for these probabilities, provide closed-form results for special cases, and show that the SCN preserves the Constant False Alarm Rate (CFAR) property under covariance mismatch. Analytical and simulation results confirm that the proposed unified framework delivers consistent detection performance and greater robustness than conventional eigenvalue- and LRT-based detectors.

This paper investigates uplink reconfigurable intelligent surface (RIS)-assisted massive random access (RA) in multiuser cellular networks with heterogeneous user distances. The goal is to enhance media access control (MAC)-layer throughput under practical signaling and channel constraints. Conventional RIS configurations using full-array probing incur excessive overhead, especially with large numbers of reflective elements (REs). To address this, we propose an overhead-aware, multi-phase uplink RA framework that integrates adaptive RIS subarray grouping with scalable multiuser scheduling for dynamic user activity. The access optimization is formulated as a sequential decision process, from which an optimal threshold-based policy with closed-form structure is derived. Building on this, an opportunistic RA algorithm is developed to jointly optimize user scheduling, subarray configuration, and access timing with low online complexity that scales with RIS resolution, subchannel count, and user density. Numerical simulations, supported by analytical results, demonstrate significant throughput gains over existing schemes—including full-array, fixed-grouping, and both orthogonal and non-orthogonal multiple access methods—across varying coherence times, RIS sizes, and user distributions. The robustness of the proposed strategy is further verified under imperfect CSI conditions, confirming its suitability for large-scale, dynamic cellular deployments. The proposed framework is further shown to remain effective under practical impairments such as spatial correlation and finite-resolution RIS phase control.

This paper presents an analytical framework for modeling line-of-sight (LoS) availability in Reconfigurable Intelligent Surface (RIS)-assisted satellite communication systems under urban blockages. A correlated LoS probability model is proposed using a bivariate Bernoulli framework that captures spatial dependencies between satellite-to-user and satellite-to-RIS links. We derive closed-form expressions for the system-level success probability, incorporating elevation angle, blockage statistics, and RIS deployment geometry. A key result is a closed-form expression for the optimal RIS-user separation distance that maximizes system reliability. Monte Carlo simulations across diverse urban environments validate the accuracy of the proposed model. Numerical results reveal that denser urban settings require shorter RIS-user distances for optimal performance. The framework enables tractable performance analysis and guides efficient RIS deployment in urban SatCom scenarios.

Caching is crucial in hybrid satellite-terrestrial networks to reduce latency, optimize throughput, and improve data availability by storing frequently accessed content closer to users, especially in bandwidth-limited satellite systems, requiring strategic Medium Access Control (MAC) layer. This paper addresses throughput optimization in satellite-terrestrial integrated networks through opportunistic cooperative caching. We propose a joint probing and scheduling strategy to enhance content retrieval efficiency. The strategy leverages the LEO satellite to probe satellite-to-ground links and cache states of multiple cooperative terrestrial stations, enabling dynamic user scheduling for content delivery. Using an optimal stopping theoretic approach with two levels of incomplete information, we make real-time decisions on satellite-terrestrial hybrid links and caching probing. Our threshold-based strategy optimizes probing and scheduling, significantly improving average system throughput by exploiting cooperative caching, satellite-terrestrial link transmission, and time diversity from dynamic user requests. Simulation results validate the effectiveness and practicality of the proposed strategies.

In Reconfigurable Intelligent Surfaces (RIS), reflective elements (REs) are typically configured as a single array, but as RE numbers increase, this approach incurs high overhead for optimal configuration. Subarray grouping provides an effective tradeoff between performance and overhead. This paper studies RIS-aided massive random access (RA) at the Medium Access Control (MAC) layer in cellular networks to enhance throughput. We introduce an opportunistic scheduling scheme that integrates multi-round access requests, subarray grouping for efficient RIS link acquisition, and multi-user data transmission. To optimize access request timing, RIS estimation overhead and throughput, we propose a multi-user RA strategy using sequential decision optimization to maximize average system throughput. A lowcomplexity algorithm is also developed for practical implementation. Both theoretical analysis and numerical simulations demonstrate that the proposed strategy significantly outperforms the extremes of full-array grouping and element-wise grouping.

Cooperative spectrum sensing (CSS) is essential for improving the spectrum efficiency and reliability of cognitive radio applications. Next-generation wireless communication networks increasingly employ uniform planar arrays (UPA) due to their ability to steer beamformers towards desired directions, mitigating interference and eavesdropping. However, the application of UPA-based CSS in cognitive radio remains largely unexplored. This paper proposes a multi-beam UPA-based weighted CSS (WCSS) framework to enhance detection reliability, applicable to various cognitive radio networks, including cellular, vehicular, and satellite communications. We first propose a weighting factor for commonly used energy detection (ED) and eigenvalue detection (EVD) techniques, based on the spatial variation of signal strengths resulting from UPA antenna beamforming. We then analytically characterize the performance of both weighted ED and weighted EVD by deriving closed-form expressions for false alarm and detection probabilities. Our numerical results, considering both static and dynamic user behaviors, demonstrate the superiority of WCSS in enhancing sensing performance compared to uniformly weighted detectors.

Reconfigurable Intelligent Surfaces (RIS) have emerged as transformative technologies, enhancing spectral efficiency and improving interference management in multi-user cooperative communications. This paper investigates the integration of RIS with Flexible-Duplex (FlexD) communication, featuring dynamic scheduling capabilities, to mitigate unintended external interference in multi-user wireless networks. By leveraging the reconfigurability of RIS and dynamic scheduling, we propose a user-pair selection scheme to maximize system throughput when full channel state information (CSI) of interference is unavailable. We develop a mathematical framework to evaluate the throughput outage probability when RIS introduces spatial correlation. The derived analytical results are used for asymptotic analysis, providing insights into dynamic user scheduling under interference based on statistical channel knowledge. Finally, we compare FlexD with traditional Full Duplex (FD) and Half Duplex (HD) systems against RIS-assisted FlexD. Our results show FlexD’s superior throughput enhancement, energy efficiency and data management capability in interference-affected networks, typical in current and next-generation cooperative wireless applications like cellular and vehicular communications.

This paper considers a MIMO Integrated Sensing and Communication (ISAC) system, where a base station simultaneously serves a MIMO communication user and a remote MIMO sensing receiver, without channel state information (CSI) at the transmitter. Existing MIMO ISAC literature often prioritizes communication rate or detection probability, typically under constant false-alarm rate (CFAR) assumptions, without jointly analyzing detection reliability and communication constraints. To address this gap, we adopt an eigenvalue-based detector for robust sensing and use a performance metric—the total detection error—that jointly captures false-alarm and missed-detection probabilities. We derive novel closed-form expressions for both probabilities under the eigenvalue detector, enabling rigorous sensing analysis. Using these expressions, we formulate and solve a —joint power allocation and threshold optimization— problem that minimizes total detection error while meeting a minimum communication rate requirement. Simulation results demonstrate that the proposed joint design substantially outperforms conventional CFAR-based schemes, highlighting the benefits of power-and threshold-aware optimization in MIMO ISAC systems.

This research presents a novel framework integrating Flexible-Duplex (FlexD) and Integrated Sensing and Communications (ISAC) technologies to address the challenges of spectrum efficiency and resource optimization in next-generation wireless networks. We develop a unified system model for a dual-functional radar-communication base station with multiple-input multiple-output capabilities, enabling dynamic uplink and downlink channel allocation. The framework maximizes network throughput while maintaining radar sensing performance, subject to signal-to-clutter-plus-noise ratio (SCNR) requirements and power constraints. Given the non-convex and combinatorial nature of the resulting optimization problem, we propose an iterative algorithm that converges to a locally optimal solution. Extensive simulations demonstrate the superiority of the proposed FlexD-ISAC framework compared to conventional half-duplex networks. Additionally, sensitivity analyses reveal the impact of SCNR requirements and power constraints on system performance, providing valuable insights for practical implementation. This work establishes a foundation for future research in dynamic, resource-efficient wireless systems that simultaneously support sensing and communication capabilities.

This paper investigates an intelligent reflective surface (IRS) assisted secure multi-user two-way communication system. The aim of this paper is to enhance the physical layer security by optimizing the minimum secrecy-rate among all user-pairs in the presence of a malicious user. The optimization problem is converted into an alternating optimization problem consisting of two sub-problems. Transmit power optimization is handled using a fractional programming method, whereas IRS phase shift optimization is handled with semi-definite programming. The convergence of the proposed algorithm is investigated numerically. The performance gain in minimum secrecy-rate is quantified for four different user configurations in comparison to the baseline scheme. Results indicate a 3.6-fold gain in minimum secrecy rate over the baseline scheme when the IRS is positioned near a legitimate user, even when the malicious user is located close to the same legitimate user.

This paper investigates optimal distributed Medium Access Control (MAC) strategies for wireless Internet of Things (IoT) networks, incorporating both terrestrial and non-terrestrial relays, including UAVs. While prior research has primarily focused on single-relay forwarding, we address the complexities of energy-efficient multi-relay data forwarding. This involves managing the challenges of relay probing, optimized relay utilization, and balancing energy trade-offs to maximize system energy efficiency (EE). To address these challenges, we propose a novel strategy called Distributed Data Collection with Opportunistic Relaying (DDC/OR), designed to optimize MAC performance in multi-relay IoT networks. Our approach leverages a decision-theoretic framework based on optimal sequential planning, extending traditional cooperative MAC models to support multiple relays. The proposed DDC/OR strategy offers a statistically optimized solution that maximizes average EE, with a rigorous proof of optimality. Additionally, we present a low-complexity implementation of the DDC/OR algorithm, suitable for practical deployments. For scenarios involving dynamic non-terrestrial movements and varying relay inter-distances, we introduce a two-timescale, self-organized algorithm to adaptively reconfigure relay strategies. To ensure scalability for large relay networks, several optimizations are introduced to enhance the feasibility of the DDC/OR approach. We validate the effectiveness of the DDC/OR strategy through extensive simulations, demonstrating significant EE gains in both terrestrial and non-terrestrial relay configurations. Notably, it also achieves comparatively better performance in latency, throughput, and overall energy consumption.

This research focuses on the challenges of distributed Medium Access Control (MAC) protocols involving Reconfigurable Intelligent Surfaces (RISs), which are still in early development. The study explores optimal channel access for multiple source-destination pairs in distributed networks with the assistance of multiple RISs. Three key issues are addressed: joint scheme for channel contention, RISs’ channel state information (CSI) acquisition, and RIS-assisted channel access; tradeoff between overhead and effective data transmission; and low-complexity distributed network operation. To achieve maximum network throughput, the paper proposes an optimal distributed Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) strategy with opportunistic RIS assistance based on statistical optimization. The proposed MAC strategy's optimality in terms of average network throughput is rigorously derived. Closed-form expressions for threshold functions of rewards for making decisions are derived, and an easy-to-implement distributed channel access algorithm is provided with online complexity$\mathcal {O}(2^{L})$, where$L$denotes the number of distributed RISs. The proposed MAC strategy is then refined, and a low-complexity distributed algorithm is developed with complexity$\mathcal {O}(L)$. Numerical simulations verify the theoretical results, demonstrating the efficiency of the proposed strategy. This work introduces innovative solutions and analytical frameworks for the distributed MAC problem with RIS assistance, significantly advancing existing research.

This paper addresses the challenges of throughput optimization in wireless cache-aided cooperative networks. We propose an opportunistic cooperative probing and scheduling strategy for efficient content delivery. The strategy involves the base station probing the relaying channels and cache states of multiple cooperative nodes, thereby enabling opportunistic user scheduling for content delivery. Leveraging the theory of Sequentially Planned Decision (SPD) optimization, we dynamically formulate decisions on cooperative probing and stopping time. Our proposed Reward Expected Thresholds (RET)-based strategy optimizes opportunistic probing and scheduling. This approach significantly enhances system throughput by exploiting gains from local caching, cooperative transmission and time diversity. Simulations confirm the effectiveness and practicality of the proposed Media Access Control (MAC) strategy.

This paper presents an optimization approach for cooperative Medium Access Control (MAC) techniques in Vehic-ular Ad Hoc Networks (VANETs) equipped with Roadside Unit (RSU) to enhance network throughput. Our method employs a distributed cooperative MAC scheme based on Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol, featuring selective RSU probing and adaptive transmission. It utilizes a dual timescale channel access framework, with a “large-scale” phase accounting for gradual changes in vehicle locations and a “small-scale” phase adapting to rapid channel fluctuations. We propose the RSU Probing and Cooperative Access (RPCA) strategy, a two-stage approach based on dynamic inter-vehicle distances from the RSU. Using optimal sequential planned decision theory, we rigorously prove its optimality in maximizing average system throughput per large-scale phase. For practical implementation in VANETs, we develop a distributed MAC algorithm with periodic location updates. It adjusts thresholds based on inter-vehicle and vehicle-RSU distances during the large-scale phase and accesses channels following the RPCA strategy with updated thresholds during the small-scale phase. Simulation results confirm the effectiveness and efficiency of our algorithm.

This research paper delves into interference mitigation within Low Earth Orbit (LEO) satellite constellations, particularly when operating under constraints of limited radio environment information. Leveraging cognitive capabilities facilitated by the Radio Environment Map (REM), we explore strategies to mitigate the impact of both intentional and unintentional interference using planar antenna array (PAA) beamforming techniques. We address the complexities encountered in the design of beamforming weights, a challenge exacerbated by the array size and the increasing number of directions of interest and avoidance. Furthermore, we conduct an extensive analysis of beamforming performance from various perspectives associated with limited REM information: static versus dynamic, partial versus full, and perfect versus imperfect. To substantiate our findings, we provide simulation results and offer conclusions based on the outcomes of our investigation.

This paper explores Physical-Layer Security (PLS) in Flexible Duplex (FlexD) networks, considering scenarios involving eavesdroppers. Our investigation revolves around the intricacies of the sum secrecy rate maximization problem, particularly when faced with coordinated and distributed eavesdroppers employing a minimum mean square error (MMSE) receiver. Our contributions include an iterative classical optimization solution and a graph neural network (GNNs) based unsupervised learning strategy. To the best of our knowledge, this work marks the initial exploration of GNNs for joint resource allocation for PLS applications. We also extend the GNN approach to address the absence of eavesdroppers' channel knowledge. Extensive numerical simulations highlight FlexD's superiority over half-duplex (HD) communications and the GNN approach's superiority in performance and time complexity over classical methods.

This research introduces Flexible Duplex (FlexD) networks, which offer dynamic time/frequency scheduling for secure uplink and downlink communications. We present novel communication models and analytical tools to evaluate the security aspects of User-User and User-Access Point connections within FlexD networks, thereby bridging the gap between FlexD networks and physical layer security. Our work establishes an analytical framework with closed-form expressions for secrecy outage probability (SOP) in FlexD networks. We also propose a power allocation and user selection strategy to maximize secrecy rates. Additionally, we explore the security performance of FlexD networks when partial channel state information (CSI) and reciprocal CSI channels are available. Extensive simulations validate our theoretical findings and highlight the security benefits compared to traditional half-duplex and full-duplex technologies. Our results suggest that the proposed communication strategies hold promise for securing future tactical wireless networks, including Device-to-Device (D2D), Machine-to-Machine (M2M), and cellular/mobile networks.

This paper investigates the signal detection problem in colored noise with an unknown covariance matrix. In particular, we focus on detecting an unknown non-random signal by capitalizing on the leading eigenvalue of the whitened sample covariance matrix as the test statistic (a.k.a. Roy's largest root test). Since the unknown signal is non-random, the whitened sample covariance matrix turns out to have a non-central F-distribution. This distribution assumes a singular or non-singular form depending on whether the number of observations$p\lessgtr$the system dimensionality$m$. Therefore, we statistically characterize the leading eigenvalue of the singular and non-singular$F$-matrices by deriving their cumulative distribution functions (c.d.f.). Subsequently, they have been utilized in deriving the corresponding receiver operating characteristic (ROC) profiles. We also extend our analysis into the high dimensional domain. It turns out that, when the signal is sufficiently strong, the maximum eigenvalue can reliably detect it in this regime. Nevertheless, weak signals cannot be detected in the high dimensional regime with the leading eigenvalue.

This paper investigates the signal detection problem in colored noise with an unknown covariance matrix. To be specific, we consider a scenario in which the number of signal bearing samples$(n)$is strictly smaller than the dimensionality of the signal space$(m)$. Our test statistic is the leading generalized eigenvalue of the whitened sample covariance matrix (a.k.a.$F- \mathbf{matrix}$) which is constructed by whitening the signal bearing sample covariance matrix with noise-only sample covariance matrix. The sample deficiency (i.e.,$m > n)$in turn makes this$F$-matrix rank deficient, thereby singular. Therefore, an exact statistical characterization of the leading generalized eigenvalue (l.g.e.) of a singular$F-\mathbf{matrix}$is of paramount importance to assess the performance of the detector (i.e., the receiver operating characteristics (ROC)). To this end, we employ the powerful orthogonal polynomial approach to derive a new finite dimensional c.d.f. expression for the l.g.e. of a singular F-matrix. It turns out that when the noise only sample covariance matrix is nearly rank deficient and the signal-to-noise ratio is$O(m)$, the ROC profile converges to a limit.

This paper focuses on achieving optimal multi-user channel access in distributed networks using a reconfigurable intelligent surface (RIS). The network includes wireless channels with direct links between users and RIS links connecting users to the RIS. To maximize average system throughput, an optimal channel access strategy is proposed, considering the trade-off between exploiting spatial diversity gain with RIS assistance and the overhead of channel probing. The paper proposes an optimal distributed Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) strategy with opportunistic RIS assistance, based on statistics theory of optimal sequential observation planned decision. Each source-destination pair makes decisions regarding the use of direct links and/or probing source-RIS-destination links. Channel access occurs in a distributed manner after successful channel contention. The optimality of the strategy is rigorously derived using multiple-level pure thresholds. A distributed algorithm, which achieves significantly lower online complexity at$O(1)$, is developed to implement the proposed strategy. Numerical simulations verify the theoretical results and demonstrate the superior performance compared to existing approaches.

Flexible duplex networks allow users to dynamically employ uplink and downlink channels without static time scheduling, thereby utilizing the network resources efficiently. This work investigates the sum-rate maximization of flexible duplex networks. In particular, we consider a network with pairwise-fixed communication links. Corresponding combinatorial optimization is a non-deterministic polynomial (NP)-hard without a closed-form solution. In this respect, the existing heuristics entail high computational complexity, raising a scalability issue in large networks. Motivated by the recent success of Graph Neural Networks (GNNs) in solving NP-hard wireless resource management problems, we propose a novel GNN architecture, named Flex-Net, to jointly optimize the communication direction and transmission power. The proposed GNN produces near-optimal performance meanwhile maintaining a low computational complexity compared to the most commonly used techniques. Furthermore, our numerical results shed light on the advantages of using GNNs in terms of sample complexity, scalability, and generalization capability.

We propose a new wireless paradigm for railways – transmissive metasurface (TMS) assisted communications. It compensates for the Doppler shift brought by high mobility and reduces signal degradation due to train carriages. Specifically, the elements of the TMS panel attached to train windows manipulate the links between the base station (BS) and the onboard users. One fundamental problem with it is: does the channel introduced by TMS outperform the traditional direct channel? To answer this, we compare the gains of direct and cascaded channels and introduce the cascaded-outperform-direct probability (CODP), which is the probability that the latter exceeds the former. We then derive two simplified closed-form CODP expressions by approximating the cascaded channel gain as mixed Gaussian and Gamma distributions. Moreover, BS-related parameters impact the CODP; we show that the CODP has (i) global maximum points concerning the azimuth angle of the path from the BS to the TMS, the distance from the BS to the railway, and the BS height, respectively, and (ii) a minimum point in terms of the azimuth angle of the path from the BS to the TMS. Finally, we provide numerical results to verify our analysis and derivations.

In this paper, we consider an underlay cognitive radio network assisted by dual-hop decode-and-forward (DF) relaying. For a general multi-user network, we adopt a max-min fairness relay selection scheme and analyse the outage probability when the channels are subject to independent and non-identical Nakagami-m fading. The relay network operates within the constraint imposed on the peak interference power tolerable by the primary receiver. We then analyse the asymptotic outage probability performance and illustrate the existence of i) the full-diversity order when the interference level at the primary user increases proportionally with the relay transmit power; and ii) an outage floor when the transmit powers of the relays are restricted by the primary receiver. We also analyse the outage probability with imperfect channel state information (CSI) and the average throughput over Rayleigh fading channels. Illustrative analytical results are accurately validated by numerical simulations.

Incorporating mobile-edge computing (MEC) in the Internet of Things (IoT) enables resource-limited IoT devices to offload their computation tasks to a nearby edge server. In this article, we investigate an IoT system assisted by the MEC technique with its computation task subjected to sequential task dependency, which is critical for video stream processing and other intelligent applications. To minimize energy consumption per IoT device while limiting task processing delay, task offloading strategy, communication resource, and computation resource are optimized jointly under both slow and fast-fading channels. In slow fading channels, an optimization problem is formulated, which is nonconvex and involves one integer variable. To solve this challenging problem, we decompose it as a 1-D search of task offloading decision problem and a nonconvex optimization problem with task offloading decision given. Through mathematical manipulations, the nonconvex problem is transformed to be a convex one, which is shown to be solvable only with the simple Golden search method. In fast-fading channels, optimal online policies depending on the instant channel state are derived even though they are entangled. In addition, it is proved that the derived policy will converge to the offline policy when the channel coherence time is low, which can help save extra computation complexity. Numerical results verify the correctness of our analysis and the effectiveness of our proposed strategies over the existing methods.

The reconfigurable intelligent surface (RIS) is a promising technology that is anticipated to enable high spectrum and energy efficiencies in future wireless communication networks. This paper investigates optimum location-based RIS selection policies in RIS-aided wireless networks to maximize the end-to-end signal-to-noise ratio for product-scaling and sum-scaling path-loss models where the received power scales with theproductandsumof the transmitter-to-RIS and RIS-to-receiver distances, respectively. These scaling laws cover the important cases of end-to-end path-loss models in RIS-aided wireless systems. The random locations of all available RISs are modeled as a Poisson point process. To quantify the network performance, the outage probabilities and average rates attained by the proposed RIS selection policies are evaluated by deriving the distance distribution of the chosen RIS node as per the selection policies for both product-scaling and sum-scaling path-loss models. We also propose a limited-feedback RIS selection framework to achieve distributed network operation. The outage probabilities and average rates obtained by the limited-feedback RIS selection policies are derived for both path-loss models as well. The numerical results show notable performance gains obtained by the proposed RIS selection policies.

This paper studies the performance and key structural properties of the optimum location-based relay selection policy for wireless networks consisting of homogeneous Poisson distributed relays. The distribution of the channel quality indicator at the optimum relay location is obtained. A threshold-based distributed selective feedback policy is proposed for the discovery of the optimum relay location with finite average feedback load. It is established that the total number of relays feeding back obeys a Poisson distribution and an analytical expression for the average feedback load is derived. The analytical expressions for the average rate and outage probability with and without selective feedback are obtained for general path-loss models. It is shown that the optimum location-based relay selection policy outperforms other common relay selection strategies notably. It is also shown that utilizing location information from five relays on average is enough to achieve almost the same performance with the infinite feedback load case. As generalizations, isotropic Poisson point processes and heterogeneous source-to-relay and relay-to-destination links are also studied.

The novel reconfigurable intelligent surface (RIS) is an emerging technology which facilitates high spectrum and energy efficiencies in Beyond 5G and 6G wireless communication applications. Against this backdrop, this paper investigates two-way communications via reconfigurable intelligent surfaces (RISs) where two users communicate through a common RIS. We assume that uplink and downlink communication channels between two users and the RIS can be reciprocal. We first obtain the optimal phase adjustment at the RIS. We then derive the exact outage probability and the average throughput in closed-forms for single-element RIS. To evaluate multiple-element RIS, we first introduce a gamma approximation to model a product of Rayleigh random variables, and then derive approximations for the outage probability and the average throughput. For large average signal-to-interference-plus-noise ratio (SINR) $\rho$, asymptotic analXsis also shows that the outage decreases at the rate $(\log(\rho)/\rho)$ where L is the number of elements, whereas the throughput increases with the rate $\log(\rho)$.

This paper investigates a three-layer IoT-fog-cloud computing system to determine the optimum workload and power allocation at each layer. The objective is to minimize maximum per-layer latency (including both data processing and transmission delays) with individual power constraints. The resulting optimum resource allocation problem is a mixed-integer optimization problem with exponential complexity. Hence, the problem is first relaxed under appropriate modeling assumptions, and then an efficient iterative method is proposed to solve the relaxed but still non-convex problem. The proposed algorithm is based on an alternating optimization approach, which yields close-to-optimum results with significantly reduced complexity. Numerical results are provided to illustrate the performance of the proposed algorithm compared to the exhaustive search method. The latency gain of three-layer distributed IoT-fog-cloud computing is quantified with respect to fog-only and cloud-only computing systems.

This paper addresses a classical problem in random matrix theory-finding the distribution of the maximum eigen-value of the correlated Wishart unitary ensemble. In particular, we derive a new exact expression for the cumulative distribution function (c.d. f.) of the maximum eigen-value of a 2 × 2 correlated non-central Wishart matrix with rank-l mean. By using this new result, we derive the exact outage probability of 2 × 2 multiple-input multiple-output maximum-ratio-combining (MIMO-MRC) in Rician fading with transmit correlation and a strong line-of-sight (LoS) component (rank-l channel mean). We also show that the outage performance is affected by the relative alignment of the eigen-spaces of the mean and correlation matrices. In general, when the LoS path aligns with the least eigenvector of the correlation matrix, in the high transmit signal-to-noise ratio (SNR) regime, the outage gradually improves with the increasing correlation. Moreover, we show that as K (Rician factor) grows large, the outage event can be approximately characterized by the c.d.f. of a certain Gaussian random variable.

In this paper, we investigate the two-way communication between two users assisted by a reconfigurable intelligent surface (RIS). The scheme that two users communicate simultaneously over Rayleigh fading channels is considered. The channels between the two users and RIS can either be reciprocal or non-reciprocal. For reciprocal channels, we determine the optimal phases at the RIS to maximize the signal-to-interference-plus-noise ratio (SINR). We then derive exact closed-form expressions for the outage probability and spectral efficiency for single-element RIS. By capitalizing the insights obtained from the single-element analysis, we introduce a gamma approximation to model the product of Rayleigh random variables which is useful for the evaluation of the performance metrics in multiple-element RIS. Asymptotic analysis shows that the outage decreases at (log(ρ)/ρ)Lrate where L is the number of elements, whereas the spectral efficiency increases at log(ρ) rate at large average SINR p. For non-reciprocal channels, the minimum user SINR is targeted to be maximized. For single-element RIS, closed-form solution is derived whereas for multiple-element RIS the problem turns out to be non-convex. The latter one is solved through semidefinite programming relaxation and a proposed greedy-iterative method, which can achieve higher performance and lower computational complexity, respectively.

We consider a relay-assisted non-orthogonal multiple access (NOMA) network consisting of a source (S), an energy-constrained relay (R), and two users, where a hybrid power-splitting (PS) and time-splitting (TS) scheme is applied at R for energy harvesting. We provide optimal transmission designs for such a network by jointly optimizing the transmit power of S, the TS and PS ratios, the power allocation ratios at S and R, and the user ordering (indicating which user should apply the successive interference cancellation). In particular, when full channel state information at the transmitters (CSIT) is available, our design can minimize the energy consumption while ensuring that two users correctly receive the desired information. When only partial CSIT is available, our design can minimize the system outage probability. We analytically show that the joint optimal solution for a given TS ratio can be derived in a closed form for both CSIT cases. The optimal TS ratio can be found either in a closed form or using a bisection method for the full CSIT case, while it can be found using a one-dimension search for the partial CSIT case. Finally, numerical results are provided to validate the analytical results and to evaluate the performance advantage of the hybrid PS/TS scheme with the proposed optimal designs.

Signal detection in colored noise with an unknown covariance matrix has a myriad of applications in diverse scientific/engineering fields. The test statistic is the largest generalized eigenvalue (l.g.e.) of the whitened sample covariance matrix, which is constructed via m-dimensional p signal-plusnoise samples and m-dimensional n noise-only samples. A finite dimensional characterization of this statistic under the alternative hypothesis has hitherto been an open problem. We answer this problem by deriving cumulative distribution function (c.d.f.) of this l.g.e. via the powerful orthogonal polynomial approach, exploiting the deformed Jacobi unitary ensemble (JUE). Two special cases and an asymptotic version of the c.d.f. are also derived. With this new c.d.f., we comprehensively analyze the receiver operating characteristics (ROC) of the detector. Importantly, when the noise-only covariance matrix is nearly rank deficient (i.e., m = n), we show that (a) when m and p increase such that m/p is fixed, at each fixed signal-to-noise ratio (SNR), there exists an optimal ROC profile. We also establish a tight approximation of it; and (b) asymptotically, reliable signal detection is always possible if SNR scales with m.

This paper investigates the classical statistical signal processing problem of detecting a signal in the presence of colored noise with an unknown covariance matrix. In particular, we consider a scenario wherem-dimensionalppossible signal-plus-noise samples andm-dimensionalnnoise-only samples are available at the detector. Then the presence of a signal can be detected using the largest generalized eigenvalue (l.g.e.) of the so called whitened sample covariance matrix. This amounts to statistically characterizing the maximum eigenvalue of the deformed Jacobi unitary ensemble (JUE). To do this, we employ the powerful orthogonal polynomial approach to determine a new finite dimensional expression for the cumulative distribution function (c.d.f.) of the l.g.e. of the deformed JUE. This new c.d.f. expression facilitates the further analysis of the receiver operating characteristics (ROC) of the detector. It turns out that, form=n, when m andpincrease such thatm/pis fixed, there exists an optimal ROC profile for each fixed signal-to-noise ratio (SNR). In this respect, we have established a tight approximation for the corresponding optimal ROC profile.

This paper considers a location-based optimal relay selection scheme for a relay-assisted wireless network where available decode-and- forward relays are distributed as a homogeneous Poisson point process. To solve an optimum relay selection problem, a central entity or the source requires information pertaining to all relay locations. Since the task of feeding this information back is impractical, we investigate a threshold-based limited feedback distributed relay selection policy. We show that the total number of relays feeding back is a Poisson distributed random variable. For a given threshold-based limited feedback distributed relay selection policy, we obtain analytical expressions for the average rate and the outage probability over the fading and no-fading communication scenarios. The derived analytical expressions are verified and the performance achieved by the proposed relay selection policy is illustrated through extensive simulations. It is observed that the limited feedback distributed relay selection policy can achieve almost the same performance with the optimum relay selection policy by only utilizing location information from a few number of relays.

This paper considers simultaneous wireless information and power transfer (SWIPT) over a dual-hop dynamic decode-and-forward (DF) relay network with the power-splitting (PS) energy harvesting protocol at the relay. The circuit power consumption (CPC), which includes power requirements for both decoding and encoding circuits, is considered at the relay. For a rate- dependent linear CPC model, we formulate an optimization problem to decide the optimal throughput, PS ratio, relay transmit power and time ratio for the source to relay transmission. Although the resultant optimization problem is nonconvex, we derive an efficient optimization algorithm, requiring significantly less floating point operations than an interior point method. Finally, we present numerical results which lead to some interesting insights for system design.

This paper investigates the location-based relay selection problem, where the source node chooses its relay from a set of spatially deployed decode-and-forward relays. The advantages of location-based relay selection are the elimination of excessive relay switching rate and the feedback reduction avoiding the requirement of having full channel state information at the source node. For a homogeneous Poisson point process of candidate relays, we first derive the distribution for the distance of the relay (relative to the source and destination nodes) selected by the optimum location-based relay selection policy. This result is independent of the functional form of the path-loss function as long as it is a non-increasing function of the transmitter-receiver separation. By utilizing the derived optimum relay distance distribution, we then obtain analytical expressions for the average rate and outage probability by considering the power-law decaying path-loss function for the no-fading and Rayleigh fading communication scenarios. It is observed that the optimum relay selection policy outperforms the other common selection strategies notably, including the ones choosing the relay closest to the source, the relay closest to the destination and the relay closest to the mid-point between source and destination.

Wireless energy transferring technology offers a constant and instantaneous power for low-power applications such as Internet of Things (IoT) to become an affordable reality. This paper considers simultaneous wireless information and power transfer (SWIPT) over a dual-hop decode-and-forward (DF) relay network with the power-splitting (PS) energy harvesting protocol at the relay. The relay is equipped with a finite capacity battery. The system performance, which is characterized by the average success probability of source to destination transmission, is a function of the resource allocation policy that selects the PS ratio and the transmit energy of the relay. We develop a mathematical framework to find an upper bound for the maximum the average success probability. The upper bound is formulated by a discrete state space Markov decision problem (MDP) and make use of a policy iteration algorithm to calculate it.

In this paper we consider wireless energy transfer for a point-to-point link. The energy transmitter sees a finite number of independent channel realizations, and, armed with (causal) knowledge of the channels, must decide how much energy to transmit in each time slot. The objective is to maximize the expected energy transferred to the receiver at the end of the time period. We show that the optimal energy allocation policy is binary: the transmitter sends no energy or all energy in a slot with this decision based on a simple threshold on the channel. As intuition demands, this threshold for transmission decreases as we move closer to the last available time slot. The performance of the optimal scheme is studied both analytically and numerically.

This paper investigates a user-fairness relay selection (RS) problem for decode-and-forward (DF) full-duplex (FD) relay networks, where multiple users cooperate with multiple relays in each coherence time. We consider two residual self-interference (RSI) models with or without direct links. We propose a sub-optimal relay selection (SRS) scheme which requires only the instantaneous channel state information (CSI) of source-to-relay and relay-to-destination links. To evaluate the performance, the outage probability of SRS is derived for different scenarios depending on RSI models and the availability of direct links. To further investigate, asymptotic expressions are derived for the high-transmit power regime. For comparison purposes, 1) the average throughputs of the FD and half-duplex (HD) modes are derived; 2) non-orthogonal transmission is considered and its performance is discussed with approximations; and 3) the impact of imperfect CSI is investigated with the aid of analysis. While simulation results are provided to verify the analytical results, they reveal interesting fundamental trends. It turns out that a significant throughput degradation occurs with FD mode over HD mode when self-interference is fully proportional to the transmit power. Since all users can communicate in the same coherence time with the FD mode, these joint RS schemes are useful for user-fairness low-latency applications.

This paper investigates the relay selection (RS) problem for multi-hop full-duplex relay networks where multiple source-destination (SD) pairs compete for the same pool of relays, under the attack of multiple eavesdroppers. To enhance the physical-layer security, within a given coherence time, our objective is to jointly assign the available relays at each hop to different SD pairs to maximize the minimum secrecy rate among all pairs. Two RS schemes, optimal RS and suboptimal RS (SRS), are proposed for two-hop networks based on global channel state information (CSI) and only SD pairs CSI, respectively. Since all users can communicate within the same coherence time, our joint RS schemes are important for the user-fairness and ultra-reliable low-latency communications. To evaluate the performance, the exact secrecy outage probability of the SRS scheme is derived under two residual self-interference models. The asymptotic analysis shows that the SRS scheme achieves full diversity. A relay-based jamming scheme is also proposed by using unassigned relays for user communications. Finally, the two-hop RS schemes and the analysis are extended to the general multi-hop network with multiple eavesdroppers. The numerical results reveal interesting fundamental trends where the proposed schemes can significantly enhance the secrecy performance.

This paper studies the relay selection (RS) problem for full-duplex (FD) relay networks with multiple source-destination (SD) pairs under the attack of colluding eavesdroppers. Based on available channel state information (CSI), both optimal relay selection (ORS) and suboptimal relay selection (SRS) schemes are considered to maximize the minimum secrecy rate among all pairs in order to enhance the physical-layer security. The secrecy performance of the more practical SRS scheme is then evaluated in terms of intercept probability and diversity order. The SRS achieves full diversity when the gains of the main-to- eavesdropper and the main-to-interference channels increase asymptotically.

A novel opportunistic antenna selection aided spatial modulation, called opportunistic spatial modulation (OSM), is proposed, which exhibits an attractive system reliability enhancement with low complexity. Its unique features enable a comprehensive analytical framework, which is challenging to acquire with existing transmit-antenna-selection-aided spatial modulation (TASS-SM) schemes. Closed-form expression of improved union bound for the average symbol error probability (ASEP) of proposed OSM-MISO system is derived. Furthermore, we compare the proposed OSM with a prevalent existing TASS-SM scheme to confirm the feasibility and effectiveness of our scheme. Simulation results are provided to corroborate the analytical results.

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

This paper proposes an opportunistic spatial modulation (OSM) scheme where the transmit antennas are divided into K ≥ 1 equal groups, and the best antenna from each group is selected to form a K transmit antenna subset for implementing spatial modulation (SM). Thus, the activation of one antenna from the subset to transmit one of the M-ary modulation symbols achieves a data rate of log2(K) + log2(M) bits per channel use. Notably, special cases of OSM include conventional SM and pure single transmit antenna selection. To characterize and comparatively evaluate OSM, we first consider phase-shift keying modulation and derive a closed-form, improved union-bound of symbol error probability (SEP) with a single-antenna receiver. Explicit expressions for the SEP in the high signal-to-noise ratio regime are also presented. Extensions to quadrature amplitude modulation analysis and simulations of multiple-antenna reception case are also provided. Simulation results corroborate the analytical results and reveal the interesting interplay between the signal and spatial constellation diagrams. For a given number of transmit antennas and targeted data rate, asymptotically, the SEP can be minimized by using only one antenna group and the largest size of signal constellation, as this configuration achieves the full-diversity order.

This paper analyzes the performance of a multi-user multihop relay network using a low complexity decentralized relay selection (DRS) scheme for decode-and-forward cooperative networks. We carry out a rigorous diversity order analysis, with Nakagami-m fading and pathloss and show that the DRS scheme achieves full diversity while maintaining a complexity that is quadratic in the number of users, quadratic in the number of relays and independent of the number of hops. For a special case of two-user networks we derive exact closed-form expressions for the outage probability by considering the order statistics. Furthermore, we extend our analysis to consider interfering relay networks and derive an accurate lower bound on the outage of an arbitrary network user. Based on the lower bound we also show how the outage probability saturates in the high signal-to-interference-plus-noise ratio regime. Extensive numerical examples are used to illustrate the accuracy of the analysis and to highlight the use of the DRS scheme in multi-user multihop relay networks.

This paper investigates the relay selection (RS) problem in full-duplex (FD) wireless networks with multiple users and multiple common relays. We consider three self-interference models at FD relays. For amplify-and-forward (AF) and decode- and-forward (DF) relaying, the exact and asymptotic expressions for the outage probability are derived over Rayleigh fading channels with distance-dependent path loss for three RS schemes: i) optimal RS (ORS); ii) naive RS; and iii) random RS. Simulation results are provided to verify the analytical results.

This paper considers an energy harvesting (EH) relay network in which the relay node harvests energy using the hybrid EH protocol, which is a composite of power-splitting (PS) and time- switching (TS) protocols. As an energy storage may help to reduce the randomness of the energy availability at the relay, we consider two cases; i) the relay does not have a long-term energy storage, and ii) the relay has an infinite capacity long-term energy storage. By maximizing the average success probability, the optimal PS ratio is analytically derived, and the optimal TS ratio is numerically calculated based on the optimal PS ratio. Numerical results of the success probability are validated with simulations.

In this paper, we analyze the outage and diversity performance of a low-complexity relay selection routing algorithm which applies to large-scale distributed decode-and-forward relay networks with two source-destination user pairs. We analyze a suboptimal decentralized relay selection (DRS) strategy that only utilizes local channel state information of the relays within a given hop, to select distinct multihop paths for each user pair. Specifically, we derive exact closed-form expressions for the outage probability and diversity order of the DRS algorithm which prove that the full diversity order is achieved with complexity that is quadratic with the number of relays in each hop. Illustrative analytical results are accurately validated by numerical simulations.

Wireless energy harvesting is one of the promising alternative techniques to power wireless networks. To improve the energy harvesting capability, energy beamforming has recently drawn significant attention. In this paper, we consider energy harvesting in a multi-user multi-input single-output (MU-MISO) network, and investigate two energy beamforming schemes which ensure fairness among the energy levels harvested by all users in the network. In particular, we investigate how to maximize the minimum harvested energy in the network by using i) a single beamforming vector; and ii) multiple beamforming vectors. We solve these problems by using semidefinite programming. The performance of different beamforming techniques is discussed with the aid of numerical simulations.

In this paper, we consider a relay network over a flat-fading channel, where the relay has no fixed power supply and thus needs to replenish energy via wireless energy harvesting (EH) from the signals transmitted by the source. We propose a novel hybrid protocol, which is a combination of existing EH protocols, such as power splitting (PS) and time switching (TS). We formulate the optimization problems and derive some explicit results. In particular, we derive the optimal PS and TS ratios at the relay for all three EH protocols to achieve the maximum throughput for information transfer from the source to the destination for both decode-and-forward and amplify-and-forward relaying schemes. We show that the proposed hybrid protocol outperforms both PS and TS protocols.

We consider a wireless energy harvesting (EH) relay network. Relays without embedded energy supply harvest energy from the source node. Considering a time switching protocol, performance measures such as average signal-to-noise ratio (SNR), outage and throughput are analyzed. Subsequently, optimal EH time is selected in order to maximize the throughput. Then, a multiple-relay network is considered with relay selection, which can achieve full diversity at any EH time. All theoretical results are validated by numerical simulations.

We consider a wireless energy harvesting (EH) relay network, in which the relay harvests energy from the source node. Considering a power splitting (PS) protocol, performance measures such as average signal-to-noise ratio (SNR), outage probability and throughput are analyzed. Subsequently, optimal PS ratios are analytically derived in order to maximize the instantaneous SNR and throughput. All theoretical results are validated by numerical simulations.

This paper investigates the relay selection (RS) problem in networks with multiple users and multiple common amplify-and-forward (AF) relays. We first give an optimality measure for RS in multiple-user relay networks. An optimal RS (ORS) algorithm is then provided, which is an extension of an RS scheme in the literature that maximizes the minimum end-to-end receive signal-to-noise ratio (SNR) of all users. The complexity of the ORS is quadratic in both the number of users and the number of relays. A suboptimal RS (SRS) scheme is also proposed, which has linear complexity in the number of relays and quadratic complexity in the number of users. Furthermore, diversity orders of both the ORS and the proposed SRS are derived and compared with those of a naive RS scheme and the single-user case. The ORS is shown to achieve full diversity, while the diversity order of the SRS decreases with the number of users. For two-user networks, the closed-form outage probabilities and array gains corresponding to the minimum SNR of the users in the RS schemes are derived. It is proved that the advantage of the SRS over the naive RS scheme increases as the number of relays in the network increases. Simulation results are provided to corroborate the analytical results.

This paper studies relay selection schemes for two-way amplify-and-forward (AF) relay networks. For a network with two users that exchange information via multiple AF relays, we first consider a single-relay selection (SRS) scheme based on the maximization of the worse signal-to-noise ratio (SNR) of the two end users. The cumulative distribution function (CDF) of the worse SNR of the two users and its approximations are obtained, based on which the block error rate (BLER), the diversity order, the outage probability, and the sum-rate of the two-way network are derived. Then, with the help of a relay ordering, a multiple-relay selection (MRS) scheme is developed. The training overhead and feedback requirement for the implementation of the relay selection schemes are discussed. Numerical and simulation results are provided to corroborate the analytical results.

As required in the IEEE 802.22 proposal, spectrum sensing techniques should be capable enough to sense the primary signal with very low receiver sensitivity such as at -116 dBm. In this paper, the detection performance of an energy detector used for spectrum sensing in cognitive radio networks is investigated under such very low signal-to-noise ratio (SNR) levels. The analysis focuses on the derivation of a closed-form expression for the average missed-detection probability over Rayleigh fading and Nakagami-m fading channels. Subsequently, the detection threshold is optimized for minimizing the total error rate. The analysis is validated by numerical and simulation results. The sensing requirements defined in IEEE 802.22 are also discussed with numerical examples.

Detection performance of an energy detector used for cooperative spectrum sensing in a cognitive radio network is investigated over channels with both multipath fading and shadowing. The analysis focuses on two fusion strategies: data fusion and decision fusion. Under data fusion, upper bounds for average detection probabilities are derived for four scenarios: 1) single cognitive relay; 2) multiple cognitive relays; 3) multiple cognitive relays with direct link; and 4) multi-hop cognitive relays. Under decision fusion, the exact detection and false alarm probabilities are derived under the generalized "k-out-of-n" fusion rule at the fusion center with consideration of errors in the reporting channel due to fading. The results are extended to a multi-hop network as well. Our analysis is validated by numerical and simulation results. Although this research focuses on Rayleigh multipath fading and lognormal shadowing, the analytical framework can be extended to channels with Nakagami-m multipath fading and lognormal shadowing as well.

Composite fading (i.e., multipath fading and shadowing together) has increasingly been analyzed by means of the K channel and related models. Nevertheless, these models do have computational and analytical difficulties. Motivated by this context, we propose a mixture gamma (MG) distribution for the signal-to-noise ratio (SNR) of wireless channels. Not only is it a more accurate model for composite fading, but is also a versatile approximation for any fading SNR. As this distribution consists of N (≥ 1) component gamma distributions, we show how its parameters can be determined by using probability density function (PDF) or moment generating function (MGF) matching. We demonstrate the accuracy of the MG model by computing the mean square error (MSE) or the Kullback-Leibler (KL) divergence or by comparing the moments. With this model, performance metrics such as the average channel capacity, the outage probability, the symbol error rate (SER), and the detection capability of an energy detector are readily derived.

This paper investigates detection capability of energy detectors. With the help of receiver operating characteristics (ROC) curve and area under the ROC curve (AUC), a new measure, Complementary AUC (CAUC), is introduced as a proxy for the overall detection capability. When relays are available to help forward the target signal, the upper bound of the CAUC under Rayleigh fading channels is derived without and with a direct path. In addition, the average CAUC is discussed for Nakagami-m fading channels without and with diversity combining. The analytical results are validated by numerical examples.

The Nakagami-lognormal distribution is the commonly used composite distribution for modeling multipath fading and shadowing. In this paper, simple and new form of distribution which can accurately represent both the mutlipath fading and shadowing effects is introduced. The signal-to-noise ratio (SNR) of the Nakagami-lognormal distribution follows the gamma-lognormal distribution, which is accurately approximated by a weighted mixture of gamma distributions. We show how the weights and other parameters of the summands are obtained. Further, accuracy of the mixture distribution is compared with the KGdistribution - a popular approximation of the Nakagami-lognormal distribution.

A simple figure of merit to describe the performance of an energy detector is desirable. The area under the receiver operating characteristic (ROC) curve, denoted (AUC), is such a measure, which varies between 1/2 and 1. If the detector's performance is no better than flipping a coin, then the AUC is 1/2 , and it increases to one as the detector performance improves. However, in the wireless literature, the AUC measure has gone unnoticed. In this paper, to address this gap, we comprehensively analyze the AUC of an energy detector with no-diversity reception and with several popular diversity schemes. The channel model is assumed to be Nakagami-m fading. First, the average AUC is derived for the case of no-diversity reception. Second, the average AUC is derived for diversity reception cases including maximal ratio combining (MRC), square-law combining (SLC) and selection combining (SC). Further, for Rayleigh fading channels, the impacts of channel estimation errors and fading correlations are analyzed. High SNR (signal-to-noise ratio) approximations and the detection diversity gain are also derived. The analytical results are verified by numerical computations and by Monte-Carlo simulations.

This paper analyzes the performance of an energy detector over wireless channels with composite multipath fading and shadowing effects. These effects are modeled by using the K and K_G channel models. Closed-form average detection probabilities are derived for both K and K_G channel models for the no-diversity reception case. A simple approximation is also derived for large values of energy threshold in the energy detector. The analysis is then extended to cases with diversity receptions including maximal ratio combining (MRC) and selection combining (SC). Analytical results are verified by Monte Carlo simulation and by numerical methods. Receiver operating characteristic (ROC) curves are presented for different degrees of multipath fading and shadowing. Finally, the Rayleigh-lognormal distribution and the K distribution are numerically compared, and the validity of the K channel model for representing the impact of shadowing on the performance of energy detection is affirmed.

In this paper, we exploit cooperative spectrum sensing technique for applications in a relay based cognitive radio network. Relays are assigned in cognitive radio networks to transmit the primary user's signal to a cognitive coordinator. This research is focused on the detection of primary user in single or multiple cognitive relay scenarios. The performance of energy detector is analyzed for independent Rayleigh fading channels. False alarm and detection probabilities are derived theoretically with or without direct communication between the primary user and the cognitive coordinator. An upper bound is also given for detection probability. Our analysis is validated by numerical and simulation results.

In this paper, we examine a general time-division multiple-access (TDMA) cooperative protocol. The wireless network consists of a source terminal, a destination terminal and n - number of intermediate relay terminals that operate in amplify-and-forward (AF) mode with a fixed-gain. Starting with the moment generating function (MGF) of the end-to-end output signal-to-noise ratio (SNR), the symbol error rate (SER) performance of M-ary phase shift keying (M-PSK) modulation schemes is theoretically investigated over independent and identical (i.i.d) Nakagami-m fading environments with maximal ratio combining (MRC) scheme at the destination. Subsequently, numerical and simulation results are provided to verify the accuracy of the formulation.