In this paper, we consider a federated learning (FL)-assisted fifth-generation (5G) resource block (RB) allocation scheme for terrestrial users. Enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC) users calculate their possible achievable uplink data rate with its local neural engine based on underlying channel conditions and packet availability at their data queue. Each user conveys this information to the 5G base station (BS), which then allocates the RB to the user that best meets its service requirements, thereby improving the network throughput. Each user trains its neural engine with either baseline clustered FL or our proposed peer-to-peer (P2P) FL. In clustered FL, clients are grouped by local model similarity, implicitly capturing user types such as eMBB, URLLC, and mMTC. In our P2P FL scheme, clients update their models through exchanges with similarity-selected peers, using similarity-adaptive weights. Simulation results demonstrate the effectiveness of our proposed FL approaches in scheduling users for transmission over conventional baseline schemes.

In this paper, we develop a secure cell-free massive multiple-input multiple-output (CF-mMIMO) communication system assisted by a passive reconfigurable intelligent surface (RIS). In this model, multiple mobile unmanned aerial vehicle access points (UAV-APs) are tasked with communicating with multiple legitimate users (UEs), while multiple jammers are in charge of sending interference signals to UEs. This work proposes a unique framework to maximize the average sum secrecy rate (ASSR) by jointly optimizing the UAV’s trajectory, total transmit power at UAV-APs, and RIS phase shift. Ensemble global optimization (EGO) is implemented followed by deep learning (DL) techniques for optimization with low computational complexity. In particular, we train a deep neural network offline, to optimize the RIS phase shift, UAV-AP trajectory, and UAV-AP transmission powers online. A real-time optimization capability of the trained model is assessed online with low computational complexity. The numerical results show the performance analysis of the best neural network for real-time optimization. Furthermore, numerical results demonstrate that passive RIS technology and increased UAV-AP speed significantly enhance the performance of the cell-free communication system.

Open Radio Access Network (O-RAN) architectures are driving a shift towards modular, software-defined RANs, where virtualization is key for flexible component development. OpenAirInterface (OAI) provide software-based RAN functionalities, complemented by Near-Real-Time RAN Intelligent Controllers (Near-RT RICs) like FlexRIC. However, the performance of these virtualized environments is fundamentally limited by the underlying compute infrastructure. This study establishes a crucial performance baseline by isolating the impact of raw computational constraints within a virtualized O-RAN testbed composed of OAI simulators and the FlexRIC controller. To achieve this, our setup deliberately excludes radio virtualization middleware, ensuring all signal processing and channel emulation to be offloaded to the CPU. We assess the impact of these constraints on the end-user’s Quality of Experience (QoE) by streaming video and audio files to a client within the simulated UE’s network namespace. Our findings reveal that video quality degrades significantly, as reflected in low VMAF, while audio quality holds steady under the same constraints.

Detecting and mitigating poisoning attacks in Federated Learning (FL) poses a significant challenge, as malicious clients can severely impair model performance through adversarial updates. In this work, we present Dynamic Ensemble Clustering for Poison-Aware Federated Learning (DECPA-FL), a novel defense framework that operates at both the client and sample levels. Our approach leverages three dynamic model clusters—normal, poison, and hybrid—each designed to address varying data properties. Unlike conventional FL methods that treat all client input identically, DECPA-FL utilizes an adaptive Isolation Forest mechanism that progresses via federated rounds to identify poisoned samples with enhanced accuracy. The system’s Poisoning-Aware Loss Function provides reduced weights to potentially contaminated data, while its adaptive learning rate mechanism adjusts training parameters based on identified poison ratios. We evaluate DECPA-FL on the CICIDS 2017 network intrusion dataset and achieve an F1-score of 96.06%, outperforming centralized baselines by 8.25% and traditional FL by 18.83%. Our method maintains robust performance even under 15% poisoning rates, where existing approaches suffer substantial degradation. Through DECPA-FL, we offer an effective and resilient defense for secure federated learning in adversarial and privacy-critical domains.

Cell-free massive multiple-input multiple-output (mMIMO) systems distribute a large number of access points (APs) over a wide geographic region, improving coverage, capacity, and user experiences notably. Similarly, reconfigurable intelligent surface (RIS) allows a significant leap forward in signal quality and energy efficiency by adding additional flexibility and intelligent control over the propagation environment of wireless systems. However, optimal resource allocation in RIS-integrated cell-free mMIMO systems faces substantial challenges due to the presence of a large number of APs and passive reflecting elements (PREs) at RIS while simultaneously providing network coverage to aerial and ground users. To efficiently navigate the high-dimensional optimization space, this paper proposes a novel framework for optimal resource allocation in RIS-assisted cell-free mMIMO systems employing deep learning (DL) techniques. In particular, we train a deep neural network offline to optimize the transmit powers at APs and the phase shift of PREs at RIS. The trained model is tested online to perform real-time optimization with low computational complexity. Simulation results demonstrate the effectiveness of the proposed data-driven resource allocation scheme while comparing it with the state-of-the-art baseline scheme.

In this paper, we design a physical (PHY)-layer digital twin model, called PHY-Layer twin by leveraging ray-tracing (RT) and deep learning (DL) for a reconfigurable intelligent surface (RIS) assisted cell-free wireless communication system. Specifically, a recurrent neural network (RNN) driven low-complexity resource allocation scheme is studied to optimize system resources by interfacing with the proposed PHY-Layer twin. The proposed PHY-Layer twin-assisted resource allocation scheme jointly learns the correlation between channel gains obtained from RT and the real-world environment, and it allocates optimal transmit power for access points (APs) of the cell-free wireless networks. Numerical results demonstrate that the proposed scheme outperforms the related state-of-the-art approaches while operating with lower computational overhead.

With UAVs on the rise, accurate detection and identification are crucial. Traditional unmanned aerial vehicle (UAV) identification systems involve opaque decision-making, restricting their usability. This research introduces an RF-based Deep Learning (DL) framework for drone recognition and identification. We use cutting-edge eXplainable Artificial Intelligence (XAI) tools, SHapley Additive Explanations (SHAP), and Local Interpretable Model-agnostic Explanations(LIME). Our deep learning model uses these methods for accurate, transparent, and interpretable airspace security. With 84.59% accuracy, our deep-learning algorithms detect drone signals from RF noise. Most crucially, SHAP and LIME improve UAV detection. Detailed explanations show the model's identification decision-making process. This transparency and interpretability set our system apart. The accurate, transparent, and user-trustworthy model improves airspace security.

Healthcare industries face challenges when experiencing rare diseases due to limited samples. Artificial Intelligence (AI) communities overcome this situation to create synthetic data which is an ethical and privacy issue in the medical domain. This research introduces the CAT-U-Net framework as a new approach to overcome these limitations, which enhances feature extraction from medical images without the need for large datasets. The proposed framework adds an extra concatenation layer with downsampling parts, thereby improving its ability to learn from limited data while maintaining patient privacy. To validate, the proposed framework’s robustness, different medical conditioning datasets were utilized including COVID-19, brain tumors, and wrist fractures. The framework achieved nearly 98% reconstruction accuracy, with a Dice coefficient close to 0.946. The proposed CAT-U-Net has the potential to make a big difference in medical image diagnostics in settings with limited data.

In this paper, we consider a cell-free massive multiple input multiple output (CF-MMIMO) communication system, where users are supported by access points (AP) in conjunction with intelligent reflecting surfaces (IRS) mounted on unmanned aerial vehicles (UAVs). Although aerial IRS (aIRS) offers agile support for expanding network coverage in CF communication systems, the effective operation of such a complex network necessitates a channel state information (CSI) acquisition scheme that exhibits low run-time computational complexity. We propose an artificial intelligence (AI)-based approach to design and develop an efficient channel prediction scheme for CSI acquisition in the CF-MMIMO network supported by aIRS considered. Simulation results demonstrate the effectiveness of the proposed scheme in predicting channel gains across a wide range of signal-to-noise ratios (SNR) while maintaining low computational complexity during real-time operations.

In the realm of the Internet of Things, reconfigurable intelligent surfaces (RISs) have emerged as a pivotal technology, offering unprecedented opportunities to enhance signal quality, coverage, and energy efficiency as part of the ongoing pursuit to overcome the limitations of conventional wireless communication systems. In this context, this paper focuses on the analysis of performance in an integrated air-to-underwater network under the amplified-and-forward relay with variable gain, specifically examining the impact of RISs on a mixed terahertz-underwater optical communication system. This study utilizes the α-μ distribution to characterize the fading effects and pointing error on the THz signal. On the other hand, the underwater turbulence on the optical signal is modeled using the mixture of Exponential Generalized Gamma distribution with pointing error impairments. To provide a basis for comparison, the heterodyne detection technique and the intensity modulation with the direct detection technique are also incorporated. Therefore, analytical expressions of outage probability, average bit error rate, and average channel capacity are demonstrated in terms of the Meijer-G function. To provide more insights, high signal-to-noise approximations of these metrics are also presented. Furthermore, the impact of various modulation schemes, fading severity, pointing errors, atmospheric turbulence conditions, and receiver detection techniques are inspected on the system performance. Finally, the analytical findings are validated through Monte-Carlo simulations, ensuring the robustness of the results.

Pilot contamination poses a critical challenge for channel estimation in dense cell-free (CF) distributed multiple-input multiple-output (CF-DMIMO) wireless networks. State-of-the-art channel estimation schemes require inversion of a high-dimensional channel covariance matrix, which is practically infeasible for dense CF-DMIMO networks owing to the requirement of large storage and high dimensional computational complexity. In this work, we investigate channel estimation problem for a CF-DMIMO network, where both terrestrial and aerial users are jointly supported by distributed access points. We formulate the problem of estimating channel coefficients from the received in-phase/quadrature (I/Q) samples as a non-linear regression problem and propose two deep-learning aided channel estimation schemes for the considered network, namely, deep model-agnostic neural network (DMANN) and deep successive contamination cancellation (DSCC) schemes. Compared to the state-of-the-art channel estimation schemes for CF-DMIMO networks, the proposed schemes (i) tackle the unavoidable pilot contamination issue in dense CF-DMIMO networks while estimating the channel gains for both terrestrial and aerial users; (2) does not require prior knowledge of signal-to-noise ratios; and (3) works well in the presence of non-Gaussian correlated noise. Simulation results demonstrate the effectiveness of the proposed schemes over state-of-the-art channel estimation schemes in various use cases of the CF-DMIMO networks.

Advanced Persistent Threat (APT) has dramatically changed the landscape of cybersecurity. APT is carried out by stealthy, continuous, sophisticated, and well-funded attack processes for long-term malicious gain thwarting most current defense mechanisms. There is a need for a defense strategy that continuously combats APT over a long time-span in imper-fect/incomplete information on attacker's actions. We propose the stochastic evolutionary game model to simulate the dynamic adversary to address this need in this work. We add the player's rationality parameter c to the Logit Quantal Response Dynamics (LQ RD) model to quantify the cognitive differences of real-world players. We propose an optimal decision-making plan by calculating the stable evolutionary equilibrium that balances a trade-off between defense cost and benefit. Cases studies conducted on Energy Delivery Systems (EDS) indicate that the proposed method can help the defender predict possible attack action, select the related optimal cyber defense remediation over time, and gain the maximum defense payoff.

Thanks to rapid advancements in microprocessors, battery technologies, and lightweight materials, unmanned aerial vehicles (UAVs), commonly known as drones, have received signif-icant interest in the past few years. As drone-related commercial and civilian applications are flourishing, Internet-of-Drones (IoD) is moving into the fast lane and quickly becoming a highly anticipated network paradigm, where drones and Zone Service Providers (ZSPs) coordinate knowledge sharing in a reliable, accurate, and efficient way. However, for the sake of both strategic and financial value to business and mission critical applications, it is of vital importance to address both data security and privacy preservation issues brought by drones' inherent resource constraints and wide-open wireless medium. In this paper, we propose a secure data collection and storage mechanism, also called SecureIoD, for the IoD environment. In SecureIoD, drones and ZSPs first mutually authenticate each other and establish a secure session key before sharing any sensitive data via an insecure wireless channel. Then, ZSPs pack the collected data into blocks and compete to add their blocks into the blockchain. We also propose a joint Proof-of- Work (PoW) and Proof-of-Stake (PoS) consensus mechanism to select the miner ZSP, where the more transactions are in the block, the easier a ZSP can solve the cryptographic puzzle. We present security verification and analysis to show that SecureIoD can resist various security attacks. Finally, we develop a real-world testbed, implement SecureIoD and existing SDDM and BACSIoD schemes, and carry out extensive simulation experiments for performance evaluation and analysis. Experimental results reveal that not only does SecureIoD have lower computation cost, energy consumption, miner selection time, and communication overhead, but also offer better security features and capabilities.

With accelerated advances in various technologies, drones, better known as unmanned aerial vehicles (UAVs), are increasingly commonplace and consequently have a more pronounced impact on society. For example, Internet of Drones (IoD), a new communication paradigm offering fundamental navigation assistance and access to information, has widespread applications ranging from agricultural drones in farming to surveillance drones in the COVID-19 pandemic. The increasingly prominent role of IoD in our society also reinforces the importance of securing such systems against various data privacy and security threats. Operationally, it can be challenging to adopt conventional off-the-shelf security products in an IoD system due to the underpinning characteristics of drones (e.g., dynamic and open communication channel). Therefore in this article, we propose a lightweight and privacy-preserving mutual authentication and key agreement protocol, hereafter referred to as PMAP. The latter uses a physical unclonable function (PUF) and chaotic system to support mutual authentication and establish a secure session key between communication entities in the IoD system. To be specific, PMAP consists of two schemes, namely: 1)${\mathrm{ PMAP}}^{D2Z}$(that mutually authenticates drone and zone service provider (ZSP) and establishes secure session keys) and 2)${\mathrm{ PMAP}}^{D2D}$(that mutually authenticates drones and establishes secure session keys). In addition, PMAP supports conditional privacy preserving so that the genuine identity of drones can only be revealed by trusted ZSPs. We evaluate the security of PMAP using automated validation of Internet security protocols and application (AVISPA), as well as provide formal and informal security analysis to show the resilience of PMAP against various security attacks. We also evaluate the performance of PMAP through extensive experiments and compare its performance with existing AKA and IBE-Lite schemes, whose findings show that PMAP achieves better performance in terms of computation cost, energy consumption, and communication overhead.

As a major building block of Healthcare 4.0, wireless body area networks (WBANs) play an important role in collecting patient’s real-time physical phenomena through small wearable or implantable intelligent medical devices and communicating with remote medical experts using short-range wireless communication techniques. However, the challenges of securing information access are partly evidenced by the difficulty in designing secure and efficient security protocols. For example, existing authentication and key agreement schemes have either potential security vulnerabilities or high communication and computation overhead. In this article, we propose a lightweight and anonymous authentication and key agreement protocol, also called liteAuth, for WBANs. In our approach, mutual authentication and session key agreement are achieved using the Tinkerbell map-based random shuffling, physical unclonable function, one-way hash function, and bitwise exclusive OR operation. The security of liteAuth is first verified using the AVISPA tool, and then its cyber resilience is analyzed. In addition, we develop a real-world testbed, implement liteAuth and two existing schemes (i.e., PSLAP and HARCI), and conduct experiments for performance evaluation and analysis. Experimental results indicate that liteAuth can improve the performance of communication overhead and computation time as well as reduce energy consumption, while meeting all security requirements.

In this paper, we derive accurate outage probability and bit error rate expressions for a high altitude platform station (HAPS) assisted terrestrial communication system. In particular, HAPS is deployed as a relay node to assist two ground stations for data transmission. Each ground station-to-HAPS communication link works on a hybrid radio frequency (RF) and free space optics (FSO) mode. Selection combining is performed at the HAPS and the destination ground station to select either RF or FSO link based on the instantaneous channel signal-to-noise ratio (SNR). The derived expressions provide insights on system design and assist analyzing HAPS-terrestrial integrated network supported by hybrid RF/FSO system. The accuracy of the derived outage probability and bit error rate expressions are validated with extensive computer simulations.

We derive non-random and Bayesian Cramer-Rao lower bound (CRLB) for pilot-aided joint estimation of angle-of-arrival (AoA), angle-of-departure (AoD), and small scale channel gain in millimeter-wave (mmW) communication networks, in which transmitter and receiver are equipped with uniform planar antenna (UPA) arrays. Numerical results reveal that Bayesian CRLB decreases with an increase in Rice factor. Furthermore, using the derived Bayesian CRLB, we also observe that uniform design of beamforming and beamcombining codebooks, in which the angles are uniformly quantized, yield better performance than other types of codebook design.

In this work, we develop a deep learning (DL) based robust data detection algorithm for a receive-diversity system, where the data is corrupted by Gaussian and different non-Gaussian noise and interference in different diversity branches. A fully-connected deep neural network (DNN) is designed for this purpose and is trained with different noisy datasets offline. The developed DNN is then applied in real-time for data detection process. We emphasize that the detector does not require the noise distribution to be known for its operation. Furthermore, by the use of simulations, we show that the proposed DL-based detector performs much better than the conventional maximal ratio combining (MRC) detector for non-Gaussian noise. Moreover, simulation results point out that the proposed detector is robust enough to operate in time-varying noisy environment.

This paper studies stochastic power control problems over a fading channel, where the transmitter randomly harvests renewable energies from environment and stores them in a battery for future data transmissions. Moreover, data packets are assumed to arrive at the data queue of transmitter with constant rate μ. To incorporate delay quality-of-service guarantees, two delay constraint models are separately considered, namely average delay model with maximum average delay constraint and statistical delay model with maximum delay outage probability constraint. Under each delay constraint model, the stochastic power control problem aims at maximizing μ considering the randomness of channel fading and energy harvesting (EH) processes. The resulting optimization problems can be formulated as infinite-horizon Markov decision processes. Under average delay model, the optimal power control policy needs to keep track of current data queue-length state in addition to the battery state. On the other hand, under statistical delay model, a sufficiently large queue-length region is assumed, hence, the optimal policy does not depend on the data queue-length state. We study various structural properties of the optimal control policies and develop online power control algorithms that converge to the optimal solutions without requiring statistical knowledge of channel fading and EH processes. By defining and learning the so-called post-decision state-value functions, the proposed learning algorithms require less complexity and converge faster than the conventional reinforcement learning algorithms. Numerical results demonstrate the effectiveness of the online learning algorithms for different delay constraint models and EH settings.

This paper studies optimal stochastic power control problem for a time-varying communication link, where the transmitter randomly harvests renewable energies from the environment. The harvested energies are stored in an energy buffer (or battery). Packets arrive at the transmitter data buffer with a constant rate μ. The objective is to maximize μ under the statistical delay and energy harvesting (EH) constraints. In order to study the optimal power control policy, we reformulate the problem as an infinite-horizon Markov decision process (MDP) using asymptotic delay analysis. The optimal policy and its structural properties are studied by employing the post-decision framework approach. We then propose an online power control algorithm, which converges to the optimal solution without requiring the statistical knowledge of the channel fading and EH processes. Numerical results demonstrate the effectiveness of the online algorithm for different delay constraints and EH settings.

We study the stochastic user scheduling and power control problem for an uplink multi-user network over time-varying channels, where the users randomly harvest renewable energies from the environment. For each user, the renewable energies and arriving data packets with a constant rate are stored in energy (battery) and data buffers, respectively. Users have statistical packet delay constraints in terms of maximum acceptable delay-outage probabilities. We classify the users as prioritized and non-prioritized users. Our goal is to maximize the arrival rate of the non-prioritized user while supporting the minimum data rate requirements for the prioritized users. We reformulate the problem as an infinite-horizon Markov decision process (MDP) using asymptotic delay analysis and study the optimal scheduling and power control policy. Since the optimal policy requires centralized processing with high computational complexity, we develop a reduced-complexity distributed algorithm, which can be implemented at each individual user. Online algorithm is devised, which does not require the statistical knowledge of the channel fading and energy harvesting (EH) processes. Numerical results demonstrate the effectiveness of the centralized and distributed schemes for different delay constraints and EH settings.

In this paper, we consider both channel state uncertainty and harvested energy state uncertainty for a source-relay-destination communication link where the source and the relay are equipped with hybrid energy sources. Taking into account these uncertainties is of important for practical energy harvesting (EH) communication. While channel state uncertainties also affect conventional communication systems and have been widely studied, harvested energy state uncertainties are specific to energy harvesting systems and have not been considered in the literature before. The considered hybrid energy sources include a constant energy source and an energy harvester. Our objective is to maximize the worst case system throughput over a finite number of transmission intervals. We propose robust optimal offline, optimal online, and suboptimal online power allocation schemes. The offline power allocation design is formulated as an optimization problem which can be solved optimally. For the online case, we propose a dynamic programming (DP) approach to compute the optimal transmit power. To alleviate the prohibitively high complexity inherent to DP, we also propose several suboptimal low-complexity online power allocation schemes. Simulation results confirm the robustness of the proposed power allocation schemes to channel and energy state uncertainties.

In this paper, we consider optimal power allocation for conventional and buffer-aided link adaptive energy harvesting (EH) relay systems, where an EH source communicates with the destination via an EH decode-and-forward relay {over fading channels}. In conventional relaying, source and relay transmit signals in consecutive time slots whereas in buffer-aided link adaptive relaying, the state of the source-relay and relay-destination channels {as well as the amounts of energy available at source and relay} determine whether the source or the relay is selected for transmission. Our objective is to maximize the system throughput over a finite number of transmission time slots for both relaying protocols. In case of conventional relaying, we propose an offline and several online joint source and relay transmit power allocation schemes. For offline power allocation, we formulate {a convex optimization problem} whereas for the online case, we propose a dynamic programming (DP) approach to compute the optimal online transmit power. To alleviate the complexity inherent to DP, we also propose several suboptimal online power allocation schemes. For buffer-aided link adaptive relaying, we show that the joint offline optimization of the source and relay transmit powers along with the link selection results in a mixed integer non-linear program which we solve optimally using the spatial branch-and-bound method. We also propose efficient online power allocation schemes for buffer-aided link adaptive relaying. Simulation results show that buffer-aided link adaptive relaying provides significant performance gains compared to conventional relaying but requires a higher complexity for computation of the power allocation solution. We also show that buffer-aided link adaptive relaying is more robust to changes in the EH rate than conventional relaying.

In this work, we consider a point-to-point link where the transmitter has a hybrid supply of energy, i.e., the energy is supplied by a constant energy source and an energy harvester, which harvests energy from its surrounding environment. Our goal is to jointly minimize the power consumed by the constant energy source and any possible waste of the harvested energy to ensure their optimum utilization for transmission of a given amount of data in a given number of time intervals. Two scenarios are considered for packet arrival. In the first scenario, we assume that all data packets have arrived before the transmission begins, whereas in the second scenario, we assume that data packets are arriving during the course of data transmission. For both scenarios, we propose optimal offline transmit power allocation schemes which provide insight on how to efficiently consume the energy supplied by the constant energy source and the energy harvester.

In this work, we consider a point-to-point communication link where the transmitter has a hybrid supply of energy. Specifically, the hybrid energy is supplied by a constant energy source and an energy harvester, which harvests energy from its surrounding environment and stores it in a battery which suffers from energy leakage. Our goal is to minimize the power consumed by the constant energy source for transmission of a given amount of data in a given number of time intervals. Two scenarios are considered for packet arrival. In the first scenario, we assume that all data packets have arrived before transmission begins, whereas in the second scenario, we assume that data packets are arriving during the course of data transmission. For both scenarios, we propose an optimal offline transmit power allocation scheme which provides insight into how to efficiently consume the energy supplied by the constant energy source and the energy harvester. For offline power allocation, we assume that causal and non-causal information regarding the channel and the amount of harvested energy is available a priori. For optimal online power allocation, we adopt a stochastic dynamic programming (DP) approach for both considered scenarios. For online power allocation, only causal information regarding the channel and the amount of harvested energy is assumed available. Due to the inherent high complexity of DP, we propose suboptimal online algorithms which are appealing because of their low complexity. Simulation results reveal that the offline scheme performs best among all considered schemes and the suboptimal online scheme provides a good performance-complexity tradeoff.

Using energy harvesting nodes can be a viable solution for energy limited cooperative communication systems. In this paper, we consider the optimization of an energy harvesting (EH) relay system, where an EH source communicates with the destination via an EH decode-and-forward (DF) relay. Our objective is to maximize the system throughput over a finite number of transmission intervals. To this end, we propose an offline and several online joint source and relay transmit power allocation schemes. For offline power allocation, we formulate a convex optimization problem which can be solved either in closed form or using standard optimization tools. For the online case, we propose a dynamic programming (DP) approach to compute the optimal online transmit power. To alleviate the complexity inherent to DP, we propose several suboptimal online power allocation schemes. Our simulation results show that the developed suboptimal schemes provide a good complexity-performance tradeoff compared to optimal online power allocation.

In this paper, we provide a high signal-to-noise ratio (SNR) error rate analysis of amplify-and-forward (AF) best relay selection in networks impaired by Rayleigh fading and generic, possibly non-Gaussian noise. The derived closed - form asymptotic error rate expressions are applicable to any type of noise with finite moments. Exploiting these analytical results, we formulate a power allocation problem with energy consumption fairness constraints for the source and the relays, which is solved using monomial fitting and a series of geometric programs. The accuracy of the derived analytical results and the effectiveness of the proposed power allocation scheme are confirmed with computer simulations.

In this letter, we investigate the performance of generalized selection combining in channels impaired by generalized fading and generic noise with finite moments. In particular, we derive accurate high signal-to-noise ratio approximations for the bit and symbol error rates of linear modulation schemes, which offer significant insight into the impact of the number of selected diversity branches, the type of fading, and the type of noise. Our analysis is applicable to all practically relevant types of fading including Rayleigh, Rician, Nakagami-q, and Nakagami-m fading, all types of noise with finite moments including Gaussian noise, Gaussian mixture noise, generalized Gaussian noise, and co-channel interference, and non-identically distributed fading and noise across the diversity branches. Our results reveal that while performance always improves with increasing number of selected branches for identically distributed noise, this is not necessarily true for non-identically distributed noise.

Best relay selection (BRS) has received considerable attention in the literature as it makes efficient use of the system resources and achieves full diversity. Partial relay selection (PRS) is an alternative to BRS and performs relay selection based on local channel state information (CSI) only at the expense of a loss in diversity. Although, in practice, relays may be deployed in unfavorable environments exposing them to non-Gaussian impairments, existing analyses and design guidelines for BRS and PRS are limited to additive white Gaussian noise channels. In this paper, we analyze the error rate of BRS and PRS in the asymptotic regime of high signal-to-noise ratio (SNR) for amplify-and-forward (AF) relays and impairment by generic noise and interference. The derived analytical results are valid for Gaussian and non-Gaussian noises with finite moments, independent and non-identically distributed Rayleigh fading, and arbitrary linear modulation schemes. In order to reduce the signaling overhead required for CSI acquisition, we propose a relay subset selection scheme for BRS and PRS. Furthermore, to guarantee fairness in energy resource usage across the relays, we introduce a fair and flexible power allocation scheme with energy consumption constraints which does not affect the achievable diversity gain. The proposed relay subset selection and power allocation schemes only require knowledge of the average CSI of the links and certain moments of the noises impairing the relays and the destination.

Full search L1-norm (FS-L1) and sphere decoding L∞-norm (SD-L∞) detectors have been previously proposed for multiple-input multiple-output (MIMO) systems as they entail a lower receiver complexity than the optimal L2-norm detector. However, the performance loss caused by application of these suboptimal detectors in additive white Gaussian noise (AWGN) has not been well investigated yet. In this paper, we analyze the asymptotic bit error rate of general FS-Lpand SD-Lp detectors in independent identically distributed Rayleigh fading channels. Our results are valid for all types of noise with finite moments including AWGN. We show that both FS-Lpand SD-Lp detectors achieve a diversity gain equal to the number of receive antennas independent of the metric parameter p and the type of noise. However, except for the conventional L2case, the performances of FS-Lpand SD-Lp detectors are not identical and the performance differences may be several dB for large numbers of receive antennas. Also, our results show that p=2 is not optimal in non-Gaussian noise and L1and L∞detectors may result in large performance gains or degradations compared to L2detectors depending on the type of noise.