Low Earth orbit (LEO) mega-constellations enable global, low-latency connectivity but challenge the security function chain (SFC) orchestration due to fast-changing visibility, resource volatility, and heterogeneous quality of service (QoS) requirements. To tackle this issue, we present in this paper SATStab, a stability-aware orchestration framework that co-designs: (i) stability-regularized objectives penalizing configuration churn across time windows; (ii) temporal decoupling with visibility-aware repair and warm starts; and (iii) hierarchical decoupling that separates fast association decisions from slower SFC placement and resource allocation. We formulate SATStab as a mixed-integer non-linear programming (MINLP) problem and evaluate it with realistic LEO dynamics. We solve it through a two-stage approach, where in the first stage, we optimize satellite-user associations while in the second stage, we optimize SFC placements. Through extensive simulations, we show that, relative to a monolithic MINLP, SATStab reduces user-satellite handovers by 54.3%, satellite-ground station (GS) re-associations by 59.3%, and SFC migrations by 32.7%, while cutting solution time by 56.8% without degrading end-to-end (E2E) delay. These results prove the efficiency of SATStab in terms of resource orchestration and resilience.

Reinforcement learning (RL) has become a key enabler for realizing adaptive and autonomous decision-making in next-generation AI-driven wireless networks, enabling real-time optimization of transmission strategies to counter jamming attacks. However, this adaptability also introduces critical vulnerabilities since the reliance of RL agents on environmental feedback renders them susceptible to deception, particularly when adversaries manipulate the environment in order to mislead the learning process. Yet, even though prior research considered adversarial jamming with white or grey-box access to the RL agent, the challenge of black-box jamming, where the jammer adapts without explicit feedback on its impact, remains largely unexplored. With this motivation, the present contribution addresses a practical adversarial scenario where a smart anti-jamming agent does not just resist jamming but actively exploits jamming signals to increase its throughput, especially as jamming attacks intensify. Defeating such an adaptive agent is particularly challenging in black-box settings, where the jammer has no knowledge of the link’s internal mechanisms or reward structure. In this context, we systematically benchmark a variety of advanced reactive jamming strategies, including both interaction-driven and optimization-driven approaches, under these realistic constraints. The achieved results indicate that adaptive, learning-driven jammers can reliably force even intelligent anti-jamming links into suboptimal operation, causing substantial throughput loss while consuming significantly less jamming power than conventional reactive jamming attacks. These findings reveal a fundamental vulnerability in RL-driven cognitive networks and highlight the urgent call for more resilient learning frameworks in order to secure next generation wireless systems.

Covert wireless communication ensures both information confidentiality and transmission untraceability, which is increasingly vital for mission-critical extended reality (XR) services. While unmanned aerial vehicles (UAVs) provide mobility and flexible coverage, and intelligent reflecting surfaces (IRSs) enable energy-efficient signal manipulation, their joint use for covert communications has not yet been sufficiently explored. This paper proposes a novel UAV-mounted IRS system for covert communications that passively reflects source signals toward a legitimate receiver while minimizing detection by an adversary warden. In contrast to previous work that treats trajectory design, beamforming, and power control in isolation, the proposed work develops a unified framework based on double deep Q-networks (DDQN) to jointly optimize the UAV trajectory, power allocation, and IRS phase shifts under covert constraints. We analytically derive the optimal detection threshold and the minimum detection error probability, which are dynamically integrated into the learning framework. The optimization problem is formulated as a constrained Markov decision process, which allows the agent to adaptively learn optimal policies in dynamic environments without relying on perfect channel knowledge. Simulation results demonstrate that the proposed framework significantly improves covert rate and energy efficiency compared with the iterative and random benchmark schemes, while also providing insights into the impact of system parameters on performance.

Reliable low Earth orbit satellite networks (LEO-SNs) should be capable of optimally adapting to dynamic environments and remaining resilient against smart jamming attacks. In this context, dynamic inter-satellite link (ISL) routing is crucial for enabling efficient and adaptive data transmission across any satellite network during smart jamming attacks. However, due to the environmental variability, ISL routing becomes a complex time-sequential optimization problem. Accordingly, in this study, we propose a digital twin-assisted federated quantum deep reinforcement learning (DT-FQDRL) framework to solve dynamic ISL routing with faster convergence and minimal-error solutions. The DT-FQDRL framework optimizes ISL routing by minimizing jamming success rate and total delay while maximizing energy efficiency. Specifically, a digital twin (DT) replicates the LEO-SN environment to simulate satellite interactions and jamming behaviors at each time step. In this virtual setting, each satellite employs quantum deep reinforcement learning (QDRL) for local training and long-term prediction. To enhance data privacy and prevent node conflicts, a hierarchical federated learning scheme aggregates local QDRL models within the DT. The optimized weights are then transferred to real satellites. Our numerical results demonstrate that the DT-FQDRL framework reduces jamming success rate by 48.16%, decreases total delay by 22.26%, and improves energy efficiency by 6.17% over existing benchmarks.

Multimodal learning emerges as a promising solution for high-precision localization, a cornerstone of 6G integrated sensing and communications (ISAC), by integrating measurements from different data sources. Yet its real-world deployment remains challenging because(i)the quality and relevance of different modalities fluctuate with frequency, noise, and antenna heterogeneity and(ii)spatial and fingerprint ambiguities under non-line-of-sight (NLOS) propagation obscure the mapping between channel measurements and positions. To overcome these challenges, we propose a spatial-context-aware dynamicfusion architecture built on the mixture-of-experts (SCADF-MoE) backbone. We first construct a million-scale comprehensive ray-tracing dataset measuring synchronized angle, distance, gain, and channel across diverse carrier frequencies, antenna geometries, and noise levels. A three-stage pre-processing pipeline then clusters neighboring points into short trajectories, enriching data samples with spatial context information. The resulting sequences are fed into SCADF-MoE: first, multimodal soft MoE blocks with learnable routing matrices dynamically fuse heterogeneous inputs according to their modality relevance in different environmental contexts; second, a modality-task MoE formulates position estimation as a multi-objective problem, simultaneously predicting coordinates of neighboring points to leverage their shared spatial correlations. Additionally, we introduce a regularization loss that enforces expert diversity and mitigates gradient conflicts during multi-task optimization. Simulations across three environments (dense-urban, suburban, canyon) and three heterogeneity dimensions (frequency, noise, antenna) demonstrate that SCADF-MoE achieves consistent sub-meter accuracy in all conditions, reducing overall MSE by 63%, and cuts unseen-NLOS error by 55% compared to state-of-the-art methods. To the best of our knowledge, this is the first work that leverages large-scale multimodal MoEs for high-precision ISAC localization.

The communication performance of traditional two-dimensional (2D) antenna arrays is approaching its theoretical limit under constraints of physical size and hardware costs, thus failing to meet the escalating demands of wireless communications. While double-layer three-dimensional (3D) antenna arrays presents a breakthrough for overcoming this bottleneck by exploiting the additional degrees of freedom, its implementation is hindered by several challenges, notably the issues of codebook design. In this paper, we propose a novel codebook scheme tailored for 3D antenna array structures. Specifically, an angle-distance-aware codebook for 3D antenna arrays is designed to cater to both near-field and far-field scenarios by minimizing inter-beam interference, with proven asymptotic orthogonality. Furthermore, evanescent codewords for both regions are effectively eliminated to improve codebook construction efficiency. Simulation results illustrate the superior performance of the proposed codebook over 2D baselines, with a 29% and 12% narrower angular and distance beamwidth ofh=λ, and a 27% gain in spectral efficiency ofh=0.5λ, owing to the vertical dimension. Moreover, practical mutual coupling that manifests as beam deviations and broadening is analyzed to establish a basis for future work.

The sixth-generation (6G) wireless systems aim to achieve ultra-high data rates and enhanced connectivity, driven by the increasing demand for broadband services and interactive applications. However, achieving such high data rates introduces significant challenges, such as intersymbol interference (ISI), which degrades signal quality and system performance. This paper proposes a novel reconfigurable intelligent surface (RIS)-assisted single-carrier (SC) frequency domain equalization (FDE) to mitigate ISI and enhance broadband connectivity. The RIS reflection coefficients are configured to maximize the received signal power at the user equipment (UE) by compensating for the phase of the dominant-tap in the end-to-end channel of the proposed system. This configuration enables coherent signal combining at the receiver, thereby enhancing signal quality and overall system performance. The performance of the proposed system is analyzed over frequency-selective Rayleigh fading channels, and the pairwise error probability (PEP) expression and upper bound for the bit error rate (BER) are derived. Analytical and simulation results demonstrate that the proposed framework consistently outperforms the state-of-the-art cyclic prefix (CP) SC-RIS system, achieving up to two orders of magnitude improvement in terms of BER. Moreover, diversity analysis shows that while conventional CP transmission achieves a diversity order of 1, the proposed RIS-assisted framework attains a higher order equal tokλ, which corresponds to the shape parameter of the distribution of the weighted sum power of the end-to-end channel. This parameter scales linearly with both the number of RIS elements and the number of effective channel taps, thereby enabling enhanced diversity in the presence of richer multipath propagation and larger RIS arrays. Finally, performance analysis explicitly demonstrates that increasing the number of RIS elements and channel taps further improves system performance, highlighting the advantages of RIS-aided spatial configuration and multipath diversity exploitation.

A metaverse can bring many benefits (i.e., self-sustaining and proactive analytics (e.g., analysis before user requests)) to wireless applications; however, its deployment is very challenging due to simultaneous quality of service (QoS) and quality of physical experience (QoE) constraints. Furthermore, the computing and communication resources of end-nodes are limited. For instance, immersive experience devices (e.g., augmented reality (AR) headsets) in a metaverse have limited computing power and therefore, might not be able to perform rendering tasks. Consequently, this paper proposes a novel task offloading framework for metaverse-empowered wireless systems. Our formulated problem aims at minimizing the cost of task offloading in the metaverse while considering both QoS and QoE constraints by optimizing the task offloading, resource allocation, and transmit power allocation variables. For QoS, we consider latency and reliability, whereas for QoE, we consider both immersive experience and packet error rate. To optimize the formulated problem, we use a decomposition-based scheme that further uses modified block-successive upper-bound minimization (BSUM), convex optimization, and multi-agent reinforcement learning (MARL) for transmit power allocation, resource allocation, and task offloading, respectively. Our solution of using convex optimization-assisted MARL for joint resource allocation and task offloading significantly improves the performance of learning in terms of reward and attaining fast QoS as well as QoE. Furthermore, BSUM significantly improves transmit power allocation when used in conjunction with a convex optimizer and MARL. Other than that, we also use a dueling (i.e., it is a reinforcement learning architecture combining the dueling network structure with the double deep Q-learning network method for more stable and efficient Q-value learning) concept to further improve the performance of MARL. Our analyses show that convex optimization, BSUM, and dueling help in significantly improving the performance of MARL. Compared to traditional MARL, our proposal results in significant improvement in terms of reward and cost, as illustrated by the results.

Low Earth orbit (LEO) satellite networks present critical challenges for security function chain (SFC) orchestration and associations due to rapid topology changes, resource volatility, and heterogeneous service requirements that render conventional SFC optimization approaches ineffective. To tackle this issue, we introduce here STARS, an optimization framework that fundamentally transforms the sequential time-window optimization for SFC orchestration and satellite association through three techniques: (1) Stability-aware regularization that penalizes configuration changes across time windows, thus reducing handovers by 54% and security function migrations by 33%; (2) Temporal decoupling that leverages solutions from prior time windows as warm-start seeds and dynamic repairing using real-time visibility constraints; and (3) Hierarchical decoupling that separates satellite association and SFC placement into computationally efficient stages, thus reducing time complexity. Through rigorous formulation as a mixed-integer non-linear programming (MINLP) and simulation-based evaluation, STARS achieves a 57% reduction in optimization solution time, a 7% reduction in the load of deployed security function instances, and efficient CPU utilization (9.81% increase) compared to benchmark schemes. STARS delivers these substantial benefits without any degradation in end-to-end delay. Note that the reported performance values are based on our specific system parameter choices and simulation setup and may not be universally representative. The co-design of stability mechanisms and decoupling strategies establishes STARS as a new paradigm for resilient satellite network optimization, balancing optimality, continuity, and computational tractability under high LEO satellite dynamicity.

Orthogonal delay-Doppler division multiplexing (ODDM) modulation has recently been regarded as a promising technology to provide reliable communications in high-mobility situations. Accurate and low-complexity channel estimation is one of the most critical challenges for massive multiple input multiple output (MIMO) ODDM systems, mainly due to the extremely large antenna arrays and high-mobility environments. To overcome these challenges, this paper addresses the issue of channel estimation in downlink massive MIMO-ODDM systems and proposes a low-complexity algorithm based on memory approximate message passing (MAMP) to estimate the channel state information (CSI). Specifically, we first establish the effective channel model of the massive MIMO-ODDM systems, where the magnitudes of the elements in the equivalent channel vector follow a Bernoulli-Gaussian distribution. Further, as the number of antennas grows, the elements in the equivalent coefficient matrix tend to become completely random. Leveraging these characteristics, we utilize the MAMP method to determine the gains, delays, and Doppler effects of the multi-path channel, while the channel angles are estimated through the discrete Fourier transform method. Finally, numerical results show that the proposed channel estimation algorithm approaches the Bayesian optimal results when the number of antennas tends to infinity and improves the channel estimation accuracy by about 30% compared with the existing algorithms in terms of the normalized mean square error.

This work investigates the performance of an Index Modulation-aided Non-Orthogonal Multiple Access (IM-NOMA) scheme in Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface (STAR-RIS)-assisted wireless networks. In the proposed system, information intended for a specific index-modulated (IM) user is conveyed via spatial modulation across STAR-RIS subsurfaces, where active indices are selected according to a predefined mapping. The IM user employs an energy-based maximum likelihood (EML) detector to infer the transmitted indices by exploiting the energy variations in the received signal. Alongside the IM user, additional users are multiplexed using conventional NOMA techniques, resulting in a hybrid IM-NOMA system. We focus on analyzing the conditional pairwise error probability (PEP) to characterize the system’s bit error rate (BER) performance. A union bound on the BER is derived using the PEP expression over Beaulieu-Xie (BX) fading channels. The resulting analysis provides tractable insights into the system behavior under practical propagation conditions. Extensive Monte Carlo simulations support the analytical findings, confirming the validity of the PEP-based union bound for evaluating the performance proposed IM-NOMA scheme in STAR-RIS environments.

Two nascent technologies, rate-splitting multiple access (RSMA) and reconfigurable intelligent surfaces (RIS), present promising avenues to enhance spectral and energy efficiencies within multi-antenna frameworks. However, passive RIS may encounter challenges in delivering substantial capacity gains due to the cumulative path loss effect. Active RIS (ARIS) equipped with low-cost amplifiers in the reflective elements emerges as a solution to mitigate the limitation. This paper investigates a multi-user multiple-input single-output (MISO) simultaneous wireless information and power transfer (SWIPT) framework, augmented by an ARIS and leveraging RSMA. The primary objective is to maximize the system's SE, subject to constraints imposed by the design of BS beamforming vectors, PS ratios, and RIS phase shifts. To address the inherent nonconvexity of this optimization problem, we propose an innovative approach that combines alternating optimization (AO) and semidefinite relaxation (SDR) algorithms. Simulations results demonstrate the significant advantages of our proposed design over established benchmarks.

The integration of low Earth orbit (LEO) satellite communication into 6G networks promises a transformative impact on global connectivity by expanding coverage to remote regions and enhancing service reliability. However, this new infrastructure also introduces significant security challenges due to its expansive attack surface. To address this concern, we propose a dynamic security functions allocation (SFA) model that optimizes the allocation of security functions (SFs) across satellites while considering computational resource limitations, dynamic topology changes, and the visibility constraints of satellite constellations. Our model leverages the flexibility of 6G network slicing (NS) to share non-critical SFs between slices, reducing resource overhead while maintaining essential security demands. To minimize the risk of sharing highly sensitive SFs between slices, our model employs a nonlinear penalty, which prioritizes minimizing risk by aggressively penalizing high-risk SFs sharing. This dynamic risk management framework assesses the probability and impact of security breaches, ensuring that SFs are shared only when the security risk is acceptable, balancing resource efficiency and security. By dynamically adapting to the network’s operational conditions, our approach provides a robust framework for efficient and secure satellite communication in 6G networks. Simulation results demonstrate the model’s flexibility in managing trade-offs across key network performance metrics.

Deploying small cells to densify heterogeneous networks (HetNets) provides a scalable way to meet the growing demand for diverse mobile services. Aggressive frequency reuse in ultra-dense networks (UDNs) further enhances spectral efficiency but intensifies inter-cell interference. With intelligent management, this interference can be harnessed to strengthen network-wide physical layer security (PLS). To this end, and in line with green-security principles, we propose that base stations collaboratively shape interference and deploy rechargeable jamming mines (RJMs) to expand secure zones by injecting targeted jamming around potential eavesdroppers, eliminating the need for dedicated jammers. To realize this, we develop a multi-agent reinforcement learning framework in which each BS independently optimizes beam orientation, transmit power, resource block allocation, and RJM activation under a zero-trust model that treats every location as a potential eavesdropper. A hybrid reward function aligns local actions with network-wide security goals, and state-dependent action masking reduces the policy search space. Extensive results show that the proposed approach substantially enlarges secure-area coverage, scales efficiently in ultra-dense deployments, and establishes a practical, sustainable foundation for collaborative PLS in next-generation wireless networks.

Reinforcement learning (RL) has emerged as a promising tool for enabling adaptive and autonomous behavior in cognitive wireless networks, where online decision-making is required under partial or no prior knowledge of the environment. By interacting with the wireless environment, RL-based cognitive links can dynamically optimize transmission strategies to mitigate interference and improve throughput. However, this adaptability introduces a critical vulnerability as RL agents are inherently sensitive to deception. The present contribution exposes this vulnerability by analyzing a deep RL-based anti-jamming link that enhances its performance by exploiting jamming energy through intelligent use of backscattering and energy harvesting. We counter this advantage by proposing a deception-based jamming strategy, termed "bait tactics," which manipulates the agent’s perceived state transitions and increases reward variance through targeted environment manipulations. Simulation results demonstrate that the proposed adversarial strategy can reduce the victim’s throughput by up to 72% while conserving up to 67% of jamming power compared to a standard reactive jamming in the considered scenario. These findings reveal a critical vulnerability in RL-driven cognitive networks and highlight the need for robust, deception-resilient learning frameworks in future wireless systems.

The escalating demands for secure wireless communications in the Internet of Everything envisioned for the 6G era emphasize the urgency of advanced security solutions beyond traditional methods, especially in extended reality applications where secure wireless communications are essential for functionality and user experience. This paper explores the integration of covert communication techniques with deep reinforcement learning to bolster security in wireless networks. Covert communication, which prevents adversaries from detecting transmissions, is a critical factor in protecting data transmission over vulnerable wireless channels. This paper considers a two-hop wireless system model that is optimized using a double-deep Q-network algorithm. The problem is formulated as a constrained Markov decision process, jointly optimizing relay selection, transmission, and jamming powers to maximize covert communication rates while minimizing detection by adversarial wardens. Comprehensive numerical analysis demonstrates the effectiveness of the proposed method under various system conditions, including different configurations of relay and jamming powers. The results confirm that our model aligns well with theoretical expectations and substantially enhances covert communication by intelligently adapting to environmental dynamics.

Large language models (LLMs) face significant challenges in specialized domains like telecommunication (Tele-com) due to technical complexity, specialized terminology, and rapidly evolving knowledge. Traditional methods, such as scaling model parameters or retraining on domain-specific corpora, are computationally expensive and yield diminishing returns, while existing approaches like retrieval-augmented generation, mixture of experts, and fine-tuning struggle with accuracy, efficiency, and coordination. To address this issue, we propose Telecom mixture of models (TeleMoM), a consensus-driven ensemble framework that integrates multiple LLMs for enhanced decision-making in Telecom. TeleMoM employs a two-stage process: proponent models generate justified responses, and an adjudicator finalizes decisions, supported by a quality-checking mechanism. This approach leverages strengths of diverse models to improve accuracy, reduce biases, and handle domain-specific complexities effectively. Evaluation results demonstrate that TeleMoM achieves a 9.7% increase in answer accuracy, highlighting its effectiveness in Telecom applications.

Modern edge cloud platforms must efficiently deploy and route containerized microservice DAGs under strict latency and cost constraints, while adapting to rapidly changing workloads and infrastructure states. Deep Reinforcement Learning (DRL) schedulers adapt well to dynamics but often lack semantic awareness of service intent and task dependencies, resulting in suboptimal decisions in unseen scenarios. To overcome these limitations, we introduce a Retrieval-Augmented Generation-assisted DRL (RAG-DRL) framework that integrates a lightweight DRL agent with a graph-based RAG module powered by a partially frozen LLM. A dynamic memory graph encodes contextual information such as node resources, network latencies, and SLA feedback. The LLM retrieves relevant historical deployments and current service intents to generate soft placement plans and reward estimates, which guide the DRL agent. These priors accelerate convergence, improve generalization across diverse conditions, and ensure real-time responsiveness. Evaluations on a realistic urban-scale edge cloud testbed confirm that RAG-DRL significantly reduces SLA violations, end-to-end latency, and resource imbalance, outperforming modern container-based schedulers. Our framework converges faster, maintains latency below 65 ms on scale, limits SLA violations to 12% under heavy load, and achieves 90 % resource utilization with balanced distribution.

Internet of Things (IoT) networks have been deployed widely making device authentication a crucial requirement that poses challenges related to security vulnerabilities, power consumption, and maintenance overheads. While current cryptographic techniques secure device communication; storing keys in Non-Volatile Memory (NVM) poses challenges for edge devices. Physically Unclonable Functions (PUFs) offer robust hardware-based authentication but introduce complexities such as hardware production and conservation expenses and susceptibility to aging effects. This paper’s main contribution is a novel scheme based on split learning, utilizing an encoder–decoder architecture at the device and server nodes, to first create a Virtual PUF (VPUF) that addresses the shortcomings of the hardware PUF and secondly perform device authentication. The proposed VPUF reduces maintenance and power demands compared to the hardware PUF while enhancing security by transmitting latent space representations of responses between the node and the server. Also, since the encoder is placed on the node, while the decoder is on the server, this approach further reduces the computational load and processing time on the resource-constrained node. The obtained results demonstrate the effectiveness of the proposed VPUF scheme in modeling the behavior of the hardware-based PUF. Additionally, we investigate the impact of Gaussian noise in the communication channel between the server and the node on the system performance. The obtained results further reveal that the achieved authentication accuracy of the proposed scheme is 100%, as measured by the validation rate of the legitimate nodes. This highlights the superior performance of the proposed scheme in emulating the capabilities of a hardware-based PUF while providing secure and efficient authentication in IoT networks.

The telecommunications industry’s rapid evolution demands intelligent systems capable of managing complex networks and adapting to emerging technologies. While large language models (LLMs) show promise in addressing these challenges, their deployment in telecom environments faces significant constraints due to edge device limitations and inconsistent documentation. To bridge this gap, we present TeleOracle, a telecom-specialized retrieval-augmented generation (RAG) system built on the Phi-2 small language model (SLM). To improve context retrieval, TeleOracle employs a two-stage retriever that incorporates semantic chunking and hybrid key-word and semantic search. Additionally, we expand the context window during inference to enhance the model’s performance on open-ended queries. We also employ low-rank adaption for efficient fine-tuning. A thorough analysis of the model’s performance indicates that our RAG framework is effective in aligning Phi-2 to the telecom domain in a downstream question and answer (QnA) task, achieving a 30% improvement in accuracy over the base Phi-2 model, reaching an overall accuracy of 81.20%. Notably, we show that our model not only performs on par with the much larger LLMs but also achieves a higher faithfulness score, indicating higher adherence to the retrieved context.

Recently, the research community has recently shown overwhelming interest in metaverse-enabled wireless devices, due to their compelling proactive learning and self-sustainability attributes. Proactive learning enables machine learning models to be trained before user requests, while self-sustainability allows a system to function with the least amount of assistance from network administrators/users. Because of these features, one can use metaverse to enable various applications (e.g., entertainment and collision avoidance) in intelligent transportation systems. However, the limitations of computing processing power (e.g., in autonomous cars) and communication resources make implementing metaverse-empowered vehicular networks challenging. Motivated by these facts, we present a new framework for cooperative sensing, communication, learning, and task offloading for vehicular networks enabled by the metaverse. Subsequently, we formulate a cost-function minimization problem that accounts for transmission energy and transmission latency. The cost is minimized by optimizing task offloading, wireless resource distribution, transmit power allocation, and sensing interval. We employ a decomposition-based strategy for simultaneous resource allocation, task offloading, sensing interval optimization, and transmit power allocation. Due to the combinatorial nature of the resource allocation and task offloading problems, matching-based solutions are used. For sensing interval optimization, convex optimization is used. On the other hand, due to the non-convex and continuous nature of the transmit power allocation problem, a proximal term is introduced into the objective function to approximate it as convex objective function, which is then solved using a convex optimizer. To gain further insights, the proposed scheme is supported by extensive numerical results.

Extended reality (XR) and 6G networks are set to transform mobile immersive experiences, with privacy and security being paramount in XR communications. Achieving secure and reliable XR experiences while meeting high-resolution and low-latency requirements is challenging for wireless networks. A novel security-aware cross-layer communication management framework is proposed, employing zero-trust spatiotemporal physical layer level manipulations for moving-target defense. Driven by deep reinforcement learning and real-time monitoring, the proposed framework adaptively reprograms the network configuration to maximize the user’s quality of experience (QoE), reduce the overall latency, and minimize the attacker’s intercept probability. The framework was evaluated in a simulated scenario featuring an indirect multi-hop communication setup. The results show that the proposed framework effectively and efficiently secures XR user communications while maintaining QoE, outperforming conventional Q-learning algorithms.

The present contribution investigates Index Modulation Aided Non-Orthogonal Multiple Access (IM-NOMA) in simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS)-assisted networks. In the proposed IM-NOMA scheme, the information for a specific user (IM user) is spatially modulated across STAR-RIS subsurfaces according to a predefined pattern. Then, the IM user detects its signal using an energy-based maximum likelihood (EML) detection method, which technically leverages the energy of the received signals to identify the active subsurface indices. In this context, the performance of the proposed IM-NOMA scheme is quantified over Beaulieu-Xie (BX) fading channels in terms of pairwise error probability (PEP), bit error rate union bound, and achievable rate. The obtained PEP expressions are then used to derive a tight upper bound on the bit error rate (BER), which is subsequently utilized in quantifying the overall system performance in terms of upper-bound BER and the achievable rate. Finally, we validate the derived analytic expressions with respective results from extensive Monte Carlo simulations that provide valuable insights of the theoretical and practical importance on the achievable performance for all users in the system.

The transition from fifth-generation (5G) to sixth-generation (6G) mobile networks necessitates network automation to meet the escalating demands for high data rates, ultra-low latency, and integrated technology. Recently, Zero-Touch Networks (ZTNs), driven by Artificial Intelligence (AI) and Machine Learning (ML), are designed to automate the entire lifecycle of network operations with minimal human intervention, presenting a promising solution for enhancing automation in 5G/6G networks. However, the implementation of ZTNs brings forth the need for autonomous and robust cybersecurity solutions, as ZTNs rely heavily on automation. AI/ML algorithms are widely used to develop cybersecurity mechanisms, but require substantial specialized expertise and encounter model drift issues, posing significant challenges in developing autonomous cybersecurity measures. Therefore, this paper proposes an automated security framework targeting Physical Layer Authentication (PLA) and Cross-Layer Intrusion Detection Systems (CLIDS) to address security concerns at multiple Internet protocol layers. The proposed framework employs drift-adaptive online learning techniques and a novel enhanced Successive Halving (SH)-based Automated ML (AutoML) method to automatically generate optimized ML models for dynamic networking environments. Experimental results illustrate that the proposed framework achieves high performance on the public Radio Frequency (RF) fingerprinting and the Canadian Institute for Cybersecurity Intrusion Detection System 2017 (CICIDS2017) datasets, showcasing its effectiveness in addressing PLA and CLIDS tasks within dynamic and complex networking environments. Furthermore, the paper explores open challenges and research directions in the 5G/6G cybersecurity domain. This framework represents a significant advancement towards fully autonomous and secure 6G networks, paving the way for future innovations in network automation and cybersecurity.

In recent years, there has been a rising trend towards emerging applications (e.g., brain-computer interaction and haptics-based autonomous cars) with diverse requirements. To effectively enable these applications via autonomous operation and intelligent analytics, one can use a metaverseFor more details on how a metaverse can enable emerging applications and architecture, please refer to khan2024ametaverse. In a metaverse, we have two spaces: (a) a meta space based on a virtual model that performs analysis and resource management and (b) a physical space comprised of real world entities. A metaverse effectively enables emerging applications by performing three main tasks: (a) distributed learning of metaverse models; (b) instantly serving the end-users; and (c) sensing of the physical environment and sharing it with the meta space for synchronized operation. To perform these tasks, efficient wireless resource management is needed. Therefore, a novel resource scheduling framework for the wireless metaverse to enable various applications is proposed. Our aim is to minimize the cost of learning and sensing in metaverse. Subsequently, we formulate a problem. Meanwhile, the reliability as well as latency constraints of the service-requesting devices/users will be fulfilled. We assign multiple resource blocks to learning and sensing devices/units, whereas we use a concept of puncturing for service-requesting devices/users upon arrival. We use a scheme that is based on block successive upper-bound minimization and convex optimization for solving our formulated problem. At the end, we use empirical cumulative distribution function vs. cost and cost vs. metaverse entities for numerical evaluations.

The sixth-generation (6G) wireless network aims to deliver remarkable advancements in system throughput, energy efficiency, traffic capacity per area, spectral efficiency, and low latency. Achieving these goals requires a highly adaptable radio interface capable of efficiently managing limited frequency resources, necessitating the development of new multiple access techniques and waveforms. In large-bandwidth multi-user networks, intersymbol interference (ISI) and inter-user interference (IUI) pose significant design challenges. Time reversal (TR) has emerged as a promising waveform candidate for 6G, as it focuses signal energy in both the time and space domains within multipath environments. Meanwhile, non-orthogonal multiple access (NOMA) offers high spectral efficiency and improved connectivity by serving multiple users over the same time-frequency-code resources. This paper explores the integration of NOMA and TR to address these challenges and proposes, for the first time in the literature, a novel receiver architecture for downlink NOMA-based TR communications, which does not require precoding at the transmitter. Specifically, power-domain NOMA is employed at the transmitter, and TR filtering is applied at each receiver. We derive novel approximated expressions for the pairwise error probability (PEP), a key element in determining the union bound on the bit error rate (BER), to assess user performance. Extensive Monte Carlo simulations are carried out to validate these analytical expressions, providing critical insights into the error rate performance for each user. Additionally, we evaluate the performance gains of the proposed NOMA-based TR receiver over the orthogonal multiple access scheme, known as time-reversal multiple access (TRMA). Results show that our approach significantly outperforms TRMA in terms of BER, particularly in sparse multipath environments, with an average BER improvement of 73.5% to 98.31%. Furthermore, our findings reveal that at high signal-to-noise ratios, the diversity gain for a specific user is proportional to the product of the user’s order, determined by its channel strength, and the number of its channel taps.

Advancements in emerging technologies, e.g., reconfigurable intelligent surfaces and holographic MIMO (HMIMO), facilitate unprecedented manipulation of electromagnetic (EM) waves, significantly enhancing the performance of wireless communication systems. To accurately characterize the achievable performance limits of these systems, it is crucial to develop a universal EM-compliant channel model. This paper addresses this necessity by proposing a comprehensive EM channel model tailored for realistic multi-path environments, accounting for the combined effects of antenna array configurations and propagation conditions in HMIMO communications. Both polarization phenomena and spatial correlation are incorporated into this probabilistic channel model. Additionally, physical constraints of antenna configurations, such as mutual coupling effects and energy consumption, are integrated into the channel modeling framework. Simulation results validate the effectiveness of the proposed probabilistic channel model, indicating that traditional Rician and Rayleigh fading models cannot accurately depict the channel characteristics and underestimate the channel capacity. More importantly, the proposed channel model outperforms free-space Green’s functions in accurately depicting both near-field gain and multi-path effects in radiative near-field regions. These gains are much more evident in tri-polarized systems, highlighting the necessity of polarization interference elimination techniques. Moreover, the theoretical analysis accurately verifies that capacity decreases with expanding communication regions of two-user communications.

A novel integrated wireless powered sensing and communication (IWPSAC) framework is proposed. Specifically, a multi-antenna transmitter utilizes a radar signal for sensing targets while enabling multiple Internet of Things (IoT) devices to harvest energy from the signal, each of which employs the collected energy to upload information to an access point (AP). Our setup further considers a reconfigurable intelligent surface (RIS) to integrate sensing, wireless energy transfer (WET) and wireless information transfer (WIT) by optimizing the phase shifts. We formulate an optimization problem to maximize the weighted sum of the communication throughput and the beampattern gain by jointly designing the energy beamforming, transmission time scheduling and RIS phase shifts. The presence of multiple coupled variables in the formulated problem renders the optimization problem non-jointly convex. To address its non-convexity, we first derive a closed-form expression for the optimal RIS phase shifts in the WIT phase. Then, an alternating optimization (AO) algorithm is proposed to solve the tradeoff problem iteratively. Concretely, this involves alternating the design of the energy beamforming and the RIS phase shifts for sensing/WET by leveraging the semidefinite programming (SDP) relaxation method. To overcome the high complexity introduced by the SDP, we introduce a low complexity AO algorithm that derives the optimal solutions for energy beamforming, transmission time scheduling, and sensing/WET phase shift using successive convex approximation (SCA), Lagrangian duality methods, Karush-Kuhn-Tucker (KKT) conditions, and the element-wise block coordinate descent (EBCD) approach. Simulation results demonstrate the performance of the proposed algorithms and underscore the superior benefits of the RIS compared to baseline schemes.

Unmanned aerial vehicles (UAVs) offer a promising solution for enhancing network coverage, reliability, and data speed in future wireless network generations. However, deploying UAVs as aerial base stations requires careful consideration of crucial design factors, including three-dimensional (3D) placement and performance optimization tailored to specific applications. In this paper, the 3D placement of multiple UAVs, acting as aerial base stations, is investigated in a dynamic user scenario. First, a closed-form expression for the coverage probability is derived. Then, to maximize the network coverage and sum rate while ensuring reliable and energy efficient system, a joint multi-objective optimization problem is formulated considering the real-time user movements. To solve the problem, an improved Chaos-based Bonobo Optimizer (CBO) scheme is proposed which combines chaotic maps with the Bonobo Optimizer (BO) algorithm. The obtained results demonstrate the superior performance of the proposed approach compared with different benchmark algorithms. The results reveal that the proposed CBO algorithm offers a minimum of $\mathbf{1 5 \%}$ and $\mathbf{9 0} \mathbf{~ M b i t / s ~ i m p r o v e m e n t s ~}$ in coverage and sum rate, respectively.

Gait biometrics which concern with recognizing individuals by the way they walk are of a paramount importance these days. Human gait is a candidate pathway for such identification tasks since other mechanisms can be concealed. Most common methodologies rely on analyzing 2D/3D images captured by surveillance cameras. Thus, the performance of such methods depends heavily on the quality of the images and the appearance variations of individuals. In this study, we describe how gait biometrics could be used in individuals' identification using a deep feature learning and inertial measurement unit (IMU) technology. We propose a model that recognizes the biological and physical characteristics of individuals, such as gender, age, height, and weight, by examining high-level representations constructed during its learning process. The effectiveness of the proposed model has been demonstrated by a set of experiments with a new gait dataset generated using a shoe-type based on a gait analysis sensor system. The experimental results show that the proposed model can achieve better identification accuracy than existing models, while also demonstrating more stable predictive performance across different classes. This makes the proposed model a promising alternative to current image-based modeling.

The Industrial Internet of Things (IIoT) has been recognized as having the potential to offer substantial benefit to a wide range of industrial sectors. However, the widespread development and deployment of IIoT pose a set of challenges, including the shortage of spectrum resources and network security. Given the heterogeneity of IIoT devices, conventional cryptographic security techniques are not sufficient, since they suffer from challenges, including computation, storages, latency, and interoperability. In this article, we present a physical layer security analysis for cognitive IIoT networks. In our system, IIoT devices opportunistically utilize the primary spectrum; thereby improving spectrum efficiency and allowing access to a large number of devices. Specifically, the considered network uses space-time block coding (STBC) in conjunction with nonorthogonal multiple access (NOMA) in underlay cognitive mode to realize data transmission for IIoT devices with high-spectrum efficiency. The secure STBC-NOMA transmission model is established by taking into account the interference from a primary user. New approximate and asymptotic expressions of secrecy outage probability (SOP) for the secondary users (SUs) are derived to characterize the system’s secrecy performance. In addition, the SU’s closed-form secrecy ergodic rate (ER) is provided. The proposed STBC-NOMA approach, when compared to the classic single antenna (SA)-based NOMA, achieves better SOP performance for all SUs, as confirmed by both analytical and simulation findings. Furthermore, we show that the proposed STBC-NOMA framework improves the SOP performance of weaker users. Additionally, the STBC-NOMA framework outperforms the SA-NOMA framework in terms of secrecy ER performance for all SUs.

As potential solutions to empower transmissions among the Internet of Things (IoT) devices, ambient radio frequency (RF) backscatter technology and reconfigurable intelligent surfaces (RISs) have recently attracted a lot of attention. To improve energy and spectrum efficiency, we design a system with a transmit antenna selection (TAS)-aided base station (BS) relying on nonorthogonal multiple access (NOMA), RIS, and backscatter communications (BackCom) with robust transmission links, allowing more users to be served effectively. We adopt the two-user grouping model in the coverage of main BS associated with a particular RIS and interference from coordinate BS is also considered to showcase differences among the performance of the two different kinds of users (i.e., the IoT user with and the IoT user without a dedicated RIS). To exhibit the system performance, we derive closed-form expressions for two main system performance metrics, namely, outage probability and ergodic capacity. A degraded performance is also considered for the case of imperfect successive interference cancellation (SIC). The benefits of the BackCom RIS-aided NOMA system are then demonstrated by comparing its performance to that of traditional orthogonal multiple access (OMA) RIS-aided backscatter systems. We then introduce analytical models to characterize the impact of the main factors on the outage performance and characterize the optimal performance in specific cases. Together with extensive simulations, our analysis shows that the system performance can be adjusted by controlling factors, including power allocation coefficients, the number of metasurfaces of RIS, and target rates.

Spatial Modulation (SM) has been proposed as a multiple-input-multiple-output (MIMO)-based technique to overcome the inter-channel interference experienced in conventional MIMO systems. It has been further shown that SM enhances energy efficiency and reduces the receiver’s complexity. Nevertheless, under high antenna correlation scenarios, the detection performance of the antenna indices degrades significantly. To address this critical concern, in this paper, we propose three autoencoder-based frameworks for spatial modulation. The first scenario, similar to conventional spatial modulation, trains the encoder for data modulation and the decoder for data demodulation as well as antenna index detection. The performance of this framework deteriorates in high antenna correlation scenarios. Therefore, two novel solutions are presented to embed the antenna index into the transmitted signal in order to reduce the receiver’s reliance on the channel conditions. The first framework adds a phase-shift keying-based antenna signature, while the other trains the encoder to learn an appropriate antenna index embedding. Simulation results show that the two enhanced frameworks result in a significantly enhanced performance, compared to conventional spatial modulation, in terms of block error rate and power efficiency under a high correlation setup (about 18 dB and 24 dB gain, respectively, at a Rician factor of 20 dB).

This article introduces a new method to improve the dependability of millimeter-wave (mmWave) and terahertz (THz) network services in dynamic outdoor environments. In these settings, line-of-sight (LoS) connections are easily interrupted by moving obstacles like humans and vehicles. The proposed approach, coined as Radar-aided Dynamic blockage Recognition (RaDaR), leverages radar measurements and federated learning (FL) to train a dual-output neural network (NN) model capable of simultaneously predicting blockage status and time. This enables determining the optimal point for proactive handover (PHO) or beam switching, thereby reducing the latency introduced by 5G new radio procedures and ensuring high quality of experience (QoE). The framework employs radar sensors to monitor and track object movement, generating range-angle and range-velocity maps that are useful for scene analysis and predictions. Moreover, FL provides additional benefits such as privacy protection, scalability, and knowledge sharing. The framework is assessed using an extensive real-world dataset comprising mmWave channel information and radar data. The evaluation results show that RaDaR substantially enhances network reliability, achieving an average success rate of 94% for PHO compared to existing reactive HO procedures that lack proactive blockage prediction. Additionally, RaDaR maintains a superior QoE by ensuring sustained high throughput levels and minimising PHO latency.

Zero-Touch Networks (ZTNs) represent a state-of-the-art paradigm shift towards fully automated and intelligent network management, enabling the automation and intelligence required to manage the complexity, scale, and dynamic nature of next-generation (6G) networks. ZTNs leverage Artificial Intelligence (AI) and Machine Learning (ML) to enhance operational efficiency, support intelligent decision-making, and ensure effective resource allocation. However, the implementation of ZTNs is subject to security challenges that need to be resolved to achieve their full potential. In particular, two critical challenges arise: the need for human expertise in developing AI/ML-based security mechanisms, and the threat of adversarial attacks targeting AI/ML models. In this survey paper, we provide a comprehensive review of current security issues in ZTNs, emphasizing the need for advanced AI/ML-based security mechanisms that require minimal human intervention and protect AI/ML models themselves. Furthermore, we explore the potential of Automated ML (AutoML) technologies in developing robust security solutions for ZTNs. Through case studies, we illustrate practical approaches to securing ZTNs against both conventional and AI/ML-specific threats, including the development of autonomous intrusion detection systems and strategies to combat Adversarial ML (AML) attacks. The paper concludes with a discussion of the future research directions for the development of ZTN security approaches.

Automatic modulation classification (AMC) is an indispensable part of intelligent receivers in modern wireless communication systems. AMC enables blind identification of modulation without prior knowledge of the signal parameters, which is a challenging task, particularly in practical scenarios with severe multipath fading, frequency-selective and time-varying channels. Although deep learning techniques have been shown to be efficient in AMC tasks, traditional convolutional and recurrent neural networks may not be able to cope with complex-valued input signals and large-scale datasets. Motivated by this, in this paper, we propose a novel perceiver-based AMC architecture that leverages the recently introduced Perceiver, which combines cross-attention and latent transformer modules, to efficiently process and classify complex-valued in-phase and quadrature (IQ) samples of the received signal. The proposed model is trained and evaluated on the DeepSig 2018 RadioML dataset. Simulation results demonstrate a significant improvement in the classification accuracy compared to a ResNet-based AMC model, particularly for higher-order quadrature amplitude modulation (QAM) and under practical signal-to-noise ratio values. These findings indicate the potential of the perceiver architecture for robust and efficient AMC in wireless communication systems.

The upcoming sixth generation (6G) is expected to support a wide range of applications that require efficient sensing, accurate localization, and reliable communication capabilities. Furthermore, 6G is expected to catalyze the development of new use cases that will require working in extreme environmental and hazardous conditions and have ultra-small size and low-cost wire-less devices. Thus, developing sustainable multi-functional wireless networks that are capable of incorporating billions of low-power devices and supporting their sensing and communication requirements on top of energy harvesting capability is of paramount importance. Motivated by this, we consider in this work a rate-splitting multiple access (RSMA)-based multifunctional wireless network with sensing, energy harvesting, and communication capabilities. We employ trust region policy optimization (TRPO), a deep reinforcement learning (DRL) algorithm, to efficiently allocate the available resources and manage the interference between the three functionalities. TRPO/DRL is capable to learn a near-optimal policy for the resource allocation problem in a complex and dynamic environment. This enables us to obtain near-optimal transmit precoders, power splitting ratios, and rate-splitting among the common and private rates in a multiple access setting. Simulation results demonstrate the effectiveness of RSMA in mitigating the interference in such multi-functional networks and its capability to accommodate the rate and energy harvesting requirements of the devices while still capable of sensing multiple targets.

The emergence of new technologies for the fifth/sixth generation (5G/6G) wireless networks has led to the development of new services, resulting in an increase in malicious activities and cyber-attacks targeting various networklayers. Edge computing, a crucial technology enabler for 6G, is expected to facilitate traffic optimisation and support new ultra-low latency services. By integrating computing power from supercomputing servers into devices at the network edge in a distributed manner, edge computing can provide consistent quality-of-service, even in remote areas, which will drive the growth of associated applications. However, the complex environment created by edge computing also poses challenges for detecting malware. Therefore, this paper proposes a novel approach to malware detection using explain ability via visualization and a multi-labelling technique. An object detection algorithm is used to identify malware families within the dataset which is created by emphasizing key regions. Using features from different malware categories in an image, this model displays a thorough malware recipe. Our experiments using real malware data demonstrate that identifying malware by its visible characteristics can significantly improve the interpretability of the detection process, enhancing transparency and trustworthiness.

Traditional anti-jamming techniques like spread spectrum, adaptive power/rate control, and cognitive radio, have demonstrated effectiveness in mitigating jamming attacks. However, their robustness against the growing complexity of internet-of-thing (IoT) networks and diverse jamming attacks is still limited. To address these challenges, machine learning (ML)-based techniques have emerged as promising solutions. By offering adaptive and intelligent anti-jamming capabilities, ML-based approaches can effectively adapt to dynamic attack scenarios and overcome the limitations of traditional methods. In this paper, we propose a deep reinforcement learning (DRL)-based approach that utilizes state input from realistic wireless network interface cards. We train five different variants of deep Q-network (DQN) agents to mitigate the effects of jamming with the aim of identifying the most sample-efficient, lightweight, robust, and least complex agent that is tailored for power-constrained devices. The simulation results demonstrate the effectiveness of the proposed DRL-based anti-jamming approach against proactive jammers, regardless of their jamming strategy which eliminates the need for a pattern recognition or jamming strategy detection step. Our findings present a promising solution for securing IoT networks against jamming attacks and highlights substantial opportunities for continued investigation and advancement within this field.

Visible light communication (VLC) is a rapidly growing wireless communication technology for the Internet of Things (IoT) that offers high data rates and low latency, making it ideal for massive connectivity. Efficient resource allocation is essential in VLC networks to minimize inter-symbol and cochannel interferences, which can greatly improve network performance and user satisfaction. This paper focuses on an indoor IoT-based VLC system that utilizes photodetectors (PDs) on users’ cell phones as receivers, with the goal of maximizing system performances and reducing power consumption by selectively activating some PDs while deactivating others. However, this objective presents a challenge due to the inherent non-convex nature of the multi-objective optimization problem, which cannot be solved by analytical means. To address this, we propose an enhanced Aquila optimization (EAO) scheme that improves upon the Aquila Optimizer (AO) by incorporating a fitness distance balance (FDB) function. We evaluate our proposed EAO in various scenarios under different settings, considering both capacity and fairness metrics. Through simulations, we demonstrate the effectiveness of our approach and its superiority over classical algorithms such as Aquila Optimizer (AO), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO) in finding the optimal solution. Our results confirm that the proposed EAO algorithm can efficiently optimize the system capacity and ensure fairness among all users, providing a promising solution for indoor VLC systems.

This paper investigates streetlight-to-vehicle visible light communication (VLC) system performance for outdoor broadcasting applications. We adopt streetlight lamps as optical internet-of-thing (IoT) devices broadcasting internet services and safety messages to road vehicles. With their asymmetrical radiation patterns, Streetlight antennas are exceedingly different from indoor lighting modules, which deploy ceiling luminaries with ideal Lambertian ones. Therefore, a realistic channel modelling for streetlight-to-vehicle VLC system should be deployed for precise performance insights. We consider a streetlight-to-vehicle VLC system in a two-lane road with multiple light poles uniformly distributed on both sides. Based on that, we investigate the system performance of the streetlight-to-vehicle VLC system in terms of the bit-error-rate (BER) and outage distance and explore the effect of different transceivers and system parameters on the performance. These consider the transmission modulation order, receiver size, height of the streetlight poles, and their corresponding intermediate distances.

This paper proposes a physical layer-based authentication scheme that exploits multiple features from the RF-front-end for wireless mesh networks. Specifically, we engineer the in-phase and quadrature-phase (IQ) samples of the legitimate nodes by generating specific ranges of carrier frequency offset (CFO), phase offset (PO), and DC offset (DCO). This engineered IQ governs all multiple legitimate node transmissions (to cover the entire ranges of CFO, PO, and DCO) and follows a specific probability mass function (PMF). We then obtain an optimal function based on the MSE criterion that closely fits the engineered IQ data, which serves as a reference for authenticating network nodes. In the authentication phase, the optimal function obtained from the IQ data transmissions of the respective node requesting authentication is compared with the optimal reference function. Successful authentication occurs when the difference between the optimal function and reference optimal function falls within predefined thresholds of absolute difference, MSE, and correlation coefficient parameters. Specifically, a node is deemed legitimate only when all three criteria meet the threshold requirements. The node undergoes a second authentication check if only one or two criteria are met. Otherwise, it is marked as a possible intruder. We generated extensive I and Q datasets following the IEEE 802.11 standard waveform to validate the proposed scheme, and the necessary metrics were evaluated. The results showed that instead of being used individually when the underlying criteria of MSE, correlation coefficient, and absolute difference are used together can guarantee better authentication, detection, and false detection rates. The findings indicate that the proposed approach attains a 100% authentication rate at a 5 × 10–2threshold MSE, which represents a 20% improvement over the individual use of MSE.

In this paper, we investigate the performance of reconfigurable intelligent surface (RIS)-assisted aerial communications, where a ground base station (GBS) communicates with distant terrestrial and/or aerial users through the assistance of a RIS-equipped unmanned aerial vehicle (RIS-UAV). The GBS uses the non-orthogonal multiple access (NOMA) scheme to transmit its signal, which is directed to the users via the RIS-UAV. First, the end-to-end channel is characterized, by considering the shadowed Rician fading, then the outage probability performance metric is derived for the underlying system model. Through numerical results, we demonstrate the impact of several system parameters on the performance of NOMA users. Specifically, we found that RIS elements need to be carefully allocated among different NOMA users, according to their channel conditions, in order to achieve the needed quality of service.

This article focuses on minimizing the energy consumption of a fleet of unmanned aerial vehicles (UAVs) disseminating information to a set of Internet of Things devices. In the considered scenario, each device wants to download a subset of files from a library of files. Considering the storage capacity of the UAVs, a framework is provided that minimizes energy consumption by optimally selecting the contributing UAVs, placing files, and planning the trajectory of each contributing UAV. In this framework, a combinatorial optimization problem is formulated, which is hard to solve directly for a practical number of devices, files, and/or UAVs. In order to tackle this challenge, we develop three solution approaches, namely, a multichromosome genetic algorithm (GA), a hybrid genetic-ant colony algorithm, and a GA with heuristic file placement. Results show that the proposed solution approaches minimize the total energy consumption and provide near-optimal solutions. Results also illustrate that the proposed framework optimizes the number of UAVs participating in the information delivery mission.

Jamming attacks, as a form of a denial-of-service attack, significantly degrade the performance of wireless communication systems and can lead to significant overhead in terms of re-transmissions and increased power consumption. In this work, we demonstrate the optimal jamming waveform in internet-of-things (IoT) networks. In particular, we present the analytical bit error rate (BER) of the system under attack by employing two common jamming waveforms: Gaussian noise, and digitally modulated. Then, we validate this analysis with the aid of simulations using the MATLAB WLAN toolbox. Obtained analytical and simulation results, demonstrated system performance degradation under jamming attacks. The simulation results agree with the analytical results in terms of determining the effective jamming waveform. Furthermore, the simulation results depict a 100% PER when the jamming to signal ratio (JSR) is OdB for both QPSK modulated and Gaussian noise waveforms which corroborates with the findings in the literature.

Non-linear Multiple-Input Multiple-Output (MIMO) has attracted considerable attention because of its high power-efficiency characteristic, particularly in the fifth generation (5G) and beyond. This paper focuses on the non-linear MIMO baseband algorithms in power-efficiency networks. In previous works, Generalized Approximate Message Passing (GAMP) and importance sampling technique were used to solve the non-linear distortion in Halved Phase-Only (HPO-) MIMO system. However, its convergence rate becomes unstable, and it’s converge is not guaranteed in some cases. In this paper, to improve the efficiency of convergence rate, we propose Gaussian Mixture Model (GMM) based ExpectationMaximization (EM) signal processing algorithm in HPO MIMO system. We first transforme channel estimation and multiuser detection problems into generalized linear mixed problems under π-phase observations. Then, the GMM algorithm is used to estimate the distribution of π-phase observation. Meanwhile, the EM algorithm is used to estimate the recovered signal. Simulation results show that the proposed method achieves high convergence and has better performance than the reference GAMP algorithm.

Intelligent reflecting surface (IRS) has emerged as a potential technology to achieve smart wireless communications and high energy efficiency. On the other hand, nonorthogonal multiple access (NOMA)-enabled backscatter communications have shown a great potential in large-scale Internet of Things (IoT) networks. In this article, we consider a downlink IRS-assisted backscatter communication with NOMA. We further consider a two-user scenario with channel disparity from the base station. We first derive the probability density function of the sum of the modulus of reflected channels, where each channel follows the Rayleigh distribution with dissimilar variances. The respective and generalized closed-form outage probability expressions are derived for the considered scenario. Simulation results validate the accuracy of the analytical outage probability expressions. We demonstrate that the far user can achieve a superior performance with the increase of reflecting elements or the reflection coefficients.

This paper investigates different precoding strategies for rate splitting multiple access (RSMA) in the downlink of multi-cell visible light communication (VLC) networks. Since classical Shannon capacity formula does not hold for VLC, we first provide a lower bound on the channel capacity for RSMA in such interfering networks. Then, we formulate a spectral efficiency maximization problem to jointly find the optimal rate-splitting and transmit precoding. Beside that, since cell-edge users suffer from additional inter-cell interference, we propose to design the precoders of different RSMA signals utilizing coordinated beamforming (CB). Subsequently, aiming to improve the performance of the CB design for RSMA, while maintain a reduced complexity, we introduce two enhanced precoding strategies for RSMA. To the best of the authors’ knowledge such a contribution has not been considered before in the open literature. It is shown in the paper that the formulated optimization problem is non-convex and a sub-optimal, yet, a low complexity solution can be obtained efficiently using semi-definite relaxation combined with successive convex approximation. Through analytical results, we illustrate the flexibility and superiority of the proposed precoding strategies for RSMA over conventional coordinated space division multiple access and non-orthogonal multiple access for different scenarios and network loads.

We consider a generalized space shift keying (GSSK)-enabled multiple-input multiple-output (MIMO) ambient backscatter communication (ABC) system. We propose a scheme to exploit the multiple antenna structure of the system to achieve a lower error-rate performance than conventional ABC systems. Furthermore, we present a novel low complexity energy-based maximum likelihood (EML) GSSK detector, which does not require the perfect knowledge of the ambient source’s signal, unlike the conventional ABC receivers. To gain insights into the performance of the proposed scheme, we derive the exact pairwise error probability (PEP) of the EML detector and further obtain an upper bound on the probability of error. We also derive a simple asymptotic PEP expression as the number of antennas of the reader becomes large. Finally, we derive a simple, asymptotic PEP at the reader when the noise variance approaches zero, i.e., under the large signal-to-interference ratio regime. We validate our analysis through Monte Carlo simulations and show a small performance loss due to the approximations.

Large intelligent surface (LIS) has recently emerged as a potential enabling technology for 6G networks, offering extended coverage and enhanced energy and spectral efficiency. In this work, motivated by its promising potentials, we investigate the error rate performance of LIS-assisted nonorthogonal multiple access (NOMA) networks. Specifically, we consider a downlink NOMA system, in which data transmission between a base station (BS) and L NOMA users is assisted by an LIS comprising M reflective elements (REs). First, we derive the probability density function (PDF) of the end-to-end wireless fading channels between the BS and NOMA users. Then, by leveraging the obtained results, we derive an approximate expression for the pairwise error probability (PEP) of NOMA users under the assumption of imperfect successive interference cancellation. Furthermore, accurate expressions for the PEP for M = 1 and large M values ( M > 10) are presented in closed-form. To gain further insights into the system performance, an asymptotic expression for PEP in high signal-to-noise ratio regime, asymptotic diversity order, and tight union bound on the bit error rate are provided. Finally, numerical and simulation results are presented to validate the derived mathematical results.

Ensuring physical layer security is a crucial task in conventional and emerging communication systems, which are typically characterized by stringent quality of service and security requirements. This also accounts for wireless technologies in the context of the Internet of Things paradigm, which are expected to exhibit considerably increased computational complexity. Based on this, the present contribution investigates the secrecy outage performance of a dual-hop decode-and-forward (DF) mixed radio-frequency/ underwater optical wireless communication (RF/UOWC) system. Such wireless network configurations are particularly useful in efficient and demanding scenarios, such as military communications. Therefore, our analysis considers one single-antenna source node (S) communicating with one legitimate destination node (D) via a DF relay node (R) equipped with multiple antennas for reception. Particularly, the relay receives the incoming signal from S via an RF link, applies selection-combining (SC) technique, fully decodes it, re-encodes it, and then forwards it to the destination via a UOWC link. The communication is performed under the eavesdropper's attempt to intercept the S - R hop (RF side). In this context, a closed-form expression for the secrecy outage probability is derived along with a thorough asymptotic analysis in the high SNR regime, based on which the achievable diversity order is provided. The offered results provide useful insights on the impact of some key system and channel parameters on the secrecy outage performance, such as the number of eavesdroppers, the number of relay antennas, fading severity parameters of RF links, and water turbulence severity of the UOWC link. The conducted analysis shows that the secrecy outage probability is dominated only by the R-D link in the high SNR regime, regardless of the S-R parameters, such as the number of relay antennas and the average SNR at the relay branches. The offered analytic results are corroborated with respective results from computer simulations. Since these parameters are closely related with the computational complexity at the involved terminals, the offered insights are useful for the design and computationally sustainable operation of such systems.

The present work investigates the physical layer security of wireless communication systems over non-homogeneous fading environments, i.e., η-μ and λ-μ fading models, which are typically encountered in realistic wireless transmission scenarios in the context of conventional and emerging communication systems. This study considers a single-input multiple-output system that consists of a single-antenna transmitter, a multi-antenna legitimate receiver, and an active multi-antenna eavesdropper. To this end, novel exact analytical expressions are derived for the corresponding average secrecy capacity and secrecy outage probability, which are corroborated by respective results from computer simulations. Capitalizing on the offered results, the physical layer security is quantified in terms of different parameters, which leads to useful insights on the impact of non-homogeneous fading environment and the number of employed antennas on the achieved physical layer security levels of the underlying system configuration. The offered results and insights are useful for the design of such systems as well as for the computational requirements and sustainability relating to such systems, since emerging communications are largely characterized by stringent quality of service and complexity requirements.

In this paper, we analyze the performance of relay-assisted (RA) non-orthogonal multiple access (NOMA) power line communications (PLC) systems. For a setup of three PLC modems, namely, a source (S), relay (R), and a destination (D), we present closed-form expressions for the outage probabilities at R and D. We consider maximal ratio combining at D and decode-and-forward protocol at R. Moreover, we formulate an optimization problem and present a closed-form solution for the optimal power allocation coefficients at S. Furthermore, we show that numerical and Monte Carlo simulation results corroborate the derived analytic expressions.

Non-orthogonal multiple access (NOMA)-based cognitive radio (CR) networks have recently emerged as a promising solution to enhance the spectral efficiency and massive connectivity problems. In this paper, we investigate the error rate performance of relay-assisted NOMA with partial relay selection in an underlay cognitive radio network. In this setup, K relays are used to assist in transmission between secondary NOMA users and a secondary base station (SBS), where the relay (R) with the strongest link with the SBS is selected to amplify-and-forward (AF) its received signals to the secondary receivers. We derive an accurate approximation for the pairwise error probability (PEP) of the secondary users with imperfect successive interference cancellation (SIC). Subsequently, the derived PEP expression is utilized to deduce a union bound, which is considered as an upper bound on the bit error rate (BER). We further formulate an optimization problem to calculate the optimum power coefficients that minimize the derived union bound. Numerical and Monte Carlo simulation results are presented to corroborate the derived analytical expressions and give some useful insights into the error rate performance of each user.

The present contribution proposes a spectrally efficient censor-based cooperative spectrum sensing (C-CSS) approach in a sustainable cognitive radio network that consists of multiple antenna nodes and experiences imperfect sensing and reporting channels. In this context, exact analytic expressions are first derived for the corresponding probability of detection, probability of false alarm, and secondary throughput, assuming that each secondary user (SU) sends its detection outcome to a fusion center only when it has detected a primary signal. Capitalizing on the findings of the analysis, the effects of critical measures, such as the detection threshold, the number of SUs, and the number of employed antennas, on the overall system performance are also quantified. In addition, the optimal detection threshold for each antenna based on the Neyman-Pearson criterion is derived and useful insights are developed on how to maximize the system throughput with a reduced number of SUs. It is shown that the C-CSS approach provides two distinct benefits compared with the conventional sensing approach, i.e., without censoring: i) the sensing tail problem, which exists in imperfect sensing environments, can be mitigated; and ii) less SUs are ultimately required to obtain higher secondary throughput, rendering the system more sustainable.

The present contribution analyzes the performance of non-orthogonal multiple access (NOMA)-based user cooperation with simultaneous wireless information and power transfer (SWIPT). In particular, we consider a two-user NOMA-based cooperative SWIPT scenario, in which the near user acts as a SWIPT-enabled relay that assists the farthest user. In this context, we derive analytic expressions for the pairwise error probability (PEP) of both users assuming the both amplify-and-forward (AF) and decode-and-forward (DF) relay protocols. The derived expressions are expressed in closed-form and have a tractable algebraic representation which renders them convenient to handle both analytically and numerically. In addition to this, we derive a simple asymptotic closed-form expression for the PEP in the high signal-to-noise ratio (SNR) regime which provide useful insights on the impact of the involved parameters on the overall system performance. Capitalizing on this, we subsequently quantify the maximum achievable diversity order of both users. It is shown that numerical and simulation results corroborate the derived analytic expressions. Furthermore, the offered results provide interesting insights into the error rate performance of each user, which are expected to be useful in future designs and deployments of NOMA based SWIPT systems.

The present contribution investigates the physical layer security in a cognitive radio network (CRN). To this end, we consider an underlay uplink CRN consisting of multiple secondary sources, a single-antenna secondary base station, and multiple eavesdroppers. In addition, we assume that the secondary sources transmit their data sequentially and that a jammer is randomly chosen from the remaining source nodes to send a jamming signal to the eavesdroppers. However, in an uplink underlay CRN, a friendly jammer is not always allowed to use its maximal transmit power as the secondary users are required to continuously adapt their power in order to avoid causing interference to the primary users. As a consequence, enhancing the system security using a jammer with low transmit power in the presence of numerous eavesdroppers turns out to be questionable. In this regard, we derive novel analytic expressions that assist in quantifying the achievable security levels and the corresponding limitations. This leads to the development of useful insights on the impact of network parameters on the performance of the system's security. The offered analytic results are corroborated through Monte Carlo simulation. It is shown, that for a low transmit power of the friendly jammer, the system's security can only be enhanced for a small number of eavesdroppers.

In this paper, we quantify the effect of residual hardware impairments (RHI) on the error rate performance of a relay-based non-orthogonal multiple access (NOMA) system, where the communication between a source node and multiple users is completed via an amplify-and-forward (AF) relay node. In particular, we focus on the pairwise error probability (PEP) analysis and derive an accurate PEP approximation to characterize the performance of NOMA users under Rayleigh fading channels. The derived PEP expression is then exploited to investigate the diversity gain and the union bound on the bit error rate (BER) of the underlying system. Our results demonstrate that the presence of RHI causes an error floor at high signal-to-noise ratio (SNR) values. This error floor yields a detrimental effect on the achievable diversity order of NOMA users, where it is shown that the diversity order of all users converges to zero.

The present work investigates the performance of non-orthogonal multiple access (NOMA) in a hybrid visible light communication (VLC) / radio frequency (RF) wireless network. In particular, we investigate the energy efficiency of the proposed architecture assuming imperfect channel state information (CSI), which is a realistic assumption that is encountered in practical indoor and outdoor wireless communication scenarios. We demonstrate that the performance of the proposed scheme in terms of energy efficiency outperforms by four-fold the corresponding performance of its orthogonal frequency division multiple access (OFDMA) counterpart. In addition, it is shown that the energy efficiency of the proposed scheme is more robust to CSI errors and line of sight (LOS) variations than the OFDMA-based scheme, which appears to be more susceptible to the CSI error and the LOS availability probability. Finally, our findings reveal that the performance gain of NOMA over OFDMA in the considered hybrid VLC/RF set up is directly proportional to the probability of LOS availability. These results are expected to be useful in the efficient design and efficient operation of hybrid VLC/RF wireless systems.

We propose a novel opportunistic ambient backscatter communication (ABC) framework for radio frequency (RF)-powered cognitive radio (CR) networks. The proposed framework considers opportunistic spectrum sensing integrated with ABC and harvest-then-transmit (HTT) operation strategies. Novel analytic expressions are derived for the average throughput, average energy consumption and energy efficiency in the considered set up. In addition, we formulate an optimization problem to maximize the energy efficiency of the CR system operating in mixed ABC- and HTT-modes, subject to primary interference and energy harvesting constraints. Next, we determine the optimal set of parameters which in turn comprise the optimal detection threshold, and the optimal degree of tradeoff between the CR system operating in the ABC- and HTT-modes. We present extensive numerical results to corroborate our analysis and to demonstrate the performance gain of the proposed model in terms of energy efficiency.

We propose a spectrally efficient censor-based cooperative spectrum sensing (C-CSS) approach for a sustainable cognitive radio network that consists of multiple antenna nodes and experiences imperfect sensing and reporting channels. First, analytic expressions are derived for the corresponding probabilities of detection and false alarm, assuming that each secondary user sends its detection outcome to a fusion center only when it believes to have detected a primary user's signal. Second, we derive lower bounds for the probability of false alarm, where we show that a sensing tail problem, which exist in the conventional (non-censor-based) scheme, can be effectively mitigated with the aid of the proposed C-CSS scheme. Simulation results are presented to corroborate the derived analytic results, and to provide theoretical and technical insights that are useful for the design of cognitive radio networks.

The F composite fading model was recently proposed as an accurate and tractable statistical model for the characterization of the composite fading conditions encountered in realistic wireless communication scenarios. In the present contribution we capitalize on the distinct properties of this composite model to evaluate the achievable effective rate over F composite fading channels. To this end, we derive an exact closed-form expression for the effective rate, which is subsequently used as a benchmark for the derivation of tight upper and lower bounds, as well as of an accurate approximation. The derived analytic expressions are provided in closed-form and benefit from being tractable both analytically and numerically. This enables the development of meaningful insights on the effect of fading conditions and/or latency on the overall system performance. Also, it allows the accurate quantification of the signal to noise ratio required in target quality of service requirements under different composite fading conditions.

Non-orthogonal multiple access (NOMA) is currently considered as a promising technology for the next-generation wireless networks. In this paper, the error rate performance of NOMA systems is investigated over Nakagami-m fading channels, while considering imperfect successive interference cancellation. In particular, this paper focuses on the pairwise error probability (PEP) analysis, where exact PEP expressions are derived to characterize the performance of all users under different fading conditions. The obtained PEP expressions are then used to derive an exact union bound on the bit error rate (BER). Through the derived PEP expressions, the asymptotic PEP analysis is presented to investigate the maximum achievable diversity gain of NOMA users. Moreover, using the derived BER bound, the power allocation problem for all users in NOMA systems is considered under average power and users BER constraints, which allows realizing the full potential of NOMA. Monte Carlo simulation and numerical results are presented to corroborate the derived analytical expressions and give valuable insights into the error rate performance of each user and the achievable diversity gain.

One of the major goals of emerging wireless systems is to prolong the lifetime of wireless communication devices. To this end, this contribution evaluates and optimizes the performance of simultaneous wireless information and power transfer (SWIPT) with an integrated energy and information receiver, which has the advantage of low complexity and energy cost. A tractable expression for the achievable rate is first derived, which is subsequently used to quantify the achievable harvested energy-rate region for the two fundamental SWIPT protocols, namely, power-splitting (PS) and time-switching (TS). In this context, the joint harvested energy-rate outage probability is then defined and minimized for a point-to-point and multicasting system, determining the optimal PS and TS factors for both linear and nonlinear energy harvesting models. In addition, a TS-based broadcasting system is dynamically optimized by maximizing the energy harvested by all users under an achievable rate threshold for each user. The formulated optimization problem is, in fact, particularly challenging due to the non-convex form of the expression for the achievable rate. Yet, an effective solution is ultimately achieved by converting this problem into a convex one. Also, respective computer simulation results corroborate the effectiveness of the proposed framework. Overall, it is shown that the offered results provide meaningful theoretical and practical insights that will be useful in the design and efficient operation of wireless powered systems. Indicatively, unlike the trend in common separated receivers, a region has been identified, where TS outperforms PS.

In this paper, we investigate the performance of energy detection-based spectrum sensing over F composite fading channels. To this end, an analytical expression for the average detection probability is first derived. This expression is then extended to account for collaborative spectrum sensing, square-law selection diversity reception, and noise power uncertainty. The corresponding receiver operating characteristics (ROC) are analyzed for different conditions of the average signal-to-noise ratio (SNR), noise power uncertainty, time-bandwidth product, multipath fading, shadowing, number of diversity branches, and number of collaborating users. It is shown that the energy detection performance is sensitive to the severity of the multipath fading and the amount of shadowing, whereby even small variations in either of these physical phenomena can significantly impact the detection probability. As a figure of merit to evaluate the detection performance, the area under the ROC curve (AUC) is derived and evaluated for different multipath fading and shadowing conditions. Closed-form expressions for the differential entropy and cross entropy are also formulated and assessed for different average SNR, multipath fading, and shadowing conditions. Then, the relationship between the differential entropy of F composite fading channels and the corresponding ROC/AUC is examined where it is shown that the average number of bits required for encoding a signal becomes small (i.e., low differential entropy) when the detection probability is high or when the AUC is large. The difference between composite fading and traditional small-scale fading is emphasized by comparing the cross entropy for Rayleigh and Nakagami-m fading. A validation of the analytical results is provided through a careful comparison with the results of some simulations.

We investigate the energy efficiency of a conventional collaborative compressed sensing (CCCS) scheme in cognitive radio networks. In particular, we derive expressions for the throughput, energy consumption and energy efficiency, and analyze the trade-off between the achievable throughput and the energy consumption of the underlying CCCS scheme. Furthermore, we formulate a multiple variable non-convex optimization problem to determine the optimum compression level that maximizes the energy efficiency, subject to interference constraints. We propose a sub-optimal solution based on tight approximations to simplify the aforementioned optimization problem, and further demonstrate that the energy efficiency achieved by the CCCS scheme is higher than that of conventional collaborative sensing scheme, under the same predefined conditions. It is further shown that the increase in the energy efficiency of CCCS scheme is due to the considerable decrease in the energy consumption, which is particularly noticeable with a large number of sensors.

In time-division-duplexing (TDD) massive multiple-input multiple-output (MIMO) systems, channel reciprocity is exploited to overcome the overwhelming pilot training and the feedback overhead. However, in practical scenarios, the imperfections in channel reciprocity, mainly caused by radio-frequency mismatches among the antennas at the base station side, can significantly degrade the system performance and might become a performance limiting factor. In order to compensate for these imperfections, we present and investigate two new calibration schemes for TDD-based massive multi-user MIMO systems, namely, relative calibration and inverse calibration. In particular, the design of the proposed inverse calibration takes into account a compound effect of channel reciprocity error and channel estimation error. We further derive closed-form expressions for the ergodic sum rate, assuming maximum ratio transmissions with the compound effect of both errors. We demonstrate that the inverse calibration scheme outperforms the traditional relative calibration scheme. The proposed analytical results are also verified by simulated illustrations.

Recently, visible light communication (VLC) networks have emerged as a possible alternative for data access, primarily indoors. The very high data rates, low implementation cost and free from radio frequency (RF) interference property make them particularly attractive for the next generation of indoor networking. Furthermore, non-orthogonal multiple access (NOMA) is a very promising candidate technique for the next generation of wireless networks, mainly due to its increased spectrum efficiency, compared to orthogonal access techniques. In this paper, we investigate, for the first time in existing literature, the practical indoor scenario of a hybrid VLC/RF network, where both VLC and RF subsystems perform NOMA. More specifically, we study the user grouping through the coalitional game theory, where each coalition is assigned to a specific access point, VLC or RF. Note that due to NOMA's particularities, optimal user grouping is still an open problem of research. Computer simulations illustrate the accuracy of the analysis and reveal the effectiveness of the proposed scheme compared to the standard opportunistic one, as well as its robustness with respect to the number of users.

This paper investigates the performance of energy detection-based spectrum sensing over Fisher-Snedecor F fading channels. To this end, an analytical expression for the corre- sponding average detection probability is firstly derived and then this is extended to account for collaborative spectrum sensing. The complementary receiver operating characteristics (ROC) are analyzed for different conditions of the average signal-to- noise ratio (SNR), time-bandwidth product, multipath fading, shadowing and number of collaborating users. It is shown that the energy detection performance is strongly linked to the severity of the multipath fading and amount of shadowing, whereby even small variations in either of these physical phenomena significantly impact the detection probability. Also, the versatile modeling capability of the Fisher-Snedecor F distribution is verified in the context of energy detection based spectrum sensing as it provides considerably more accurate characterization than the conventional Rayleigh fading model. To confirm the validity of the analytical results presented in this paper, we compare them with the results of some simulations.

This paper presents an efficient blind channel estimation technique for orthogonal frequency division multiplexing (OFDM) systems over-time varying channels. New frame structure is proposed, where different modulation schemes are employed to estimate the time-varying channel coefficients. Amplitude shift keying (ASK) and phase shift keying (PSK) modulation schemes are utilized to modulate particular pair of subcarriers over consecutive OFDM symbols, where the ASK and PSK symbols cooperate to enable blind estimation of the channel coefficients. In particular, PSK modulated symbols are employed in the amplitude- coherent detector (ACD) to allow blind detection for the ASK symbols. After that, the detected ASK symbols, with interpolation, are used to estimate the channel coefficients for the full frame. Exact closed-form expression for the symbol error rate (SER) of the ASK symbols is derived and corroborated with Monte Carlo simulations to evaluate the performance of the proposed technique and compare it with the pilot based OFDM system. Analytical and simulation results show that the proposed estimator can provide estimation with accuracy and computational complexity that are comparable to pilot based estimators.

The explosive growth of connected devices and the increasing number of broadband users have led to an unprecedented growth in traffic demand. To this effect, the next generation wireless systems are envisioned to meet this growth and offer a potential data rate of 10 Gbps or more. In this context, an attractive solution to the current spectrum crunch issue is to exploit the visible light spectrum for the realization of high-speed commutation systems. However, this requires solutions to certain challenges relating to visible light communications (VLC), such as the stringent requirements of VLC-based intensity modulation and direct detection (IM/DD), which require signals to be real and unipolar. The present work proposes a novel power-domain multiplexing based optical asymmetric modulation (OAM) scheme for indoor VLC systems, which is particularly adapted to transmit high-order modulation signals using linear real and unipolar constellations that fit into the restrictions of IM/DD systems. It is shown that the proposed scheme provides improved system performance that outperforms alternative modulation schemes, at no extra complexity.

In-phase/quadrature-phase Imbalance (IQI) is considered a major performance-limiting impairment in direct-conversion transceivers. Its effects become even more pronounced at higher carrier frequencies such as the millimeter-wave frequency bands considered for 5G systems. In this work, we quantify the effects of IQI on the performance of different modulations under multipath fading channels. This is realized by developing a comprehensive framework for the symbol error rate (SER) analysis of coherent phase shift keying (PSK), noncoherent differential phase shift keying (DPSK) and noncoherent frequency shift keying (FSK) under IQI effects. In this context, the moment generating function of the signal-to-interference-plus-noise-ratio is first derived for single-carrier systems suffering from transmitter (TX) IQI only, receiver (RX) IQI only and joint TX/RX IQI. Capitalizing on this, we derive analytic expressions for the SER of the different modulation schemes considered. These expressions are corroborated with simulation results and they provide insights into the dependence of IQI on the system parameters. We further demonstrate that, while in some cases, IQI can cause a slight degradation of the SER performance and, hence, it can be neglected, in other cases it should be compensated in order to achieve a reliable communication link.

Novel composite fading models were recently proposed based on inverse gamma distributed shadowing conditions. These models were extensively shown to provide remarkable modeling of the simultaneous occurrence of multipath fading and shadowing phenomena in emerging wireless scenarios such as cellular, off-body and vehicle-to-vehicle communications. Furthermore, the algebraic representation of these models is rather tractable, which renders them convenient to handle both analytically and numerically. Based on this, the present contribution analyzes the ergodic capacity over the recently proposed $\kappa-\mu$ / inverse gamma composite fading channels, which were shown to characterize excellently multipath fading and shadowing in line-of-sight communication scenarios, including realistic vehicular communications. Novel analytic expressions are derived which are subsequently used in the analysis of the corresponding system performance. In this context, the offered results are compared with respective results from cases assuming conventional fading conditions, which leads to the development of numerous insights on the effect of the multipath fading and shadowing severity on the achieved capacity levels. It is expected that these results will be useful in the design of timely and demanding wireless technologies such as wearable, cellular and inter-vehicular communications.

Non-orthogonal multiple access (NOMA) has been recently proposed as a viable technology that has the potential to improve the spectral efficiency of fifth generation (5G) wireless networks and beyond. However, in practical communication scenarios, transceiver architectures inevitably suffer from radio-frequency (RF) front-end related impairments that can lead to non-negligible degradation of the overall system performance. In this context, in-phase/quadrature-phase imbalance (IQI) constitutes a major impairment in direct-conversion transceivers. Based on this, the present contribution quantifies the effects of IQI on the performance of NOMA based systems under multipath fading conditions. This is realized by first deriving novel analytic expressions for the signal-to-interference-plus-noise ratio and the outage probability of NOMA systems subject to IQI at the transmitter and/or the receiver sites. Capitalizing on these results, we demonstrate that the effects of IQI differ considerably between the different NOMA users and depending on the considered system's parameters.

The η-μ / inverse gamma and κ-μ / inverse gamma distributions were recently introduced as particularly flexible and tractable composite fading models that provide accurate characterization of multipath and shadowing effects, which are encountered simultaneously during wireless transmission in emerging communication scenarios such as off-body, cellular and vehicular-to-vehicular communications. The present contribution analyzes the symbol error rate performance of digital communications over these fading channels. To this end, we derive novel analytic expressions for the symbol error rate of multiple amplitude based modulated systems under these fading conditions, which are subsequently used in the analysis of the corresponding system performance. In this context, numerous insights are developed on the effect of different fading conditions on the corresponding error rate, which are expected to be useful in the design of timely and demanding wireless technologies such as wearable, cellular and vehicular communication systems.

The Fisher-SnedecorFdistribution was recently proposed as an accurate and tractable composite fading model in the context of device-to-device communications. The present work derives the product of the Fisher-SnedecorFcomposite fading model, which is useful in characterizing fading effects in numerous realistic communication scenarios. To this end, novel analytic expressions are first derived for the probability density function, the cumulative distribution function and the moment of the product ofNstatistically independent, but not necessarily identically distributed, Fisher-SnedecorFrandom variables. Capitalizing on these expressions, we derive tractable closed-form expressions for channel quality estimation of the proposed model as well as the corresponding outage probability and average bit error probability for binary modulations. The offered results are corroborated by extensive Monte-Carlo simulation results, which verify the validity of the derived expressions. It is shown that the number of cascaded channels affects considerably the corresponding performance, as a variation of over an order of magnitude is observed across all signal-to-noise ratio regimes.

The performance of physical-layer security of the classic Wyner's wiretap model over Fisher-Snedecor F composite fading channels is considered in this work. Specifically, the main channel (i.e., between the source and the legitimate destination) and the eavesdropper's channel (i.e., between the source and the illegitimate destination) are assumed to experience independent quasi-static Fisher-Snedecor F fading conditions, which have been shown to be encountered in realistic wireless transmission scenarios in conventional and emerging communication systems. In this context, exact closed-form expressions for the average secrecy capacity (ASC) and the probability of non-zero secrecy capacity (PNSC) are derived. Additionally, an asymptotic analytical expression for the ASC is presented. The impact of shadowing and multipath fading on the secrecy performance is investigated. Our results show that increasing the fading parameter of the main channel and/or the shadowing parameter of the eavesdropper's channel improves the secrecy performance. The analytical results are compared with Monte-Carlo simulations to validate the analysis.

Visible light communication (VLC) systems are expected to provide remarkably high speed indoor communications and effective ubiquitous connectivity. However, the key limitation of such systems is the narrow modulation bandwidth of the light sources. Based on this, non-orthogonal multiple access (NOMA) has been recently proposed as an effective method that can enhance considerably the spectral efficiency of indoor downlink VLC systems. In this context, the present work is devoted to the evaluation of the bit-error-rate (BER) performance of NOMA-based VLC systems. Specifically, a novel closed-form expression is first derived for the BER of the considered set up, by also taking into account the realistically incurred cancellation errors and interference terms. The validity of the derived expressions is verified through extensive comparisons with respective results from Monte Carlo simulations, while their algebraic representation is relatively simple, which renders them convenient to handle both analytically and numerically. This leads to meaningful insights on the behavior and performance gains achieved, thanks to the adoption of NOMA, which are particularly useful in future design and deployment of VLC systems.

Coherent detection requires exact knowledge of the channel state information, which is often a challenging task in demanding practical applications. Based on this, non-coherent detection of differentially modulated signals can be considered as an alternative method. The present paper investigates the effects of in-phase/quadrature-phase imbalance (IQI), which are known to degrade the performance of wireless communication systems. Specifically, we evaluate the effects of IQI on the bit error rate (BER) performance of differential quadrature phase shift keying (dQPSK) for ideal receiver (RX) with transmitter (TX) IQI, ideal TX with RX IQI and joint TX/RX IQI. Explicit analytic expressions are derived for the BER of both single-carrier and multi-carrier systems suffering from IQI at the TX and/or RX. Extensive Monte-Carlo simulation as well as offered analytic results show that realistic TX/RX IQI values can degrade the corresponding BER by over 30%. Likewise, it is shown that the detrimental effects of IQI are more considerable on DQPSK than on QPSK.

In this paper, we investigate the application of differential modulation in simultaneous wireless information and power transfer (SWIPT) relay networks. Considering time switching (TS) and power splitting (PS) receiver architectures, we adopt a moments-based approach to derive novel expressions for the outage probability and throughput of SWIPT relay systems with the amplify-and-forward (AF) relaying protocol. We quantify the impact of several system parameters involving the energy conversion efficiency and the TS and PS ratio assumptions, imposed on the energy harvesting (EH) relay terminal. Our results reveal that the throughput performance of the TS protocol is superior to that of the PS protocol at lower receive signal-to-noise (SNR) values, which is in contrast to point-to-point SWIPT systems. A Monte Carlo simulation study is presented to corroborate the proposed analysis.

Channel reciprocity in time-division duplexing (TDD) massive multiple-input multiple-output (MIMO) systems can be exploited to reduce the overhead required for the acquisition of channel state information (CSI). However, perfect reciprocity is unrealistic in practical systems due to random radio-frequency (RF) circuit mismatches in uplink and downlink channels. This can result in a significant degradation in the performance of linear precoding schemes, which are sensitive to the accuracy of the CSI. In this paper, we model and analyse the impact of RF mismatches on the performance of linear precoding in a TDD multi-user massive MIMO system, by taking the channel estimation error into considerations. We use the truncated Gaussian distribution to model the RF mismatch, and derive closed-form expressions of the output signal-to-interference-plus-noise ratio for maximum ratio transmission and zero forcing precoders. We further investigate the asymptotic performance of the derived expressions, to provide valuable insights into the practical system designs, including useful guidelines for the selection of the effective precoding schemes. Simulation results are presented to demonstrate the validity and accuracy of the proposed analytical results.

Visible light communication (VLC) has been proposed as a promising and efficient solution to indoor ubiquitous broadband connectivity. In this paper, non-orthogonal multiple access, which has been recently introduced as an effective scheme for fifth generation (5G) wireless networks, is considered in the context of VLC systems under different channel uncertainty models. To this end, we first derive a novel closed-form expression for the bit-error-rate (BER) under perfect channel state information (CSI). Capitalizing on this, we then quantify the effect of noisy and outdated CSI by deriving a simple and accurate approximation for the former and a tight upper bound for the latter. The offered results are corroborated by respective results from extensive Monte Carlo simulations and assist in developing useful insights on the effect of imperfect CSI knowledge on the overall system performance. Furthermore, it was shown that while noisy CSI leads to slight degradation in the BER performance, outdated CSI can cause considerable performance degradation, if the order of the users' channel gains change due to the involved mobility.

This paper is devoted to the end-to-end performance analysis, optimal power allocation (OPA), and energy-efficiency (EE) optimization of decode-and-forward (DF)-based full-duplex relaying (FDR) and half-duplex relaying (HDR) systems. Unlike existing analyses and works that assume simplified transmission over symmetric fading channels, we consider the more realistic case of asymmetric multipath fading and shadowing conditions. To this end, exact and asymptotic analytic expressions are first derived for the end-to-end outage probabilities (OPs) of the considered DF-FDR set ups. Based on these expressions, we then formulate the OPA and EE optimization problems under given end-to-end target OP and maximum total transmit power constraints. It is shown that OP in FDR systems is highly dependent upon the different fading parameters and that OPA provides substantial performance gains, particularly, when the relay self-interference (SI) level is strong. Finally, the FDR is shown to be more energy-efficient than its HDR counterpart, as energy savings beyond 50% are feasible even for moderate values of the SI levels, especially at larger link distances, under given total transmit power constraints and OP requirements.

In this paper, we investigate joint relay selection and optimal power allocation, as a means to maximize the achievable rate of an underlay cooperative cognitive radio with carrier aggregation, taking into account the availability of multiple carrier components in two different bands and primary users (PUs) with specific average outage probability requirements. For the acquisition of the interference thresholds, which are set by the PUs on the secondary user (SU), we incorporate a minimum feedback strategy into the problem formulation, based on the minimization of the PUs outage probabilities. The resulting non-convex optimization problem is transformed into a convex one and optimally solved using dual decomposition and an efficient iterative method with closed-form power policies. Simulation results illustrate that the proposed configuration exploits the available degrees of freedom in an efficient way which maximizes the SU throughput while the average outage probability of the PUs is kept at acceptable levels.

The present work quantifies the effects of asymmetric fading conditions on differentially modulated amplify-and-forward relaying systems. To this end, novel bit error rate expressions are derived for the case that the source-relay and relay-destination links experience non-line-of-sight multipath fading whilst the source-destination link is subject to: multipath fading, shadowing, and composite fading. Simple and tight approximate and asymptotic expressions are also derived, leading to useful insights into the system design. It is shown that the incurred performance variations range from one to few orders of magnitude compared to the standard case of symmetric Rayleigh scenarios, which verifies the importance to account for fading conditions realistically. In addition, differential phase-shift keying is shown to provide adequate performance in severe fading conditions in the moderate and high-signal-to-noise ratio regimes.

In this letter, the performance of differential modulation in simultaneous wireless information and power transfer (SWIPT) cooperative amplify-and-forward (AF) networks is investigated. In particular, we derive novel closed-form expressions for the probability density function (pdf) of the end-to-end signal-to-noise ratio (SNR) and the average bit error rate (ABER) of the considered SWIPT cooperative scenario. Based on the derived results, we analyze the impact of the underlying system parameters on the system performance. Numerical results show that the optimum location of the relay terminal is closer to the source than to the destination. Moreover, it is demonstrated that the value of the power splitting (PS) ratio at the relay significantly impacts the system performance. The results of Monte Carlo simulations are provided to corroborate the analysis.

Cooperative communication has been shown to provide significant increase of transmission reliability and network capacity while expanding coverage in cellular networks. The present work is devoted to the investigation of the end-to-end performance and power allocation of a maximum-ratio-combining based regenerative multi-relay cooperative network over non-homogeneous scattering environment, which is the realistic case in many practical wireless communication scenarios. Novel analytic expressions are derived for the end-to-end symbol-error-rate of both M-ary phase-shift keying and M-ary quadrature amplitude modulation over independent and non-identically distributed generalized fading channels are given by exact analytic expressions that involve the Lauricella function and can be readily evaluated with the aid of a proposed computing algorithm. Simple analytic expressions are also derived for the corresponding symbol-error-rate at asymptotically high signal-to-noise ratios. The derived expressions are corroborated with respective results from computer simulations and are subsequently employed in formulating a sum-power optimization problem that enhances the system performance under total sum-power constraint within the multi-relay cooperative system. It is also shown that asymptotically optimum power allocation provides substantial performance gains over the corresponding equal power allocation, particularly, when the source-relay and relay-destination paths are highly unbalanced.

Botnets, which consist of remotely controlled compromised machines called bots, provide a distributed platform for several threats against cyber world entities and enterprises. Intrusion detection system (IDS) provides an efficient countermeasure against botnets. It continually monitors and analyzes network traffic for potential vulnerabilities and possible existence of active attacks. A payload-inspection-based IDS (PI-IDS) identifies active intrusion attempts by inspecting transmission control protocol and user datagram protocol packet's payload and comparing it with previously seen attacks signatures. However, the PI-IDS abilities to detect intrusions might be incapacitated by packet encryption. Traffic-based IDS (T-IDS) alleviates the shortcomings of PI-IDS, as it does not inspect packet payload; however, it analyzes packet header to identify intrusions. As the network's traffic grows rapidly, not only the detection-rate is critical, but also the efficiency and the scalability of IDS become more significant. In this paper, we propose a state-of-the-art T-IDS built on a novel randomized data partitioned learning model (RDPLM), relying on a compact network feature set and feature selection techniques, simplified subspacing and a multiple randomized meta-learning technique. The proposed model has achieved 99.984% accuracy and 21.38 s training time on a well-known benchmark botnet dataset. Experiment results demonstrate that the proposed methodology outperforms other well-known machine-learning models used in the same detection task, namely, sequential minimal optimization, deep neural network, C4.5, reduced error pruning tree, and randomTree.

This work is devoted to the outage probability analysis of full-duplex (FD) regenerative relay systems over multipath fading channels. Unlike the majority of analyses that assume basic symmetric fading conditions, the present work considers asymmetric generalized fading conditions, which are more realistic in practical communications scenarios. To this end, we assume that the source-relay path is subject to κ - μ multipath fading conditions, that can also account for line-of-sight communications, whereas the source-to-destination and relay-to-destination paths are subject to η-μ fading conditions that typically hold for non-line-of-sight communications. Novel analytic expressions are derived for the outage probability (OP) of the considered FD as well as for the corresponding half-duplex (HD) relay case for comparisons. These expressions are given in closed-form and have a tractable algebraic representation which renders them convenient to handle both analytically and numerically. Based on this, they are subsequently employed in analyzing the corresponding performance for different communication scenarios. It is shown that the OP of the FD relay system is highly dependent upon the severity of fading and that its performance outperforms significantly the spectral efficiency increases.

Radio frequency (RF) front-ends constitute a fundamental part of both conventional and emerging wireless communication systems. However, in spite of their importance they are often assumed ideal, although they are practically subject to certain detrimental impairments, such as amplifier nonlinearities, phase noise and in phase and quadrature (I/Q) imbalance (IQI). The present work is devoted to the quantification and evaluation of the RF IQI effects in the context of realistic wireless vehicle-to-vehicle (V2V) communications over double-Nakagami-m fading channels. Novel closed form expressions are derived for the corresponding outage probability for the case of ideal transmitter (TX) and receiver (RX), ideal TX and I/Q imbalanced RX, I/Q imbalanced TX and ideal RX, and joint I/Q imbalanced TX/RX. The offered analytic results have a relatively convenient algebraic representation and their validity is extensively justified through comparisons with respective results from computer simulations. Based on these, it is shown that cascaded fading results to considerable degradations in the system performance and that assuming ideal RF front-ends at the TX and RX induces non-negligible errors in the outage probability evaluation that can exceed 20% in several V2V communication scenarios.

The present work is devoted to the analytic performance evaluation of cooperative spectrum sensing (CSS) over generalized fading channels. The proposed analysis is based on the efficient Gaussian-Finite-Mixture (GFM) that allows the derivation of a simple and accurate closed-form expression for the average probability of energy detection (ED) under different fading environments. Capitalizing on this, we derive generalized closed-form expressions for the global probabilities of detection for the CSS with two main hard centralized fusion rules, namely, the AND and the OR rules. The efficiency and usefulness of the proposed expressions is justified by comparing the corresponding complementary receiver operating characteristic (ROC) curves for both multipath and composite multipath/shadowing fading channels, which are otherwise particularly difficult to obtain. The offered analytic results are corroborated by respective results from computer simulations and it is shown that the corresponding performance depends significantly on both the severity of fading and the involved number of users in the collaborative network.

The analysis of composite fading channels, which are typically encountered in wireless channels due to multipath and shadowing is quite involved, as the underlying fading distributions do not lend themselves to analysis. An example of such channels are the Nakagami/Rayleigh-Lognormal fading channels. Several simplified expressions have been proposed in the literature. In this paper, a generalized fading model for composite and non-composite fading models, based on the so-called Mixture of Gaussians (MoG) distribution, is proposed. The well-known expectation-maximization algorithm is utilized to estimate the parameters of the MoG model. Furthermore, relying on the proposed MoG model, we derive closed form expressions for several performance metrics used in wireless communication systems, including the raw moments, the amount of fading, the outage probability, the average channel capacity, and the moment generating function. In addition, the symbol error rate of L-branch maximum ratio combining diversity receiver is studied for linear coherent signaling schemes. Monte Carlo simulations are presented to corroborate the analytical results and to assess the accuracy of the MoG model.

We propose a new antenna selection scheme for a massive MIMO system with a single user terminal and a base station with a large number of antennas. We consider a practical scenario where there is a realistic correlation among the antennas and imperfect channel estimation at the receiver side. The proposed scheme exploits the sparsity of the channel matrix for the effective selection of a limited number of antennas. To this end, we compute a sparse channel matrix by minimising the mean squared error. This optimisation problem is then solved by the well-known orthogonal matching pursuit algorithm. Widely used models for spatial correlation among the antennas and channel estimation errors are considered in this work. Simulation results demonstrate that when the impacts of spatial correlation and imperfect channel estimation introduced, the proposed scheme in the paper can significantly reduce complexity of the receiver, without degrading the system performance compared to the maximum ratio combining.

In this paper, cooperative spectrum sensing under perfect and imperfect feedback channels is investigated. In particular, the optimal number of cognitive radios (CRs) required to minimize the total error of the sensing process when a perfect/imperfect reporting channels is derived. We show that the total error is always greater than zero regardless of the number of cooperating nodes. Furthermore, we derive a general formula that represents the boundaries of false alarm probability, and demonstrate that tightening the value of this boundary alone will not result in an improvement of system performance. Additionally, we show that errors in the reporting feedback channels can easily be mitigated when the optimal number of CRs participating in the sensing process is used; therefore, the overall detection probability is improved. Interestingly, results show that errors in the feedback channels can improve the detection probability at a low signal to noise ratio (SNR) link between the primary users (PUs) and the CRs.

The present paper is devoted to the evaluation of energy detection based spectrum sensing over different multipath fading and shadowing conditions. This is realized by means of a unified and versatile approach that is based on the particularly flexible mixture gamma distribution. To this end, novel analytic expressions are firstly derived for the probability of detection over MG fading channels for the conventional single-channel communication scenario. These expressions are subsequently employed in deriving closed-form expressions for the case of square-law combining and square-law selection diversity methods. The validity of the offered expressions is verified through comparisons with results from respective computer simulations. Furthermore, they are employed in analyzing the performance of energy detection over multipath fading, shadowing and composite fading conditions, which provides useful insighs on the performance and design of future cognitive radio based communication systems.

This paper is devoted to the analytic investigation of a maximum-ratio-combining based regenerative multi-relay cooperative wireless network over non-homogeneous scattering environments. Such propagation conditions are rather realistic as they are encountered often in practical wireless transmission scenarios. Novel analytic expressions are derived for the symbol-error-rate of M-ary phase shift keying (M-PSK) over independently and non-identically distributed fading channels. The derived expressions are based on the moment-generating-function (MGF) approach and are given in closed-form in terms of the generalized Lauricella series. A simple algorithm for computing this special function is also proposed while the offered results are validated extensively through comparisons with respective results from computer simulations. Based on this, they are particularly useful in the analytic performance evaluation of such cooperative systems. To this end, it is shown that the performance of the cooperative system is significantly affected, as expected, by the number of employed relays as well as by the value of the involved fading parameters η and μ.

The present paper analyzes the outage probability of full-duplex (FD) regenerative relay systems over multipath fading channels. Unlike the majority of investigations that assume basic symmetric fading conditions, this analysis considers asymmetric generalized fading conditions, which are more realistic as they are encountered more often in practical wireless transmissions. To this end, it is assumed that the source-relay and source-destination links are subject to k -μ multipath fading conditions, which can represent generalized line-of-sight communication scenarios; on the contrary, the relay-to-destination link is subject to η-μ fading conditions that typically represent generalized non-line-of-sight communication scenarios. Novel analytic expressions are derived for the outage probability (OP) of the considered FD relaying system. These expressions are given in closed-form and have a relatively tractable algebraic form which renders them convenient to handle both analytically and numerically. To this effect, they are subsequently employed in analyzing the corresponding performance for various communication scenarios. It is shown that the OP of the FD relay system is, as expected, highly dependent upon the severity of fading, the relay self-interference and the interference from the direct link. Furthermore, it is shown that at relatively high average signal-to-noise ratio values, the outage probability at low fading severity and at high relay self interference outperforms the respective performance for the case of high fading severity, but with low relay self-interference. Based on this, the offered results can be useful in the design and deployment of future full-duplex based cooperative communication systems.

Novel analytic solutions are derived for integrals that involve the generalized Marcum Q-function, exponential functions and arbitrary powers. Simple closed-form expressions are also derived for specific cases of the generic integrals. The offered expressions are both convenient and versatile, which is particularly useful in applications relating to natural sciences and engineering, including wireless communications and signal processing. To this end, they are employed in the derivation of the average probability of detection in energy detection of unknown signals over multipath fading channels as well as of the channel capacity with fixed rate and channel inversion in the case of correlated multipath fading and switched diversity.

Accurate fading characterization and channel capacity determination are of paramount importance in both conventional and emerging communication systems. The present work addresses the non-linearity of the propagation medium and its effects on the channel capacity. Such fading conditions are first characterized using information theoretic measures, namely, Shannon entropy, cross entropy and relative entropy. The corresponding effects on the channel capacity with and without power adaptation are then analyzed. Closed-form expressions are derived and validated through computer simulations. It is shown that the effects of nonlinearities are significantly larger than those of fading parameters such as the scattered-wave power ratio, and the correlation coefficient between the in-phase and quadrature components in each cluster of multipath components.

Proline residues are common source of kinetic complications during folding. The X-Pro peptide bond is the only peptide bond for which the stability of the cis and trans conformations is comparable. The cis-trans isomerization (CTI) of X-Pro peptide bonds is a widely recognized rate-limiting factor, which can not only induces additional slow phases in protein folding but also modifies the millisecond and sub-millisecond dynamics of the protein. An accurate computational prediction of proline CTI is of great importance for the understanding of protein folding, splicing, cell signaling, and transmembrane active transport in both the human body and animals. In our earlier work, we successfully developed a biophysically motivated proline CTI predictor utilizing a novel tree-based consensus model with a powerful metalearning technique and achieved 86.58 percent Q2 accuracy and 0.74 Mcc, which is a better result than the results (70-73 percent Q2 accuracies) reported in the literature on the well-referenced benchmark dataset. In this paper, we describe experiments with novel randomized subspace learning and bootstrap seeding techniques as an extension to our earlier work, the consensus models as well as entropy-based learning methods, to obtain better accuracy through a precise and robust learning scheme for proline CTI prediction.

Wafer defects, which are primarily defective chips on a wafer, are of the key challenges facing the semiconductor manufacturing companies, as they could increase the yield losses to hundreds of millions of dollars. Fortunately, these wafer defects leave unique patterns due to their spatial dependence across wafer maps. It is thus possible to identify and predict them in order to find the point of failure in the manufacturing process accurately. This paper introduces a novel simplified subspaced regression framework for the accurate and efficient identification of defect patterns in semiconductor wafer maps. It can achieve a test error comparable to or better than the state-of-the-art machine-learning (ML)-based methods, while maintaining a low computational cost when dealing with large-scale wafer data. The effectiveness and utility of the proposed approach has been demonstrated by our experiments on real wafer defect datasets, achieving detection accuracy of 99.884% and R2of 99.905%, which are far better than those of any existing methods reported in the literature.

We investigate the use of differential space frequency block codes (SFBCs) with orthogonal frequency division multiplexing (OFDM) over underwater acoustic channels. While SFBC efficiently exploits spatial transmit diversity, differentially coherent detection eliminates the need for extensive signal processing required for channel tracking. System performance is demonstrated using real data transmitted in the 12-26 kHz acoustic band from a vehicle moving at 0.5-2 m/s and received over a 100 m shallow water channel, using 4-QAM and a varying number of carriers ranging from 128 to 2048. Performance results demonstrate the advantage of the differentially coherent SFBC detection over the conventional, coherent SFBC detection which suffers from imperfect channel estimation.

In this paper, we address the problem of detecting blackhole attack targeting the Multi Point Relays (MPRs), using Vehicular Ad hoc networks-Secure Optimized Link State Routing protocol (VANET-OLSR). In the literature, watchdog model was proposed to detect network related attacks. However, this technique relies on routing level monitoring, which suffers from high false positives due to channel collision. As a solution to improve the watchdog detection, we propose a cooperative intrusion detection system based on cross layer architecture, that correlates both MAC and network layers detections. This is achieved by (1) monitoring the number of RTS/CTS (request to send/clear to send) requests of the watchdogs and the detected nodes at the MAC layer; and (2) recalculating watchdogs detection percentage after aggregating the results with MAC monitors. Cooperative monitoring in both network and MAC levels helps recognize the presence of channel collision and hence, reduces the false alarm rate. Simulation results corroborate that the use of cooperative cross layer design improves the detection percentage and minimizes the false positive rate.

Cooperative communication techniques are used in vehicular ad hoc networks (VANETs) to enhance system performance through distributed spatial diversity. In this paper, the average error rate performance of a VANET Cooperative Selection-Combining Receiver (CO-SCR) is analyzed over generalized statistically measured fading channels. Source-Destination and Source-Relay-Destination links are assumed to be independent but not necessary identically distributed. The Gaussian finite mixture is utilized to mathematically formulate an approximation for the probability density function (PDF) of the received signal-to-noise ratio (SNR) at the output of CO-SCR at the destination vehicle. Then, the PDF of the SNR (SNR-PDF) expression is utilized to derive a closed-form expression for the approximated average error rate using different coherent modulation techniques. Monte-carlo simulation results are presented to validate the analysis.

Proline cis-trans isomerization (CTI) plays a key role in the rate-determining steps of protein folding. Accurate prediction of proline CTI is of great importance for the understanding of protein folding, splicing, cell signaling, and transmembrane active transport in both the human body and animals. Our goal is to develop a state-of-the-art proline CTI predictor based on a biophysically motivated intelligent consensus modeling through the use of sequence information only (i.e., position specific scores generated by PSI-BLAST). The current computational proline CTI predictors reach about 70-73 percent Q2 accuracies and about 0.40 Matthew correlation coefficient (Mcc) through the use of sequence-based evolutionary information as well as predicted protein secondary structure information. However, our approach that utilizes a novel decision tree-based consensus model with a powerful randomized-metal earning technique has achieved 86.58 percent Q2 accuracy and 0.74 Mcc, on the same proline CTI data set, which is a better result than those of any existing computational proline CTI predictors reported in the literature.

In this paper, we investigate cooperative Single-Carrier Frequency-Domain Equalization (SC-FDE) for two-way relay networks, where multiple users each equipped with multiple antennas exchange their information through a multi-antenna relay node in a bi-directional manner. Under network total power constraint, we present optimal relay beamforming for the multiuser two-way relay system, where the relay transceiver processor is designed based on the minimum mean-square-error (MMSE) criterion. We drive a closed-form expression for the signal-to-interference noise ratio (SEVR) at each of the user terminals, and further present a joint user-relay antenna selection algorithm by applying the estimation of distribution algorithm (EDA). The proposed EDA has a low computational complexity, and its effectiveness is verified through simulation results.

In this study, the authors investigate the outage probability of underlay cognitive radio systems with relay selection. In particular, they consider a secondary multi‐relay network operating in the amplify‐and‐forward (AF) mode and only the ‘best’ relay is selected, which satisfies an index of merit. The proposed selection strategy takes into consideration the effect of primary user (PU) interference. That is, the authors assume that the secondary multi‐relay network is exposed to unwanted interference from a neighboring PU network. They derive a closed‐form outage probability expression and further present a thorough asymptotic diversity order analysis of the underlying scenario. A simulation study is presented to corroborate the analytical results and to have further insight into the performance of the proposed selection strategy.

In this study, transmission of JPEG2000 images using an unequal power allocation (UPA) scheme and orthogonal frequency division multiplexing (OFDM) over block‐fading frequency‐selective channels is presented. A distortion model is provided to evaluate the contribution of each coding pass (CP) in the construction of the received image. The optimisation algorithm exploits the hierarchical structure of the JPEG2000 images and uses the distortion model along with the channel state information for allocating optimal values of power for each CP to minimise the end‐to‐end distortion. Furthermore, the actual total power consumed for transmission is measured and compared with the total power initially assigned. For the purpose of simulations, the authors set the number of OFDM subcarriers to be 16, the length of the cyclic prefixes equal to the channel memory length and analyse the quality of the received image in a 2‐tap and 3‐tap frequency‐selective channel, with and without our proposed UPA technique in an OFDM system. The results show an improvement of up to 10.5 dB in the decoded image quality when the UPA scheme is used. In addition, our system manages to maintain similar quality for the received image in a multi‐tap channel scenario.

In this paper, we investigate several relay selection strategies for cooperative Single-Carrier Frequency-Domain Equalization (SC-FDE) with the amplify-and-forward protocol. We consider both maximum likelihood (ML)-SC-FDE and minimum mean square error (MMSE)-SC-FDE receivers. We provide a novel pairwise error probability (PEP)-based selection criterion (SHARM) for frequency selective channels. We further present several selection strategies for cooperative (C) MMSE-SC-FDE receivers, which are motivated by minimizing the instantaneous error rate. These are, norm-based relay selection (NBRS), instantaneous mutual information-based relay selection (CBRS), singular value based relay selection (SVRS), and equalizer output signal quality-based relay selection (EQRS) strategies. We further propose a novel relay selection strategy, selective-to-flat fading relay selection (SFRS), in which from the effective frequency selective source-relay-destination channel link associated with the selected relay, only the channel tab with highest power is passed to the destination terminal. Additionally, to tackle the multiple relay selection problem considering generic mobile scenarios with moderately fast fading channels, in order to select the near best relay subset within the minimum processing time, we apply estimation of distribution algorithm (EDA) and formulate a modified EDA for the relay selection problem. Our results show promising performance of EDA with comparable computational complexity.

In this paper, we propose a handover algorithm for WiMAX networks. The algorithm relies on the computation of the received signal-to-noise ratio (SNR) at a Mobile Station (MS) from neighboring Base Stations (BSs) combined with the capacity estimation of the targeting cell. The proposed handover algorithm is implemented by a joint decision between the MS and BS nodes. The performance of the proposed algorithm is evaluated in terms of call dropping ratio and system throughput. A comparison with the conventional hard handover algorithm is presented.

In this paper, we investigate a novel Random-based Fair-Allocation Fuzzy Comprehensive Evaluation- based Strategy with Relay Clustering (RFRC) for dual-hop Single- Carrier Frequency-Domain Equalization (SC-FDE) in cooperative systems with multiple relays and amplify-and- forward (AF) relaying. We address the problem of fair resource allocation for SC-FDE minimum mean square error (MMSE) receivers. In particular, relays are classified into multiple disjoint clusters through the K-means algorithm, where each cluster is identified by a centroid. The centroids of the clusters are directly related to the average amount of resources available to the relays belonging to that cluster, as well as the quality of the relaying' links. The final centroids of the clusters after the convergence of the K-means algorithm, are used to generate contribution factors for each group. Different sub-channels are further associated with uniform random distribution where the thresholds of the uniform random variables are associated with the cluster's contribution factors. RFRC for SC-FDE proves superior SER performance as well as improved diversity gain which is achieved by randomizing the allocation. The employed algorithm further improves the performance by optimizing the number of clusters. Numerical results are provided to corroborate the mathematical modeling.

Deployment of relays with data storage capability is progressively becoming popular in the literature as it provides significant performance improvement over relays with non-storage capability. In this paper, we consider a multi-branch multi-hop decode-and-forward (DF) relay system. We assume each relay has data storage and the data can be stored temporarily if the next channel is not good enough to transmit through. We derive an asymptotic and approximate outage probability expression for the considered system under generalized fading channel. Based on the derived expression, we formulate an optimization problem which allocates source and relay transmit powers under different system requirements.

In this paper, we study the lifetime of a dualhop multi-relay wireless sensor network (WSN) OFDM-based system using an adaptive random relay selection strategy. In this strategy, the relays cooperate according to contribution factors associated with each relay. An analytical study of the system lifetime is presented along with simulation results. The results show that the allocation scheme guarantees fairness in allocating the resources in each relay. The results also show the impact of the number of relays on the lifetime of the WSN.

Error performance and power allocation for multi-hop decode-and-forward (DF) systems are addressed. The different channels treated are Rayleigh, Rician, Nakagami-m, and Nakagami-q, and these are collectively referred to as generalized fading. The modulation can be any linear scheme. The asymptotic (high SNR) error rate expression is derivedwhich can incorporate independent and non-identically distributed (i.n.d.) channels in different hops. Power allocation schemes for the source and collaborating nodes are also presented for enhancing the power efficiency of modeled multi-hop performance. Simulations confirm the error expressions, and demonstrate, under considerable assumptions in transmission systems modeling, the performance of a DF multi-hop systems, including with optimal power allocation. The formulation allows (i) the configuration of modeled multi-hop systems from a wide variety of radio links including diversity-enabled ones or non-fading ones, and (ii) the evaluation of their end-to-end, high-SNR, optimized error performance.

In this paper, we investigate the performance of relay selection in an underlay cognitive radio system in the presence of primary user (PU) interference. In particular, we consider a secondary multi-relay network operating in the amplify-and-forward (AF) mode and only the “best” relay which satisfies an index of merit is selected. The proposed selection strategy takes into consideration the effect of PU interference, i.e., we assume that a secondary relay network is exposed to unwanted interference from a neighboring PU network. We derive a closed-form outage probability expression for the secondary multi-relay network and further present a thorough asymptotical diversity order analysis. A simulation study is presented to corroborate the analytical results and to have further insight into the performance the proposed selection strategy.

In this paper, a transmission of JPEG2000 images using an Unequal Power Allocation (UPA) scheme and Orthogonal Frequency Division Multiplexing (OFDM) technique over a block fading - frequency selective channel is presented. Using instantaneous and average channel state information, power is assigned to each bit in the JPEG2000 bitstream based on its contribution to the decoded image quality. Moreover, the actual total power consumed for transmission is measured and compared with the total power initially assigned. Simulation results show an improvement of up to 10.5 dB in the decoded image quality when the UPA scheme is used. In addition, the simulation results demonstrate the effectiveness of the proposed UPA algorithm in frequency selective block fading channels.

The authors study the performance of wireless bidirectional relay-assisted networks in the presence of imperfect channel state information, where two end-source terminals S1 and S2 communicate with the assistance of M relay terminals Rj's. The max–min relay selection criterion is used to select the best relay that maximises the minimum signal-to-noise ratio of the links S1 → Rj → S2 and S2 → Rj → S1 over all relay terminals. The authors investigate the impact of imperfect channel estimation on the outage probability Pout of the system by means of the correlation coefficient pSi of the estimated channel gains and their actual values. Furthermore, the authors show that in a bidirectional relay-assisted network neither of the links S1 → Rj → S2 and S2 → Rj → S1 dominates the performance of the system. Instead, the performance is determined by the average performance of the two links, based on that the authors then discuss the power allocation in such networks. The authors demonstrate that in order to minimise Pout of the entire system, increasing the transmission power of the link with better estimation cannot compensate for the effect of the worse link and therefore the optimum power allocation with the least complexity is to transmit at each source terminals S1 and S2 with equal powers. Numerical results are also presented to corroborate the analytical expressions.

In this paper, we investigate the performance of selection cooperation in the presence of imperfect channel estimation. In particular, we consider a cooperative scenario with multiple relays and amplify-and-forward protocol over frequency flat fading channels. In the selection scheme, only the "best" relay which maximizes the effective signal-to-noise ratio (SNR) at the receiver end is selected. We present lower and upper bounds on the effective SNR and further we provide closed-form expressions for the bounds on average symbol error rate (ASER), outage probability and average capacity per bandwidth of the received signal in the presence of channel estimation errors. A simulation study is presented to corroborate the analytical results and to demonstrate the performance of relay selection with imperfect channel estimation.

The authors investigate the effect of feedback delay on the average capacity of a decode-and-forward cooperative network with relay selection. In particular, a multi-relay cooperative scenario is considered, where the best relay is selected from a subset of relays that are able to decode the source information correctly. In this selection scenario, the authors assume that the destination terminal estimates the relay-to-destination (R→D) channel-state-information perfectly and sends the index of the best relay to the relay terminals via a delayed feedback link. The authors investigate the performance of the considered scenario in terms of average capacity. Simulation results are presented to corroborate the analytical results.

In this letter, we investigate the effect of feedback delay and channel estimation errors in a decode-and-forward (DF) cooperative network with relay selection. In particular, we consider a multirelay cooperative scenario, where the best relay is selected from a subset of relays that are able to decode the source information correctly. In the selection scenario, the destination terminal estimates the relay-to-destination (R → D) channel state information (CSI) and sends the index of the best relay to the relay terminals via a delayed feedback link. We investigate the performance of the considered scenario in terms of average symbol error rate (ASER) and asymptotic diversity order. Simulation results are presented to corroborate the analytical results.

In this letter, we investigate cooperative diversity with relay selection over cascaded Rayleigh fading channels. In particular, we analyze the performance of a relay selection scheme for cooperative vehicular networks with the decode-and-forward (DF) protocol. Only the “best” relay, which satisfies an index of merit, is selected. We ignore the direct transmission between the source (S) and its destination (D), and assume that the destination has perfect knowledge of theS→RandR→Dchannel gains. We study the performance of the underlying scheme in terms of outage probability and investigate its achievable diversity order.

In this paper, we propose an efficient receiver design for single carrier frequency-domain equalization (SC-FDE) for relay-assisted transmission scenario over frequency selective channels. Building upon our earlier work, we propose a novel minimum mean square error (MMSE)-based receiver design tailored to broadband cooperative networks. We show that, by incorporating linear processing techniques, our MMSE-based receiver is able to collect full antenna and multipath diversity gains, while maintaining low complexity implementation. Specifically, under the assumption of perfect power control and high signal-to-noise ratio (SNR) for the underlying links and assuming either of source-to-relay (S → R) or relay-to-destination (R → D) links to be frequency selective Rician fading, our performance analysis demonstrates that the proposed receiver is able to achieve a maximum diversity order of min (LSR,LRD) + LSD+ 2, where LSR, LRD, and LSDare the channel memory lengths for S → R, R → D, and source-to-destination (S → D) links, respectively. Simulation results demonstrate that our proposed receiver outperforms the conventional cooperative MMSE-SC-FDE receiver by performing close to the matched filter bound (MFB).

In this paper, we analyze the effect of feedback delay and channel estimation errors on the performance of a decode-and-forward (DF) cooperative transmission scenario with relay selection. In our relay selection scheme, only one relay with the best relay-to-destination (R → D) channel quality is selected among the set of relays that decode the source information correctly. Specifically, the destination terminal first estimates the channel state information (CSI) of all active R → D links and then sends the index of the best relay to the relay terminals via a delayed feedback link. Due to the time varying nature of the fading channels, selection is performed based on the old version of the channel estimate. Closed-form expressions for the outage probability, average capacity and average symbol error rate (ASER) are derived. Through asymptotic diversity order analysis, we show that the presence of feedback delay reduces the asymptotic diversity order to one, while the effect of channel estimation errors reduces it to zero. Finally, simulation results are presented to corroborate the analytical results.

In this paper, we propose an efficient receiver design for single carrier frequency-domain equalization (SCFDE) for relay-assisted transmission scenario over frequency selective channels. Building upon our earlier work, we propose a novel minimum mean square error (MMSE)-based receiver design tailored to broadband cooperative networks. We show that, by incorporating linear processing techniques, our MMSE-based receiver is able to collect full antenna and multipath diversity gains, while maintaining low complexity implementation. Specifically, under the assumption of perfect power control and high signal-to-noise ratio (SNR) for the underlying links and assuming either of S → R or R → D links to be frequency selective Rician fading, our performance analysis demonstrates that the proposed receiver is able to achieve a maximum diversity order of min(LSR, LRD) + LSD+2, where LSR, LRD, and LSDare the channel memory lengths for S → R, R → D, and S → D links, respectively. Complexity analysis and simulation results demonstrate that our proposed receiver outperforms the conventional cooperative MMSE-SC-FDE receiver by performing close to matched filter bound (MFB), while providing minimal computational complexity.

In this paper, we investigate the performance of selection cooperation in the presence of imperfect channel estimation. In particular, we consider a cooperative scenario with multiple relays and amplify-and-forward protocol over frequency flat fading channels. In the selection scheme, only the "best" relay which maximizes the effective signal-to-noise ratio (SNR) at the receiver end is selected. We present lower and upper bounds on the effective SNR and derive closed-form expressions for the average symbol error rate (ASER), in the presence of channel estimation errors. A simulation study is presented to corroborate the analytical results and to demonstrate the performance of relay selection with imperfect channel estimation.

In this paper, we investigate the effect of imperfect channel estimation on the performance of inter-vehicular communication systems. In particular, we derive the bit error rate (BER) expression in the presence of channel estimation errors for a space-time block coded (STBC) inter-vehicular communication system based on cascaded Rayleigh fading. Our diversity analysis reveals that the underlying scheme is able to extract the full spatial diversity. Based on the derived BER expression, we formulate a power-allocation problem to optimally allocate power to both training and data transmission phases.

In this paper we propose the idea of Phase-Superposition-Phase-Forward (PSPF) relaying for 2-way 3-phase cooperative network involving constant envelop modulation with discriminator detection in a time-selective Rayleigh fading environment. A semi-analytical expression for the bit-error-rate (BER) of the system is derived and comparison with one-way relay is made. Preliminary results indicate that 2-way relaying with PSPF suffers only a moderate loss in energy efficiency (of 1.25 dB) when compared to its one-way relaying counterpart. We attribute the loss to non-optimal power allocation amongst the three nodes in the system. On the other hand, PSPF improves the transmission efficiency by 33% and it offers a way to avoid expensive linear power amplifiers and complicated signal processing at the relay. It is thus a viable alternative to some of the 2-way relaying techniques in the literature.

Unwanted feedback between the donor (receive) and coverage (service) antennas of a relay (repeater) are created by the radio echoes from the local scatters and direct path antenna isolation limitations. These radio echoes create interference not only in the incoming signal from the base station, but also cause instability in the repeater. In this paper, we present an interference cancellation system (ICS) for the multihop WCDMA 3G networks. Various characteristic features of the interference cancellation system are illustrated using the MATLAB simulation results. An isolation gain of 40dB is achieved.

In this paper, we investigate the performance of selection cooperation in the presence of channel estimation errors. In particular, we consider a cooperative scenario with multiple relays and amplify-and-forward protocol over frequency flat fading channels. In the selection scheme, only the "best" relay which maximizes the effective signal-to-noise ratio (SNR) at the receiver end is selected. We present a lower bound on the effective SNR and derive closed-form expressions for the outage probability of the transmitted signal in the presence of channel estimation error. A simulation study is presented to corroborate the analytical results and to demonstrate the performance of relay selection with imperfect channel estimation.

Zero-Padding Orthogonal Frequency Division Multiplexing (ZP-OFDM) has been proposed as an alternative solution to coded-OFDM, which often incurs high decoding complexity. Various ZP-OFDM receivers have been proposed in the literature, exchanging performance with complexity. On the contrary, we propose in this paper a novel reduced-complexity minimum mean square error (MMSE)-based receiver for ZP-OFDM transmissions without sacrificing performance. We demonstrate that the proposed receiver enjoys a remarkably simple decoding scheme, while outperforming conventional ZP-OFDM-MMSE in terms of symbol error rate (SER) performance. We further show that, by incorporating linear processing techniques, our MMSE-based receiver is able to collect full antenna and multipath diversity gains.

In this paper, we analyze the impact of imperfect channel estimation on the performance of pilot symbol assisted modulation (PSAM) used in a relay communication system with distributed space time block code (STBC) and amplify-and-forward protocol. We derive correlation coefficients between the channel coefficients and their estimates when the relay-to-destination link is either non-fading or fading, in terms of Doppler frequency, number of pilot symbols and SNR. This enables us to choose the optimum number of pilot symbols to compensate for the estimation error when the fading and Doppler effects are severe. Our performance analysis demonstrates that the presence of fading in the relay-to-destination link manifests itself by introducing additional Doppler frequency terms. Furthermore, we derive a tight lower bound for the bit error rate (BER) of BPSK modulation with channel estimation errors in terms of cross correlation coeffcients and the number of pilot symbols. Simulation results are also presented to validate our analytical results.

In this paper, we study the transmission of a Gaussian signal in a multi-relay cooperative system, where each relay is half-duplex and employs the amplify-and-forward (AF) relaying protocol. We focus on the analysis of the distortion exponent that characterizes the high signal-to-noise ratio (SNR) behavior of the end-to-end distortion of the received signal. Specifically, we investigate the feedback scheme where the limited channel state feedback is combined with separate source and channel coding to help the transmission. The feedback scheme is followed by three AF-based multi-relay cooperation protocols, respectively, namely the orthogonal AF protocol, the nonorthogonal AF protocol, and the slotted AF protocol. We derive the optimal distortion exponents of all three cases, and illustrate the effect of the feedback resolution, bandwidth ratio, and number of relays on the optimal distortion exponent. It is shown that the feedback scheme outperforms the best known non-feedback strategies for multi-relay cooperative systems with only a few bits of feedback information.

Zero-Padding Orthogonal Frequency Division Multiplexing (ZP-OFDM) has recently been introduced to avoid coded-OFDM's high decoding complexity. Various sub-optimal ZP-OFDM receivers have been developed in the literature to tradeoff performance with implementation complexity. In this paper, we propose a new iterative detection scheme for ZP-OFDM transmissions tailored to broadband cooperative networks with single relay and amplify-and-forward relaying. By avoiding channel dependent matrix inversion, which is inevitable in the case of minimum mean square error (MMSE)-ZP-OFDM transmissions, and incorporating linear processing techniques, we show that our proposed receiver is able to bring significant complexity reduction in the receiver design, while outperforming cooperative MMSE-ZP-OFDM.

In this paper, we propose a new detection scheme for single carrier frequency-domain equalization (SC-FDE) for relay-assisted transmission scenario over frequency selective channels. We show that, by incorporating linear processing techniques, our new receiver significantly outperforms the minimum mean square error (MMSE)-distributed (D)-SC-FDE receiver in terms of the error rate performance. Simulation results and complexity analysis demonstrate that our proposed receiver outperforms the conventional cooperative MMSE-SC-FDE receiver by performing close to matched filter bound (MFB), while incurring a minimal additional computational complexity.

In this paper, we investigate the performance of a single-relay assisted cooperative link, where the source, relay, and destination terminals are equipped with multiple transmit and receive antennas. We focus on the so-called transparent amplify-and-forward (T-AaF) for MIMO relay channels, in which the relay requires neither channel state information (CSI) nor synchronization of symbols or carrier. Specifically, we propose and analyze new selection schemes, where the relay, based on the received signal energies, selects the "best" received signal on the S → R hop and forwards it to the destination terminal with either repetitive or selective transmission. Through the derivation of pairwise error probability (PEP) expressions and asymptotic order of diversity (AOD) analysis, we demonstrate that the proposed selection schemes preserve full diversity order, while maintaining low complexity implementation, which makes them attractive from a practical point of view. Furthermore, we show that adopting selective transmission on the R → D hop provides better performance, better throughput, and significantly better energy efficiency than repetitive transmission. A Monte Carlo simulation study is also presented to corroborate the analytical results and to provide detailed performance comparisons between the two schemes under consideration.

In this paper, we propose a novel low complexity two-stage minimum mean square error (MMSE)-based receiver for single carrier frequency-domain equalization (SC-FDE) for space-time block coded (STBC) transmissions over frequency selective channels. We demonstrate that the proposed receiver enjoys a remarkably simple decoding scheme. We further show that, by incorporating linear processing techniques, our MMSE-based receiver is able to collect full antenna and multipath diversity gains, while maintaining low complexity, thus, eliminating the need for maximum-likelihood sequence detection (MLSD), which has certainly prohibitive complexity, specially, when the constellation size of the transmitted signals and/or the block length increases. Simulation results demonstrate that our proposed receiver significantly outperforms the conventional SC-MMSE-FDE receiver, while maintaining nearly similar complexity.

In this paper, we consider a single-relay cooperative scenario, where the source, relay, and destination terminals are equipped with multiple transmit and receive antennas. We focus on the so-called transparent amplify-and-forward (T-AaF) for MIMO relay channels, in which the relay requires neither channel state information (CSI) nor synchronization of symbols or carrier. Specifically, we propose and analyze new selection schemes where the relay, based on the received signal energies, select the "best" received signals on the S rarr R hop and retransmits them to the destination terminal with either repetitive or selective transmission. Through the derivation of pairwise error probability (PEP) expressions and asymptotic order of diversity (AOD) analysis, we demonstrate that the proposed selection schemes maintain full diversity order. Furthermore, we show that selective transmission on R rarr D hop has better performance, better throughput, and significantly better energy efficiency than repetitive transmission.

In this paper, we consider the transmission of a Gaussian signal in a multi-relay cooperative system, where each relay is half-duplex and employs the decode-and-forward relaying protocol. We focus on the analysis of the distortion exponent, which characterizes the high signal-to-noise ratio (SNR) behavior of the end-to-end distortion. Specifically, we investigate the layered source coding with progressive or broadcast transmission. Each transmission scheme is further combined with the repetition-based or relay-selection-based multi-relay cooperation protocol. We derive the distortion exponents of all four cases and illustrate the effect of the bandwidth expansion ratio, number of relays and cooperation protocols on the optimal distortion exponent. We also establish the successive refinability of the diversity-multiplexing tradeoff of the repetition-based and relay-selection-based cooperation protocols in multi-relay cooperative systems.

In this paper, we investigate distributed differential space-time block coding (STBC) for cooperative communications over frequency-selective fading channels. We carefully exploit the unitary structure of the orthogonal STBC to design a low complexity differential STBC receiver for multi-carrier broadband cooperative networks. We consider amplify-and-forward relaying and assume a single-relay scenario. Under the assumption of perfect power control for the relay terminal and high signal-to- noise ratio for the underlying links, our performance analysis demonstrates that the considered scheme is able to exploit fully the spatial diversity. We further present Monte Carlo simulation results to confirm our analytical observations.

In this paper, we investigate the performance of a single-relay cooperative scenario where the source, relay, and destination terminals are equipped with multiple transmit/receive antennas. We particularly focus on the so-called blind amplify- and-forward relaying in which the availability of channel state information at the relay terminal is not required. Through the derivation of pairwise error probability, we quantify analytically the impact of multiple antenna deployment assuming various scenarios which involve relay location and power allocation assumptions imposed on the cooperating nodes.

In this letter, we investigate the effect of imperfect channel estimation on the performance of distributed space-time block codes (DSTBCs) with amplify-and-forward relaying. Exploiting the orthogonality of the underlying code, we derive a maximum likelihood metric conditioned on the channel estimate acquired through the insertion of pilot symbols. For a large number of pilot symbols, we demonstrate that the proposed decoding rule coincides with the so-called mismatched receiver. On the other hand, as the number of pilot symbols decreases, the proposed decoder converges to a non-coherent detector. Through Monte-Carlo simulations, we further demonstrate that the performance of the proposed scheme lies within 0.8 dB of the genie receiver performance bound.

In this paper, we investigate the performance of a single-relay cooperative scenario where the source, relay and destination terminals are equipped with multiple transmit/receive antennas. We assume that conventional space-time block codes are employed in the underlying source-to-destination (SrarrD), source-to-relay (S rarr R) and relay-to-destination (R rarr D) links, and consider both decode-and-forward (DaF) and amplify - and-forward (AaF) relaying techniques. For the latter one, we consider two variants based on the availability of channel state information (CSI); namely, blind AaF and CSI-assisted AaF. Through the derivation of pairwise error probability, we quantify analytically the impact of multiple antenna deployment for each relaying technique under various scenarios which involve relay location and power control assumptions imposed on cooperating nodes. Our transmission model assumes that the source and destination terminals are equipped with MStransmit and N receive antennas, respectively, and the relay terminal is equipped with MRreceive and MTtransmit antennas. For a scenario where R rarr D and S rarr D links are balanced and S rarr R link experiences sufficiently large SNR, our performance analysis demonstrates that the maximum achievable diversity order is MTmin(MS, N)+MSN for blind AaF scheme and N(MT+MS) for both CSI-assisted AaF and DaF schemes. For another scenario where R rarr D link has a sufficiently large SNR and S rarr R and S rarr D links are balanced, CSI-assisted AaF, blind AaF and DaF schemes achieve diversity orders of MS(N + MR), MS(N + MT), and MSN, respectively. Other scenarios involving the availability of non-fading R rarr D link and poor inter-user channel quality are further investigated. An extensive Monte Carlo simulation study is also presented to corroborate the analytical results and to provide detailed performance comparisons among the three relaying techniques under consideration.