Artificial intelligence (AI), and more specifically deep learning techniques, have demonstrated strong capabilities in processing wireless signals, enabling automatic modulation classification (AMC). However, existing AI-based AMC methods often produce unreliable predictions and lack robustness to out-of-distribution (OOD) inputs, which limits their deployment in real-world scenarios. This study aims to address the gap in simulations to real deployments by fitting predictions to OOD scenarios. We propose an uncertainty-aware AMC framework based on supervised contrastive learning (SupCon). The proposed framework aims to enhance classification reliability, particularly under OOD conditions. In this framework, a ResNet-based representation learning model that consists of a feature extractor and a projection head is first trained using a combination of SupCon loss and cross-entropy (CE) loss to produce class-discriminative embeddings. By decoupling the shared representation learning model from task-specific classifiers, the framework enables a modular and computationally efficient uncertainty estimation strategy, avoiding redundant computation during inference. The learned embeddings are then processed by a set of class-wise binary classifiers that provide both classification and sample-wise uncertainty estimates. A rejection mechanism is incorporated to improve decision reliability by discarding uncertain predictions. We evaluate the proposed framework on the RadioML 2018 dataset. Experimental results show that our approach significantly improves representation quality and classification reliability compared to conventional supervised learning. Specifically, the classification accuracy increases from 63.7% to 93.6% under in-distribution conditions, and from 30.8% to 92.5% under 50% OOD contamination, while maintaining over 85% recall on accepted predictions.

Federated learning (FL) over wireless networks using analog transmission can efficiently utilize the communication resource but is susceptible to errors caused by noisy wireless links. In this paper, assuming a multi-antenna base station, we jointly design downlink-uplink beamforming to maximize FL training convergence over time-varying wireless channels. We derive the round-trip model updating equation and use it to analyze the FL training convergence to capture the effects of downlink and uplink beamforming and the local model training on the global model update. Aiming to maximize the FL training convergence rate, we propose a low-complexity joint downlink-uplink beamforming (JDUBF) algorithm, which adopts a greedy approach to decompose the multi-round joint optimization and convert it into per-round online joint optimization problems. The per-round problem is further decomposed into three subproblems over a block coordinate descent framework, where we show that each subproblem can be efficiently solved by projected gradient descent with fast closed-form updates. An efficient initialization method that leads to a closed-form initial point is also proposed to accelerate the convergence of JDUBF. Simulation demonstrates that JDUBF substantially outperforms the conventional separate-link beamforming design.

In mmWave wireless networks, signal blockages present a significant challenge due to the susceptibility to environmental moving obstructions. Recently, the availability of visual data has been leveraged to enhance blockage prediction accuracy in mmWave networks. In this work, we propose a Vision Transformer (ViT)-based approach for visual-aided blockage prediction that intelligently switches between mmWave and Sub-6 GHz frequencies to maximize network throughput and maintain reliable connectivity. Given the computational demands of processing visual data, we implement our solution within a hierarchical fog-cloud computing architecture, where fog nodes collaborate with cloud servers to efficiently manage computational tasks. This structure incorporates a generative AI-based compression technique that significantly reduces the volume of visual data transmitted between fog nodes and cloud centers. Our proposed method is tested with the real-world DeepSense 6G dataset, and according to the simulation results, it achieves a blockage prediction accuracy of$\mathbf{9 2. 7 8 \%}$while reducing bandwidth usage by 70.31 %.

Cell-free Integrated Sensing and Communication (ISAC) aims to revolutionize 6th Generation (6G) networks. By combining distributed access points with ISAC capabilities, it boosts spectral efficiency, situational awareness, and communication reliability. Channel estimation is a critical step in cell-free ISAC systems to ensure reliable communication, but its performance is usually limited by challenges such as pilot contamination and noisy channel estimates. This paper presents a novel framework leveraging sensing information as a key input within a Conditional Denoising Diffusion Model (CDDM). In this framework, we integrate CDDM with a Multimodal Transformer (MMT) to enhance channel estimation in ISAC-enabled cell-free systems. The MMT encoder effectively captures inter-modal relationships between sensing and location data, enabling the CDDM to iteratively denoise and refine channel estimates. Simulation results demonstrate that the proposed approach achieves significant performance gains. As compared with Least Squares (LS) and Minimum Mean Squared Error (MMSE) estimators, the proposed model achieves normalized mean squared error (NMSE) improvements of 8 dB and 9 dB, respectively. Moreover, we achieve a 27.8% NMSE improvement compared to the traditional denoising diffusion model (TDDM), which does not incorporate sensing channel information. Additionally, the model exhibits higher robustness against pilot contamination and maintains high accuracy under challenging conditions, such as low signal-to-noise ratios (SNRs). According to the simulation results, the model performs well for users near sensing targets by leveraging the correlation between sensing and communication channels.

Reconfigurable intelligent surfaces (RIS) have emerged as a promising technology for enhancing wireless communication by dynamically controlling signal propagation in the environment. However, their efficient deployment relies on accurate channel state information (CSI), which leads to high channel estimation overhead due to their passive nature and the large number of reflective elements. In this work, we solve this challenge by proposing a novel framework that leverages a pre-trained open-source foundation model (FM) named large wireless model (LWM) to process wireless channels and generate versatile and contextualized channel embeddings. These embeddings are then used for the joint optimization of the BS beamforming and RIS configurations. To be more specific, for joint optimization, we design a deep reinforcement learning (DRL) model to automatically select the BS beamforming vector and RIS phase-shift matrix, aiming to maximize the spectral efficiency (SE). This work shows that a pre-trained FM for radio signal understanding can be fine-tuned and integrated with DRL for effective decision-making in wireless networks. It highlights the potential of modality-specific FMs in real-world network optimization. According to the simulation results, the proposed method outperforms the DRL-based approach and beam sweeping-based approach, achieving 9.89% and 43.66% higher SE, respectively.

Federated learning (FL) with over-the-air computation efficiently utilizes the communication resources, but it can still experience significant latency when each device transmits a large number of model parameters to the server. This paper proposes the Segmented Over-The-Air (SegOTA) method for FL, which reduces latency by partitioning devices into groups and letting each group transmit only one segment of the model parameters in each communication round. Considering a multiantenna server, we model the SegOTA transmission and reception process to establish an upper bound on the expected model learning optimality gap. We minimize this upper bound, by formulating the per-round online optimization of device grouping and joint transmit-receive beamforming, for which we derive efficient closed-form solutions. Simulation results show that our proposed SegOTA substantially outperforms the conventional full-model OTA approach and other common alternatives.

Mobile traffic prediction is an important enabler for optimizing resource allocation and improving energy efficiency in mobile wireless networks. Building on the advanced contextual understanding and generative capabilities of large language models (LLMs), this work introduces a context-aware wireless traffic prediction framework powered by LLMs. To further enhance prediction accuracy, we leverage in-context learning (ICL) and develop a novel two-step demonstration selection strategy, optimizing the performance of LLM-based predictions. The initial step involves selecting ICL demonstrations using the effectiveness rule, followed by a second step that determines whether the chosen demonstrations should be utilized, based on the informativeness rule. We also provide an analytical framework for both informativeness and effectiveness rules. The effectiveness of the proposed framework is demonstrated with a real-world fifth-generation (5G) dataset with different application scenarios. According to the numerical results, the proposed framework shows lower mean squared error and higherR2-Scores compared to the zero-shot prediction method and other demonstration selection methods, such as constant ICL demonstration selection and distance-only-based ICL demonstration selection.

We consider online distributed optimization in a networked system, where multiple devices assisted by a server collaboratively minimize the accumulation of a sequence of global loss functions that can vary over time. To reduce the amount of communication, the devices send quantized and compressed local decisions to the server, resulting in noisy global decisions. Therefore, there exists a tradeoff between the optimization performance and the communication overhead. Existing works separately optimize computation and communication. In contrast, we jointly consider computation and communication over time, by proactively encouraging temporal similarity in the decision sequence to control the communication overhead. We propose an efficient algorithm, termed Online Distributed Optimization with Temporal Similarity (ODOTS), where the local decisions are both computation- and communication-aware. Furthermore, ODOTS uses a novel tunable virtual queue, which removes the commonly assumed Slater’s condition through a modified Lyapunov drift analysis. ODOTS delivers provable performance bounds on both the optimization objective and constraint violation. Furthermore, we consider a variant of ODOTS with multi-step local gradient descent updates, termed ODOTS-MLU, and show that it provides improved performance bounds. As an example application, we apply both ODOTS and ODOTS-MLU to enable communication-efficient federated learning. Our experimental results based on canonical image classification demonstrate that ODOTS and ODOTS-MLU obtain higher classification accuracy and lower communication overhead compared with the current best alternatives for both convex and non-convex loss functions.

We consider online distributed optimization where a server and multiple devices collaborate to minimize a sequence of time-varying global loss functions. To accommodate slow devices that may require multiple time slots to compute their local decisions, the server uses semi-asynchronous aggregation of the local decisions, which complicates device scheduling and performance optimization. In this work, we first analyze the convergence of semi-asynchronous aggregation in the presence of time-varying local update delays and loss-function weights. Our analysis leads to an online scheduling problem to minimize the accumulated age of information on the local decision updates, subject to individual long-term constraints on the total weights of the scheduled devices. We then design an efficient scheduling policy, termed Age-of-Information Minimization with Weight Limits (AIMWeL), through a modified Lyapunov optimization approach that uses the weighted sum of linear age-of-information values and quadratic virtual queues as a new Lyapunov function. We show that AIMWeL has bounded optimality ratio, via a novel double relaxation approach to handle the unique scheduling-dependent communication indicator with time-varying probabilities of completing local decision update caused by semi-asynchronous aggregation. When AIMWeL is applied to semi-asynchronous federated learning, our simulation results based on standard image classification datasets demonstrate that AIMWeL uses significantly less time to reach the same classification accuracy achieved by the current best alternatives for both convex logistic regression and non-convex convolutional neural networks.

Integrated sensing and communications is a key enabler for the 6G wireless communication systems. The multiple sensing modalities will allow the base station to have a more accurate representation of the environment, leading to context-aware communications. Some widely equipped sensors such as cameras and RADAR sensors can provide some environmental perceptions. However, they are not enough to generate precise environmental representations, especially in adverse weather conditions. On the other hand, the LiDAR sensors provide more accurate representations, however, their widespread adoption is hindered by their high cost. This paper proposes a novel approach to enhance the wireless communication systems by synthesizing LiDAR point clouds from images and RADAR data. Specifically, it uses a multimodal transformer architecture and pre-trained encoding models to enable an accurate LiDAR generation. The proposed framework is evaluated on the DeepSense 6G dataset, which is a real-world dataset curated for context-aware wireless applications. Our results demonstrate the efficacy of the proposed approach in accurately generating LiDAR point clouds. We achieve a modified mean squared error of 10.39 with {256, 128, 64, 64} convolutional filters in the LiDAR decoder, as compared to 38.58 achieved for the all-zeroes benchmark. Visual examination of the images indicates that our model can successfully capture the majority of structures present in the LiDAR point cloud for diverse environments. By integrating LiDAR synthesis with existing sensing modalities, our method can enhance the performance of various wireless applications, including beam and blockage prediction.

Large language models (LLMs), especially generative pre-trained transformers (GPTs), have recently demonstrated outstanding ability in information comprehension and problem-solving. This has motivated many studies in applying LLMs to wireless communication networks. In this paper, we propose a pre-trained LLM-empowered framework to perform fully automatic network intrusion detection. Three in-context learning methods are designed and compared to enhance the performance of LLMs. With experiments on a real network intrusion detection dataset, in-context learning proves to be highly beneficial in improving the task processing performance in a way that no further training or fine-tuning of LLMs is required. We show that for GPT-4, testing accuracy and F1-Score can be improved by 90%. Moreover, pre-trained LLMs demonstrate big potential in performing wireless communication-related tasks. Specifically, the proposed framework can reach an accuracy and F1-Score of over 95% on different types of attacks with GPT-4 using only 10 in-context learning examples.

This paper studies the design of wireless federated learning (FL) for simultaneously training multiple machine learning models. We consider round robin device-model assignment and downlink beamforming for concurrent multiple model updates. After formulating the joint downlink-uplink transmission process, we derive the per-model global update expression over communication rounds, capturing the effect of beamforming and noisy reception. To maximize the multi-model training convergence rate, we derive an upper bound on the optimality gap of the global model update and use it to formulate a multi-group multicast beamforming problem. We show that this problem can be converted to minimizing the sum of inverse received signal-to-interference-plus-noise ratios, which can be solved efficiently by projected gradient descent. Simulation shows that our proposed multi-model FL solution outperforms other alternatives, including conventional single-model sequential training and multi-model zero-forcing beamforming.

Placing selected content at the edge of the network close to the users, known as caching, is an important technique to improve the efficiency of content delivery in wireless networks. In this paper, we consider caching in a cloud radio access network (C-RAN) in which the primary fronthaul link operates in the mmWave range and may switch to microwave frequencies in the case of blockage. We aim to minimize the average long-term network cost by optimizing dynamic fetching and caching decisions. Importantly, we consider the realistic case of user request distributions and blockage rates being a priori unknown and not necessarily stationary. We introduce change point detection (CPD) to detect significant changes in the environment; we couple this step with reinforcement learning (RL): our key contribution, the proposed change point detection assisted reinforcement learning (CPRL) algorithm learns the environment and (re-)optimizes the caching policy to solve the associated Markov decision process (MDP) problem. Essentially, CPD allows our learning algorithm to adapt its caching strategy to the new environment which shows faster convergence. The numerical results show that our proposed approach improves the efficiency of caching in wireless networks, making it more adaptable to changing request patterns over time.

We consider online distributed optimization in a networked system, where multiple devices assisted by a server collaboratively minimize the accumulation of a sequence of global loss functions that can vary over time. To reduce the amount of communication, the devices send quantized and compressed local decisions to the server, resulting in noisy global decisions. Therefore, there exists a tradeoff between the optimization performance and the communication overhead. Existing works separately optimize computation and communication. In contrast, we jointly consider computation and communication over time, by encouraging temporal similarity in the decision sequence to control the communication overhead. We propose an efficient algorithm, termed Online Distributed Optimization with Temporal Similarity (ODOTS), where the local decisions are both computation- and communication-aware. Furthermore, ODOTS uses a novel tunable virtual queue, which completely removes the commonly assumed Slater’s condition through a modified Lyapunov drift analysis. ODOTS delivers provable performance bounds on both the optimization objective and constraint violation. As an example application, we apply ODOTS to enable communication-efficient federated learning. Our experimental results based on real-world image classification demonstrate that ODOTS obtains higher classification accuracy and lower communication overhead compared with the current best alternatives for both convex and non-convex loss functions.

We study joint downlink-uplink beamforming design for wireless federated learning (FL) with a multi-antenna base station. Considering analog transmission over noisy channels and uplink over-the-air aggregation, we derive the global model update expression over communication rounds. We then obtain an upper bound on the expected global loss function, capturing the downlink and uplink beamforming and receiver noise effect. We propose a low-complexity joint beamforming algorithm to minimize this upper bound, which employs alternating optimization to breakdown the problem into three subproblems, each solved via closed-form gradient updates. Simulation under practical wireless system setup shows that our proposed joint beamforming design solution substantially outperforms the conventional separate-link design approach and nearly attains the performance of ideal FL with error-free communication links.

Long-term average proportional-fair (LTAPF) throughput optimization through power control is a popular resource allocation problem which is typically approximated by weighted sum-rate (WSR) maximization. WSR optimization is non-convex and strongly NP-hard in general. In this letter, we demonstrate that, in fact, the original sum-log-average-throughput power control problem can be recast as a convex program and thus solved to global optimality efficiently. We also generalize our result to show that the long-term average$\alpha$-fair utility maximization problem can be recast as convex for$\alpha \in (1,\infty)$. Numerical results demonstrate a substantial gain in LTAPF throughput compared to state-of-the art algorithms used to solve the max-WSR problem.

Cellular networks have evolved to deliver high-speed broadband services to support the requirements of IoT applications, which demand high speed, low latency, and massive capacity. A primary market goal is to provide support for ultra-reliable low latency communication (URLLC). URLLC requires sub-milliseconds-level latencies as defined by the third generation partnership project (3GPP). One of the promising technologies to achieve the aforementioned specifications is grant-free (GF) access for uplink resources. The GF scheme enables the user equipment (UE) to transmit data over pre-allocated resources which reduces communication latency. This paper proposes an intelligent Reinforcement Learning (RL) based allocator of grants trained via Deep Q-Learning. The experimental results show effect of the number of UEs in the network, and the percentage of unstable UEs on the speed of the RL agent's convergence.

Cooperative driving is a promising technology in the future Connected Autonomous Vehicles (CAV) because of its benefits to safety and fuel efficiency. However, since CAV will be relying heavily on wireless communication to cooperatively coordinate road maneuvering, latency and reliability of communication still pose a challenge. In this paper, we propose a novel scheme based on deep learning prediction to enhance the uplink resource allocation process in 5G C-V2X. The proposed scheme enables the base station to predict vehicle maneuvers, subsequently, assign it the required resource in advance without the need for scheduling request and granting process. This scheme improved the ability of 5G NR to support cooperative driving requirements. Moreover, we compare both traditional and proposed schemes discussing issues that arise from the introduction of prediction models and possible approaches for further enhancements in the future.

In this work, the performance of spatial modulation (SM) for multiple-input multiple output (MIMO) underlay cognitive radio systems is studied under the effect of channel and hardware impairments. Specifically, a closed-form expression of the average pairwise error probability (APEP), an asymptotic APEP, and a tight upper bound of the average bit error rate (ABER) are derived for Rayleigh fading channels. Useful remarks are highlighted on the system diversity and on the impact of channel and hardware impairments. Numerical results, which are corroborated through simulations, show that the ABER performance saturates to a constant value in the high power region due to the effects of channel and hardware impairments.

In this paper, spatial modulation (SM) is used in a cooperative decode-and-forward (DF) cognitive radio system in order to enhance the overall spectral efficiency. In particular, a multi- antenna secondary transmitter communicates with a single antenna secondary receiver with the help of DF secondary relays in the presence of multiple primary users (PUs). To study the secondary system performance, we derive a closed-form expression for the average pairwise error probability (PEP) over Rayleigh fading channels assuming limited feedback from the PUs. A tight upper bounded average bit error rate is obtained using the PEP expression. Moreover, simple approximate expressions are obtained to get insights on the system diversity and estimation errors effects. Numerical results, which match simulations, show the effectiveness of SM in improving the overall secondary performance in the presence of channel estimation errors.

In this paper, we investigate the effects of shadowing and multi-path fading on a MIMO down-link amplify-and-forward (AF) dual-hop network. In the system, antenna selection is adopted at the first hop based on the shadowing information and we assume both hops undergo Generalized-K composite fading channel. For the considered model, approximate lower bound outage and error probability expressions are derived and asymptotic analysis is performed to investigate the effects of shadowing on the system performance. Moreover, the power allocation optimization problem is studied where optimal power values are obtained. Finally, the analytical findings are validated by the simulations.

This paper studies the recent novel multiple-input multiple-output transmission technique called quadrature spatial modulation (QSM), in underlay cognitive radio (CR) systems. In particular, a multi-antenna secondary transmitter (ST) communicates with a multi-antenna secondary receiver (SR) in the presence of a primary receiver (PR). Considering only the statistical knowledge of the ST-PR channel gain, the QSM-CR scheme is investigated using a mean value (MV)-based power allocation strategy referred to as MV-based scheme. Furthermore, assuming that the ST-PR channel gain is perfectly known, the QSM-CR scheme is investigated using a power allocation method based on instantaneous channel state information (CSI), referred to as CSI-based scheme. In each scheme, considering imperfect ST-SR channel estimation, we study the secondary system performance, where closed-form expressions for the average pairwise error probability (P̅E̅P̅) are derived over Rayleigh fading channels. A tight upper bounded average bit error rate is obtained using the derived P̅E̅P̅ expression. Moreover, simple approximate expressions are obtained to get insights on the system diversity and channel estimation errors' effects. Numerical results, which match with simulations, illustrate the robustness of QSM in enhancing the overall system performance in the presence of estimation errors.

Quadrature spatial modulation (QSM) in cognitive radio (CR) is a new spectral efficient multiple-input multiple-output (MIMO) digital modulation technique. In QSM, the spatial constellation diagram of conventional spatial modulation (SM) system is extended to include both in-phase and quadrature components of the data symbol. As such, QSM combined with CR techniques achieve significant improvement in the overall spectral efficiency while the advantages of SM are retained. In this paper, we study the performance of QSM-MIMO-CR systems in the presence of mutual primary-secondary co-channel interference. A closed-form expression for the average pair-wise error probability (PEP) of the secondary system is derived and used to calculate a tight upper bound of the average bit error rate (ABER) over Rayleigh fading. In addition, a simple asymptotic expression is derived and analyzed. Simulation results, which corroborate the numerical ones, show the importance of QSM in improving the overall secondary performance.

In this paper, spatial modulation (SM) is used in multiple-input multiple-output (MIMO) spectrum sharing systems in order to enhance the overall spectral efficiency. In particular, a multi-antenna secondary transmitter, employing SM as a modulator, communicates with a multi-antenna secondary receiver in the presence of multiple primary users. To study the effect of estimation errors on the secondary system performance, we derive a closed-form expression for the average pairwise error probability (PEP) over Rayleigh fading channels assuming limited feedback from the PUs. A tight upper bounded average bit error rate is obtained using the PEP expression. Moreover, simple approximate expressions are obtained to get insights on the system diversity and estimation errors' effects. Numerical results, which match with simulations, show the efficacy of SM in improving the overall secondary performance in the presence of estimation errors.

This paper studies the use of spatial modulation (SM) in multiple-input multiple-output (MIMO) cognitive radio networks considering the primary receiver interference constraint and the maximum transmit power of the secondary transmitter. In particular, we investigate the effect of estimation errors on the secondary system performance, where a closed-form expression is derived for the average pairwise error probability (PEP) in Rayleigh fading environments. Based on this PEP expression, a tight upper bounded average bit error probability is obtained using the union bound formula. In addition, an asymptotic analysis is conducted and simple approximate expressions are derived to get useful insights on the system diversity and estimation errors' effects. Numerical results, which are validated through simulations, show that the SM is robust against estimation errors.

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

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

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

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

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