This paper proposes a hierarchical deep reinforcement learning (DRL) framework based on the soft actor-critic (SAC) algorithm for hybrid underlay-overlay cognitive Internet of Things (CIoT) networks with simultaneous wireless information and power transfer (SWIPT)-energy harvesting (EH) and cooperative caching. Unlike prior hierarchical DRL approaches that focus primarily on spectrum access or power control, our work jointly optimizes EH, hybrid access coordination, power allocation, and caching in a unified framework. The joint optimization problem is formulated as a weighted-sum multi-objective task, designed to maximize throughput and cache hit ratio while simultaneously minimizing transmission delay. In the proposed model, CIoT agents jointly optimize EH and data transmission using a learnable time switching (TS) factor. They also coordinate spectrum access under hybrid overlay-underlay paradigms and make power control and cache placement decisions while considering energy, interference, and storage constraints. Specifically, in this work, cooperative caching is used to enable overlay access, while power control is used for underlay access. A novel three-level hierarchical SAC (H-SAC) agent decomposes the mixed discrete-continuous action space into modular subproblems, improving scalability and convergence over flat DRL methods. The high-level policy adjusts the TS factor, the mid-level policy manages spectrum access coordination and cache sharing, and the low-level policy decides transmit power and caching actions for both the CIoT agent and PU content. Simulation results show that the proposed hierarchical SAC approach significantly outperforms benchmark and greedy strategies. It achieves better performance in terms of average sum rate, delay, cache hit ratio, and energy efficiency, even under channel fading and uncertain conditions.

The Internet of Things (IoT) is increasingly evolving into a more intelligent and environmentally sustainable ecosystem. However, as IoT systems proliferate, they face persistent challenges, including limited spectrum and energy resources. These challenges can be mitigated by integrating cognitive radio (CR) technology and energy harvesting (EH) techniques. CR enables wireless devices to increase their spectral resources by opportunistically utilizing licensed spectrum bands, while EH allows energy-constrained devices to become self-sustaining and extend their operational lifetime. The increasing diversity of wireless devices and data, combined with rapidly changing wireless environments and unpredictable energy availability, presents significant challenges. As a result, traditional optimization-based approaches often fall short. The advent of artificial intelligence (AI) offers a promising alternative, enabling low-power wireless (LPW) networks to become self-managing, self-sustaining, and adaptive to changing conditions through data-driven methods. However, existing surveys rarely address cognitive LPW networks in depth and often overlook the role of AI in enabling EH and smart operations. This survey fills that gap by reviewing state-of-the-art AI approaches that address key challenges in EH-enabled cognitive LPW systems, including power allocation, resource optimization, and task scheduling. We review key advancements in cognitive LPW systems, such as IoT, backscattering networks, multi-hop and relay-assisted networks, and multiple access networks. Finally, we highlight key insights from the literature and outline promising directions for future research on cognitive LPW systems that enable smart, green, scalable, and secure deployments.

Resource-constrained IoT devices are particularly vulnerable to network intrusions, highlighting the need for effective security mechanisms. Network Intrusion Detection Systems (NIDS) address these threats by monitoring traffic and detecting malicious behavior. However, deploying anomaly-based NIDS on such devices faces three key challenges: privacy concerns in centralized training, the scarcity of labeled data and high computational demands of machine learning models in real-world IoT environments. To address these challenges, this study presents a Lightweight Federated Few-Shot Learning (Light-FFSL) framework, specifically designed for secure and efficient intrusion detection in IoT settings. The framework preserves data privacy by employing federated learning, which enables collaborative model training across devices without exposing sensitive data. To overcome data scarcity, an LSTM-based prototypical few-shot model is used, allowing the system to generalize from only a small number of labeled samples per class. To reduce computational cost, the framework incorporates dynamic scheduling and convergence-based updates. These mechanisms dynamically select participating devices and reduce unnecessary communication for federated training. In addition, the model architecture integrates pruning and post-training quantization into an LSTM-driven prototypical network. Pruning removes redundant parameters before training to reduce complexity, while quantization converts the trained model into an efficient integer-based form, lowering memory requirements and speeding up inference. The effectiveness of the proposed framework is validated through experiments on real-world IoT network datasets, demonstrating its ability to operate efficiently under resource constraints.

In this paper, we address the challenge of dynamic spectrum access in a cognitive Internet of Things (CIoT) network where a secondary user (SU) operates under both energy constraints and adversarial interference from a smart jammer. The SU coexists with primary users (PUs) and must ensure that its transmissions do not exceed a predefined interference threshold on licensed channels. At each time slot, the SU must jointly determine whether to transmit or harvest energy, which channel to access, and the appropriate transmit power while satisfying energy and interference constraints. Meanwhile, a smart jammer actively selects a channel to disrupt, aiming to degrade the SU’s communication performance. This setting presents a significant challenge due to its multi-level decision structure and hybrid action space, which combines both discrete and continuous decisions. To tackle this, we propose a novel Hierarchical Deep Deterministic Policy Gradient (H-DDPG) framework that decomposes the decision-making process into three levels: the high-level policy determines the mode (transmit or harvest), the mid-level policy selects the channel, and the low-level actor outputs a continuous power level. Concurrently, the jammer is modeled as a reinforcement learning agent that learns an adaptive channel jamming strategy using a discrete variant of DDPG. Simulation results show that our H-DDPG approach outperforms conventional flat reinforcement learning baselines.

This paper introduces a novel Quantum Genetic Algorithm (QGA) designed to solve the Quadratic Assignment Problem (QAP), a structure widely applied in communication network optimization, where efficient solutions are critical. This approach utilizes quantum circuits as the objective function, enabling simultaneous evaluation of entire individuals at each generation of the Genetic Algorithm (GA) through quantum superposition and the SWAP test. A preprocessing step involving inverse stereographic projection is applied to ensure proper normalization of data. The method is validated in a case study, analyzing the impact of varying the number of elites and an additional algorithm parameter on solution quality and convergence speed. This work underscores the potential of quantum computing for addressing large-scale optimization challenges in communication networks.

In cognitive Internet of Things (CIoT) networks, efficient spectrum sharing is essential to address increasing wireless demands. This paper presents a novel deep reinforcement learning (DRL)-based approach for joint cooperative caching and spectrum access coordination in CIoT networks, enabling the CIoT agents to collaborate with primary users (PUs) by caching PU content and serving their requests, fostering mutual benefits. The proposed DRL framework jointly optimizes caching policy and spectrum access under challenging conditions. Unlike traditional cognitive radio (CR) methods, where CIoT agents vacate the spectrum for PUs, or relaying techniques, which merely support spectrum sharing, caching brings data closer to the edge, reducing latency by minimizing retrieval distance. Simulations demonstrate that our approach outperforms others in lowering latency, increasing CIoT and PU cache hit rates, and enhancing network throughput. This approach redefines spectrum sharing, offering a fresh perspective on CIoT network design and illustrating the potential of DRLguided caching to highlight the benefits of collaboration over dynamic spectrum access scenarios, elevating CIoT performance under constrained resources.

Spectrum sensing in large-scale cognitive radio networks (CRNs) presents significant challenges, as it typically necessitates numerous static secondary users (SUs) to determine the spectrum state. Current cooperative spectrum sensing (CSS) methods require SUs to transmit their private sensing data to a central unit. This centralized approach not only raises security concerns but also leads to considerable communication overhead. To address these issues, this paper introduces FeRAP, a novel CSS framework based on unsupervised federated representation learning. We leverage the mobility of multiple SUs to collect spectrum sensing data, allowing them to collaboratively yet distributively train a learning model to determine the spectrum state. The FeRAP framework employs a novel deep federated$\beta$variational autoencoder ($\beta$-VAE) for distributed representation learning, which identifies independent latent variables and learns disentangled representations of the sensing data in a lowerdimensional space. Furthermore, Affinity Propagation (AP) is then trained locally on the learned representations at each cooperating SU to securely and autonomously infer the spectrum state. FeRAP is a fully data-driven solution, requiring no modelbased assumptions or prior knowledge of channel or signal characteristics for training. Numerical results demonstrate that FeRAP's CSS performance is on par with supervised deep learning-based CSS techniques. Extensive simulations conducted under various network settings and propagation environments confirm the effectiveness and scalability of FeRAP.

With the rapid growth of IoT networks, traditional centralized Network Intrusion Detection Systems (NIDS) face challenges in maintaining data privacy while processing large volumes of data. Federated Learning (FL) enhances privacy by enabling distributed learning without sharing raw data. However, its effectiveness is limited by the scarcity of labeled attack data. To address this, we propose a Federated Few-Shot Learning (FFSL) framework for IoT network intrusion detection. The framework integrates Few-Shot Learning (FSL) to improve NIDS generalization from a few labeled examples. Meanwhile, FL facilitates collaborative training across distributed IoT devices while preserving privacy. The proposed approach employs a metric-based method combined with Long Short-Term Memory (LSTM) networks to capture temporal dependencies in network traffic, enhancing detection accuracy. The proposed technique is evaluated on the Telemetry of services, Operating systems, and Network traffic dataset for the IoT (ToN_IoT) dataset, the method achieves higher precision, recall, and F1-score compared to state-of-the-art techniques while ensuring computational efficiency and data privacy.

In interweave cognitive radio (CR), users can transmit data only when licensed bands are vacant. Deep learning (DL) allows CRs to intelligently sense and identify channel availability. However, most supervised DL detectors require a substantial amount of labeled training data, which can be challenging to obtain in interweave CR. In this paper, we introduce G-VAP, an unsupervised deep generative approach for cooperative spectrum sensing (CSS). G-VAP utilizes an advanced variant of variational autoencoders (VAEs) called β-VAE to identify independent latent variables and encourage accurate and disentangled sensing data representation in a lower dimensional latent space. Furthermore, G-VAP utilizes the affinity propagation (AP) algorithm for unsupervised clustering to detect primary user activity. Unlike other unsupervised clustering methods, AP’s performance is not reliant on the initialization of cluster centroids. Furthermore, G-VAP leverages the cooperation among CRs to sustain a high detection performance. Our approach is fully data-driven, operates without any model-driven assumptions, and does not require prior knowledge of channel or signal characteristics for training. Numerical results indicate that G-VAP for CSS performs comparably to benchmark supervised DL-based CSS. Extensive simulations have been conducted in diverse network settings, propagation environments, and fading conditions, which have proved the effectiveness of G-VAP.

The increasing demand for data transmissions in next-generation wireless networks necessitates effective spectrum utilization, a challenge addressed by cognitive radio (CR) through enhancing spectral efficiency. In hybrid underlay-interweave CR, secondary users (SUs) adapt their transmissions when primary users (PUs) are active to avoid causing interference and operate at full power during idle spectrum periods. The primary network's tolerance for interference is directly influenced by the currently active PUs. Consequently, the primary network's interference threshold exhibits a dynamic characteristic. By accurately determining the channel activity of the primary network, SUs can effectively optimize spectrum usage. This strategy enables the SUs to have higher transmit power when permitted, resulting in higher performance gains. Therefore, we propose an unsupervised learning framework for sensing in cooperative hybrid CR networks to precisely determine the channel state of the primary network. Our unsupervised approach utilizes principal component analysis (PCA) for feature preprocessing and dimensionality reduction and a Gaussian mixture model (GMM) for channel state identification. Furthermore, our approach requires no prior knowledge and learns on a small amount of unlabeled sensing data. Our findings suggest that our proposed CR network can accurately and efficiently determine primary network channel states based on a variety of performance metrics. Moreover, we demonstrate that the proposed unsupervised framework outper-forms popular supervised learning techniques. Furthermore, it is shown that the proposed learning approach offers reduced complexity and is robust to low signal-to-noise ratios.

This paper explores the application of a federated learning-based multi-agent reinforcement learning (MARL) strategy to enhance physical-layer security (PLS) in a multi-cellular network within the context of beyond 5G networks. At each cell, a base station (BS) operates as a deep reinforcement learning (DRL) agent that interacts with the surrounding environment to maximize the secrecy rate of legitimate users in the presence of an eavesdropper. This eavesdropper attempts to intercept the confidential information shared between the BS and its authorized users. The DRL agents are deemed to be federated since they only share their network parameters with a central server and not the private data of their legitimate users. Two DRL approaches, deep Q-network (DQN) and Reinforce deep policy gradient (RDPG), are explored and compared. The results demonstrate that RDPG converges more rapidly than DQN. In addition, we demonstrate that the proposed method outperforms the distributed DRL approach. Furthermore, the outcomes illustrate the trade-off between security and complexity.

In the evolving landscape of the Internet of Things (IoT), integrating cognitive radio (CR) has become a practical solution to address the challenge of spectrum scarcity, leading to the development of Cognitive IoT (CIoT). However, the vulnerability of radio communications makes radio jamming attacks a key concern in CIoT networks. In this article, we introduce a novel deep reinforcement learning (DRL) approach designed to optimize throughput and extend network lifetime of an energy-constrained CIoT system under jamming attacks. This DRL framework equips a CIoT device with the autonomy to manage energy harvesting (EH) and data transmission, while also regulating its transmit power to respect spectrum-sharing constraints. We formulate the optimization problem under various constraints, and we model the CIoT device’s interactions within the channel as a model-free Markov decision process (MDP). The MDP serves as a foundation to develop a double deep Q-network (DDQN), designed to help the CIoT agent learn the optimal communication policy to navigate challenges, such as dynamic channel occupancy, jamming attacks, and channel fading while achieving its goal. Additionally, we introduce a variant of the upper confidence bound (UCB) algorithm, named UCB interference-aware (UCB-IA), which enhances the CIoT network’s ability to efficiently navigate jamming attacks within the channel. The proposed DRL algorithm does not rely on prior knowledge and uses locally observable information, such as channel occupancy, jamming activity, channel gain, and energy arrival to make decisions. Extensive simulations prove that our proposed DRL algorithm that utilizes the UCB-IA strategy surpasses existing benchmarks, allowing for a more adaptive, energy-efficient, and secure spectrum sharing in CIoT networks.

The Internet of Things (IoT) has attracted a lot of interest owing to its various applications. Cognitive IoT (CIoT) networks utilize the cognitive radio (CR) technology to relieve spectrum congestion and boost network performance. In this context, this article proposes a novel deep reinforcement learning (DRL) approach for joint power control and channel access coordination, tailored to energy-constrained CIoT networks. Unlike the existing works, our approach considers coordination dynamics between the competing devices and adopts a realistic energy harvesting (EH) model. The goal of the CIoT transmitter is to meet the interference constraint imposed by the primary network and coordinate channel access with the other CIoT devices while optimizing its lifetime and performance. We model the joint power control and access coordination problem as a model-free Markov decision process (MDP) and introduce a novel deep Q-network (DQN) architecture. This architecture enables a CIoT transmitter to autonomously make decisions regarding EH and data transmission, while also regulating transmit power to maximize the network’s performance and lifetime. These decisions incorporate critical factors, such as channel occupancy by other devices, EH opportunities, and interference constraints without prior knowledge. Through extensive simulations we demonstrate that the proposed DQN strategy achieves faster convergence than the benchmarks, facilitating adaptive, energy-efficient, and realistic spectrum sharing in CIoT networks. Additionally, our algorithm consistently achieves higher performance in terms of average sum rate, interference ratio, and rewards compared to the benchmarks.

In this paper, we present DEAP learning, an unsupervised approach for cooperative spectrum sensing. DEAP learning uses a sparse autoencoder (SAE) to learn a useful representation of the sensing data, followed by unsupervised clustering using affinity propagation (AP) algorithm for identifying primary user activity. Our suggested approach does not require prior information about the channel characteristics or the sensing data for training. Moreover, in contrast to other unsupervised clustering methods, the performance of AP is not dependent on cluster centroids initialization. DEAP learning leverages a few cooperating secondary users to minimize cooperation costs while achieving a high detection performance. Our numerical results suggest that our proposed sensing approach achieves comparable performance to supervised and state-of-the-art unsupervised deep learning-based sensing, without requiring a substantial amount of training data. To demonstrate the merits of DEAP learning, extensive simulations have been conducted under a wide range of primary and secondary network settings. Moreover, we evaluate DEAP learning while considering communication channel impairments. Overall, our DEAP learning approach illustrates the potential for robust performance in cooperative spectrum sensing.

To cope with the unprecedented ubiquity of smart applications, Machine-Type Communication (MTC), the cellular communication backbone of the Internet of Things (IoT), has become an inevitable choice. In this paper, we investigate the achievable performance of MTC in an Ultra-Dense Network (UDN). To fully utilize the available resources in 5G and beyond networks, we exploit the propagation characteristics and excess bandwidth of the Millimeter wave (mmWave) band. Using tools from stochastic geometry, we provide a mathematical framework to evaluate the achievable Signal-to-Interference plus Noise Ratio (SINR) per user and the average capacity per Small Cell (SC) while considering the severe Inter-Cell Interference (ICI) of UDNs and the blockage effect in mmWave. The accuracy of the formu-lated analytical expressions is verified through extensive Monte-Carlo simulations. The obtained results show the existence of an optimal Small Cell (SC) density that maximizes the utilization of the deployed SCs.

This paper investigates the downlink (DL) performance of cell-free (CF) massive multiple-input multiple-output (mMIMO) systems under limited-fronthaul capacity. We consider a practical fronthaul deployment where the fronthaul network is modeled as a hybrid millimeter wave (mmWave) /microwave network. In particular, we assume the presence of multiple edge-cloud processor (ECP)s to which access points (AP)s are associated in a distance-based criterion. The APs are supported by mmWave fronthaul link given that line-of-sight (LoS) links exist with their associated ECPs, otherwise, microwave fronthaul links will be used. We analyze the achievable DL data rates under distributed and centralized system operations. For the distributed system operation, APs apply local conjugate beamforming (CB) precoding along with beamforming training. On the other hand, ECPs apply zero-forcing (ZF) precoding in the centralized system operation. Results reveal that the centralized system operation remarkably outperforms the distributed counterpart under perfect fronthaul links. However, the impact of limited capacity fronthaul links is more prominent on the centralized system operation with ZF precoding. Moreover, we show that the network should be designed according to the adopted system operation and the capacities of fronthaul links.

Cognitive radio (CR) users can transmit data as vacant licensed bands become available. By using machine learning, CR users can intelligently sense channel activity and determine the availability of empty channels. Learning-based CR systems that use supervised learning for spectrum sensing require labeled training data. Furthermore, the majority of existing deep learning-based detectors are supervised, requiring a lot of labeled training data to achieve adequate performance. On the other hand, obtaining a large amount of labeled data in practical CR may be difficult. To address this gap, we propose DeepSense, which is an unsupervised cooperative sensing approach that uses representation learning by a sparse autoencoder (SAE) and unsupervised clustering by a Gaussian mixture model (GMM). DeepSense does not rely on cooperation among many SUs. Instead, it uses the learned representation to improve the detection performance, which significantly decreases the network's cooperation overhead. DeepSense does not require any prior knowledge, such as noise characteristics or channel state information, to operate. Furthermore, only a small amount of unlabeled data is needed for training. Extensive simulations have been conducted, which suggest that the proposed detector is able to learn hidden features in the sensing data that allows it to achieve an excellent detection performance. Moreover, our results show that DeepSense outperforms pure GMM, and attains comparable detection performance to benchmark deep supervised learning-based cooperative sensing.

Physical-layer security (PLS) is examined in this paper for an underlay cognitive radio network (CRN). Two secondary users (SUs) interact through a relay that is equipped with multiple antennas and harvests energy from the SU transmitter's messages via a power splitting (PS) approach. Communication between the relay and SU destination is being intercepted by several eavesdroppers. Therefore, to diminish the eavesdroppers' interception capabilities, the SU destination gathers energy from relayed messages and exploits it to generate and broadcast jamming signals intended to mislead the eavesdroppers. Colluding and non-colluding eavesdroppers are both considered and contrasted as possible strategies for intercepting private information. Additionally, for a more realistic assumption, the connection between the relay and the legitimate SU receiver is assumed to follow the cascaded Rayleigh fading model. PLS is assessed in terms of the probability of non-zero secrecy capacity and the intercept probability.

In this paper, a reinforcement learning technique is employed to maximize the performance of a cognitive radio network (CRN). In the presence of primary users (PUs), it is presumed that two secondary users (SUs) access the licensed band within underlay mode. In addition, the SU transmitter is assumed to be an energy-constrained device that requires harvesting energy in order to transmit signals to their intended destination. Therefore, we propose that there are two main sources of energy; the interference of PUs’ transmissions and ambient radio frequency (RF) sources. The SU will select whether to gather energy from PUs or only from ambient sources based on a predetermined threshold. The process of energy harvesting from the PUs’ messages is accomplished via the time switching approach. In addition, based on a deep Q-network (DQN) approach, the SU transmitter determines whether to collect energy or transmit messages during each time slot as well as selects the suitable transmission power in order to maximize its average data rate. Our approach outperforms a baseline strategy and converges, as shown by our findings.

The rising era of smart living requires unprecedented advancements in cellular networks to support the communications of the Internet of Things (IoT), referred to as machine-type communication (MTC). Hence, we consider an ultradense network (UDN) environment supported by wireless backhaul links (BHs) and investigate the achievable performance gains for MTC. By doing so, we avoid the complexity, cost, and/or infeasibility of providing fiber BHs for the massive number of small cells (SCs) found in UDNs. We utilize the millimeter wave (mmWave) band to support the communications between the IoT Devices (IoTD) and their serving SCs. By doing so, the excess available bandwidth can be used to support a massive number of IoTDs while the propagation characteristics of the mmWave signals can be exploited to mitigate the severe intercell interference (ICI) found in UDNs. In this regard, we formulate a mathematical framework using tools from stochastic geometry to derive analytical expressions for the density of supported IoTDs and the average capacities per SC on both the access link (AL) and the BH. In addition, we obtain a tight lower bound of the average capacity per SC under the considered wireless limited-capacity BHs. The obtained results show the existence of an optimal active SC density that maximizes SC utilization.

This paper investigates the downlink (DL) performance of cell-free (CF) massive multiple-input multiple-output (mMIMO) systems under a wireless mMIMO-based fronthaul network operation. Particularly, we consider multiple edge-cloud processors (ECP)s serving access points (AP)s using one of three possible fronthaul network operations, namely, microwave, millimeter wave (mmWave), or hybrid microwave/mmWave. Under each fronthaul network operation, we analyze the achievable DL data rates for two different microwave-based operations of the access link (APs-users), namely, distributed and centralized operations, while assuming APs with/without decoding capabilities. In the distributed operation, APs are responsible for performing both channel estimation and DL data precoding tasks, whereas ECPs are the responsible entities for carrying out such tasks in the centralized counterpart. Our results show that the integration between the centralized access link operation and the hybrid-based fronthaul network provides the highest DL data rates when APs are empowered with decoding capabilities. However, integrating the distributed access link operation with the microwave-based fronthaul network achieves ultimate performance when APs are not supported with decoding capabilities. Interestingly, we reveal that APs with low fronthaul capacities dominantly control the preferred network configuration in-terms of the densities and the number of deployed antennas for both APs and ECPs.

One of the major technological breakthroughs to support unprecedented demands of the future generations of wireless communication networks is cell-free (CF) massive multiple-input multiple-output (mMIMO). However, a fronthaul network with high and reliable capacity links is a prerequisite to realize the full potential of the CF mMIMO. Aiming at deploying a cost-efficient fronthaul network, this paper proposes a millimeter wave (mmWave)-based fronthaul network for the CF mMIMO system operation thanks to the broad bandwidth in the mmWave frequency band. Stochastic geometry tools have been exploited to reflect the impact of the considered fronthaul network on the uplink (UL) performance of CF mMIMO systems. Results reveal that increasing the blockage density deteriorates the average UL data rates, however, increasing the density of CPUs can limit the blockages effect on the system performance. Besides, while it is more preferable to deploy a large number of antennas per access points (AP)s under low blockage densities, having a large number of APs, provided with a small number of antennas leads to superior UL data rates at high blockage densities.

In Cognitive radio (CR), users take advantage of vacant licensed bands to transmit their data as they become available. Cognitive users employ an autonomous perception-action decision cycle that starts with sensing the activity of licensed users. By using machine learning techniques, CR users can attain their full cognitive potential and smartly detect empty frequency bands. Learning-based CR systems that utilize supervised learning for spectrum sensing require labeled data for model training. Having readily accessible labeled data is a challenging task for CR networks, since it necessitates cooperation between licensed and unlicensed users. In interweave CR networks, such cooperation is not feasible and imposes a significant communication overhead. Motivated by the above, we address the practical limitation of labeled data scarcity in learning-based CR networks by designing a novel unsupervised learning framework for cooperative spectrum sensing based on a Gaussian mixture model (GMM) and principal component analysis (PCA) that uses a small amount of unlabeled data for training and requires no prior knowledge of the radio environment. The system is mathematically simulated, and its performance is evaluated based on various detection performance metrics. According to our findings, our proposed approach outperforms the GMM algorithm and is on par with supervised learning algorithms such as SVM, RF, and DT. Furthermore, the proposed approach is shown to be robust to low SNRs.

In this paper, physical-layer security (PLS) of an underlay cognitive radio network (CRN) operating over cascaded Rayleigh fading channels is examined. In this scenario, a secondary user (SU) transmitter communicates with a SU receiver through a cascaded Rayleigh fading channel while being exposed to eavesdroppers. By harvesting energy from the SU transmitter, a cooperating jammer attempts to ensure the privacy of the transmitted communications. That is, this harvested energy is utilized to generate and spread jamming signals to baffle the information interception at eavesdroppers. Additionally, two scenarios are examined depending on the manner in which eavesdroppers intercept messages; colluding and non-colluding eavesdroppers. These scenarios are compared to determine which poses the greatest risk to the network. Furthermore, the channel cascade effect on security is investigated. Distances between users and the density of non-colluding eavesdroppers are also investigated. Moreover, cooperative jamming-based energy harvesting effectiveness is demonstrated.

Cognitive radio (CR) technology can leverage intelligence enabled by the integration of machine learning (ML) to successfully deliver pervasive connectivity for next-generation wireless networks. In this, a comprehensive overview of the uses of intelligent cognitive radio in a wide range of existing and emerging wireless technologies, including energy harvesting, physical-layer security, Internet of Things (IoT), mobile communications (vehicular and railway), and unmanned aerial vehicle (UAV) communications, will be presented. The interplay between intelligent CR and current and future technologies will be discussed. Emphasis will be on how the aforementioned techniques can benefit from intelligent CR and vice versa. For each technology, the key motivation for using intelligent networks will be highlighted CR with existing state-of-the-art ML approaches. The problems and prospective research avenues, and a futuristic road map exploring different possibilities for overcoming challenges through trending concepts will also be discussed.

Efficient uplink access is a keystone for the successful deployment of machine-type communication (MTC) that enables the promising Internet of Things (IoT). In this article, we introduce a communication framework that provides uplink access for MTC while avoiding both collisions and large signaling. Particularly, we propose a two-stage uplink access technique that combines both fast-uplink grant and nonorthogonal multiple access (NOMA). Using the fast grant, the base station (BS) schedules the devices without sending scheduling requests. Afterward, NOMA facilitates the grant sharing where pairing is done in a distributed manner to reduce signaling overhead. In addition, NOMA plays a major role in decoupling the challenges associated with fast grant, namely, active set prediction and optimal scheduling. In massive networks, full information gathering at the BS is impractical. Hence, multiarmed bandit (MAB) learning is adopted to schedule the fast grant devices. We devise an abstraction model for the source traffic predictor needed for a fast grant such that the prediction error can be evaluated. Consequently, the performance of the proposed scheme is analyzed in terms of average resource wastage and outage probability. The simulation results show the effectiveness of the proposed method in saving the scarce resources while verifying the analysis accuracy. In addition, the results show that the proposed scheme can easily attain the impractical optimal OMA performance, in terms of the achievable rewards, at an affordable complexity. Furthermore, the ability of the proposed scheme in picking quality MTC devices (MTDs) with strict latency is also proved.

The recent advances and unprecedented ubiquity of computation-intensive applications such as virtual reality and mobile augmented reality force new approaches to handle the accompanying challenges. Ultra-Dense Network (UDN) as a leading direction in 5G and beyond offers an opportunistic degree of freedom to be exploited where a massive number of low-power and low-cost Small Cells (SCs) are deployed. In particular, integrating Edge Computing Servers (ECSs) within the SCs at the edge of the cellular network paves the way to tackle several challenges including the processing of computation-intensive tasks with low latency. To exploit the full potentials of UDNs and ECSs, we deploy multiple associations of SCs to the same user while partitioning and offloading its computation-intensive task to the integrated ECSs therein. In this regard, we formulate a mathematical framework using tools from stochastic geometry to evaluate the average processing delay per user. Extensive Monte-Carlo simulations are conducted to verify the accuracy of our analytical results under different system parameters. Results show the existence of an optimal order of multiple-association. In addition, we propose a novel offline task division approach which significantly reduces the overall delay.

Ultra-Dense Networks (UDNs), where Small Cells (SCs) will be deployed in very high densities, are considered as a leading promising technology in 5G and beyond. Such SCs being equipped with multiple antennas can further enhance the achievable performance. In particular, significant gains can be obtained in securing the wireless data transfer through Physical Layer Security (PLS) protocols. In this paper, we study the combined effects and trade-offs between densifying the SCs and increasing the number of antennas per SC on the achievable downlink secrecy rate per user. Using tools from stochastic geometry, we derive an analytical expression for the achievable average secrecy rate. The obtained results show that both approaches of densifying the SCs and/or increasing the number of antennas per cell enhance the secrecy level of communication. Interestingly, we show that reducing the density of SCs can be compensated by increasing the number of antennas per cell in terms of the achievable secrecy rate.

This paper investigates the downlink (DL) performance of cell-free (CF) massive multiple-input multiple-output (mMIMO) systems with limited capacity fronthaul links. The achievable DL rates are derived assuming two different CF mMIMO system operations, namely, distributed and centralized system operations. For the distributed system operation, access points (AP)s apply local conjugate beamforming (CB) precoding along with beamforming training (BT). On the other hand, the central processing unit (CPU) applies zero-forcing (ZF) precoding in the centralized system operation. Results revealed that the impact of limited capacity fronthaul links is more prominent on the centralized system operation. In addition, while the distributed system operation is more preferable under low capacities of fronthaul links, the centralized counterpart attains superior performance at high capacities of fronthaul links.

In this paper, we propose a nearest line-of-sight relay (NLR) selection technique for internet of things (IoT) devices in millimeter wave (mmWave) relaying systems. We present a tractable analytical framework to characterize the network connectivity for the proposed technique using tools from stochastic geometry. Moreover, we investigate the impact of the relay-selected region and the distance between the base station and IoT device on the network connectivity of mmWave relaying systems. The analytical results unveil a high degree of accuracy which is confirmed by extensive simulations at different relay densities, blockage densities, and signal-to-noise ratio (SNR) thresholds. Results obtained via both simulations and analyses reveal the trade-off between the network connectivity and the energy consumption of IoT devices. Results also reveal a significant impact of blockage density and controlling the relay-selected region on the network connectivity and energy consumption.

A cognitive radio (CR) network consists of wireless devices that opportunistically borrow vacant licensed bands. Cognitive users adaptively employ a perception-action decision cycle. Learning-based CR networks use past acquired knowledge of the radio environment to make smarter decisions. CR systems that use supervised learning for spectrum sensing require labeled data for training purposes. Having readily available labeled data is a complex task for CR networks, as it requires cooperation between the primary and secondary users. Such cooperation is not possible in interweave CR networks and imposes a cooperation overhead. Motivated by the above, we tackle the problem of labeled data scarcity in practical learning-based CR networks. We propose an unsupervised two-stage learning framework for cooperative spectrum sensing. The system combines the superior performance of the Support Vector Machine (SVM) and low cost training data of the Gaussian Mixture Model (GMM). A system model is proposed, and the system’s performance is evaluated based on the Receiver Operating Characteristics (ROC) and Area Under the ROC Curve (AUC). We obtain an upper and a lower performance bounds in terms of the AUC. The detection performance is compared for the SVM, GMM, and the proposed two-stage system based on the ROC. Additionally, we evaluate the detection performance of the CR network under different primary network sizes. Our results show that the two-stage learning approach attains a higher detection performance than the GMM algorithm, and achieves the same or comparable performance to the SVM algorithm.

The cloud radio access network (CRAN) is considered a very promising architecture for the next generation cellular networks. However, in order to fully utilize the advantages of that architecture, optimization algorithms with superior performance and low complexity are needed. Such algorithms can improve the network performance significantly, in terms of enhancing spectral efficiency and reducing latency. Deep learning based techniques are considered very potential candidates due to their high performance and very low online computational complexity. In this paper, we implement a deep learning based power allocation technique for CRAN. Unlike the previous works, we consider user association in our optimization problem, which is essential to reflect a real cellular scenario. Simulation results prove that our technique can obtain a very close to optimal performance, with negligible computational complexity.

In this paper, we propose a non-orthogonal multiple access (NOMA)-based communication framework that allows machine-type devices (MTDs) to access the network while avoiding congestion. The proposed technique is a two-step mechanism that first employs fast uplink grant to schedule the devices without sending a request to the base station (BS). Secondly, NOMA pairing is employed in a distributed manner to reduce signaling overhead. Due to the limited capability of information gathering at the BS in massive scenarios, learning techniques are best fit for such problems. Thus, multi-armed bandit learning is adopted to schedule the fast grant MTDs. Then, constrained random NOMA pairing is proposed to assist in decoupling the two main challenges of fast uplink grant schemes, namely active set prediction and optimal scheduling. Using NOMA, we were able to significantly reduce the resource wastage due to prediction errors. Additionally, the results show that the proposed scheme can easily attain the impractical optimal OMA performance, in terms of the achievable rewards, at an affordable complexity.

Physical-layer security (PLS) for an underlay cognitive radio network (CRN)-based simultaneous wireless information and power transfer (SWIPT) over cascaded κ-µ fading channels is investigated. The network is composed of a pair of secondary users (SUs), a primary user (PU) receiver, and an eavesdropper attempting to intercept the data shared by the SUs. To improve the SUs’ data transmission security, we assume a full-duplex (FD) SU destination, which employs energy harvesting (EH) to extract the power required for generating jamming signals to be emitted to confound the eavesdropper. Two scenarios are presented and compared; harvesting and non-harvesting eavesdropper. Moreover, a trade-off between the system’s secrecy and reliability is explored. PLS is studied in terms of the probability of non-zero secrecy capacity and the intercept probability, whereas the reliability is studied in terms of the outage probability. Results reveal the great impact of jamming over the improvement of the SUs’ secrecy. Additionally, our work indicates that studying the system’s secrecy over cascaded channels has an influence on the system’s PLS that cannot be neglected.

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.

Coexistence of Human-Type Communications (HTCs) and Machine-Type Communications (MTCs) is inevitable. Ultra-Dense Networks (UDNs) will be efficacious in supporting both types of communications. In a UDN, a massive number of low-power and low-cost Small Cells (SCs) are deployed with density higher than that of the HTC users. In such a scenario, the backhaul capacities constitute an intrinsic bottleneck for the system. Hence, we propose a multiple association scheme where each HTC user associates to and activates multiple SCs to overcome the backhaul capacity constraints mainly encountered in the downlink. In addition, having more active cells allows for more MTC devices to be supported by the network. Using tools from stochastic geometry, we formulate a novel mathematical framework investigating the performance of HTC in both downlink and uplink as well as the uplink MTC. Stretched Exponential Path Loss (SEPL) model is considered to practically reflect the UDN environment. Extensive simulations were conducted to verify the accuracy of the mathematical analysis under different system parameters. Results show the existence of an optimum number of SCs to which an HTC user may connect under backhaul capacity constraints. Besides, the proposed multiple-association scheme improves the performance of MTC in terms of both ASE and density of supported devices.

Operating beyond-5G networks at the millimeter wave (mmWave) band imposes technological challenges while offering great opportunities. The nature of millimeter waves renders the communication quality susceptible due to blockage caused by obstacles. Hence, multi-hop relaying is likely to play a significant role in improving the performance of mmWave networks. In this paper, we investigate the performance of two appropriate routing techniques for mmWave networks, namely minimum hop count (MHC) and nearest LoS relay to the destination with MHC (NLR-MHC). Analytical models are provided to evaluate the performance of the two routing techniques using tools from stochastic geometry. We model the distribution of hop count using phase-type distribution, and then we use this distribution to derive analytical results for the coverage probability and spectral efficiency. Results reveal the significant impact of densities of relays and blockages on the performance of the aforementioned routing techniques in-terms of spectral efficiency, connectivity probability, and average hop count. It is also demonstrated that NLR-MHC achieves a superior coverage probability and spectral efficiency compared to MHC. However, MHC provides better performance in-terms of connectivity probability and average hop count.

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.

Cell-free (CF) massive multiple-input multiple-output (MIMO) system is currently considered as a promising network architecture to satisfy the anticipated rate requirements of beyond-5G networks. However, in practical scenarios with the presence of high-velocity users, the network experiences an inevitable performance degradation due to the Doppler shift effect. This paper analyzes the potential of frame length optimization in limiting the Doppler shift effect on the performance of time-division duplexing CF massive MIMO under different mobility conditions. In doing so, we derive novel expressions for tight lower bound of the average downlink (DL) and uplink (UL) rates. Capitalizing on the derived analytical results, we provide an analytical framework to determine the optimal frame length that limits the Doppler shift effect on DL and UL rates according to some criterion. Our results show perfect match of both analytical and simulated results under different system settings. Also, we reveal that the optimal frame lengths for maximizing the DL and UL rates are different and depend mainly on the transmission criterion and the users' velocities. Besides, our results demonstrate the high potential of adapting the frame length according to the velocity conditions in limiting the Doppler shift effect compared to applying a fixed frame length.

Non-Orthogonal Multiple-Access (NOMA) where multiple users share the same resources simultaneously, is one promising candidate for beyond 5G. Besides, an Ultra-Dense Network (UDN) with massive numbers of deployed Small Cells (SCs) can significantly boost the performance of the network. In this paper, we propose a hybrid NOMA/OMA scenario deployed within a UDN environment to support a massive number of devices under the umbrella of the massive MachineType Communication (mMTC) use case. NOMA is performed through pairing devices from two disjoint groups with different normalized Signal-to-Interference Ratio (SIR). Using tools from stochastic geometry, we derive an analytical expression for the Area Spectral Efficiency (ASE) gain when deploying our proposed hybrid NOMA/OMA scheme and compare it to a scenario of pure Orthogonal Multiple-Access (OMA). Moreover, in order to reflect the characteristics of the UDN environment, we model the large scale fading using the Stretched Exponential Path Loss (SEPL) model. We show through both simulations and analyses that the gain obtained from the hybrid NOMA/OMA scheme can be optimized based on the different system parameters. We also investigate the impact of densifying the network on the significance of NOMA deployment.

This paper investigates the downlink (DL) performance of cell-free (CF) massive multiple-input multiple-output (MIMO) systems in the presence of pilot spoofing attacks. Assuming no statistical information of the eavesdropper is available at the access points (AP)s while adopting non-orthogonal pilot sequences in the uplink (UL) training phase, new analytical expressions for the achievable DL rates and information leakage are derived. Besides, we propose a new DL transmission protocol in which the nearest APs to users send DL pilots so that the users can estimate the corresponding DL channel conditions and detect the intended DL transmitted symbols. The performance of the proposed technique is compared with other techniques in the literature, namely, no DL pilots and DL beamforming training. Results reveal that the proposed technique is more robust under pilot spoofing attacks as it significantly limits the information leakage to the eavesdropper and achieves superior secrecy rates.

In this paper, we propose an analytical framework to characterize the hop count distribution for the minimum hop count (MHC) routing technique in mmWave networks. The hop count distribution is modelled as a phase-type distribution, and its parameters are derived using tools from stochastic geometry. Capitalizing on the hop count distribution, we investigate the impact of limiting the hop count on the connectivity of mmWave networks. We further demonstrate that the network connectivity varies according to specific system parameters, such as the densities of relays and blockages. Results obtained via both simulations and analyses reveal the trade-off between the network connectivity and the delay as a function of the hop count.

In this paper, a cooperative Secondary Network (SN) is proposed that operates under a hybrid underlay-interweave model. The narrowband sensing problem under the interweave model was formulated as a binary hypothesis problem. The Fusion Center (FC) uses learning techniques, namely the Gaussian Mixture Model (GMM), Support Vector Machine (SVM), and Naive Bayes' (NB) to classify the state of the channel. An SVM kernel was chosen to best fit our hybrid network, since a nonlinear relationship exists between the energy vectors collected at the FC. Furthermore, the degree of the polynomial SVM kernel was manipulated to minimize classification errors. The multi-class SVM (MSVM) algorithm was reformulated to fit our multiple hypothesis problem in the underlay model. The performance of the hybrid network was evaluated based on the Receiver Operating Characteristics (ROC) and classification accuracy. In addition, the accuracy of the MSVM is improved through the cooperation of the SUs. Our results show that the proposed learning-based hybrid model is robust to low SNR environments, and yields an improved performance compared with traditional cooperative sensing techniques. Moreover, we show that the Gaussian SVM kernel surpasses other proposed learning algorithms achieving as high as an 80% detection rate with as low as 10% false alarm.

In this paper, we consider jointly optimizing user association, resource allocation and power allocation in a two tier heterogeneous cloud radio access network (H-CRAN). Our objective is to utilize all the network resources in the most efficient way to maximize the network average throughput, while keeping some constraints such as the quality of service (QoS), interference protection to the devices associated with the Macro remote radio head (RRH), and fronthaul capacity. In our system, we propose using coordinated multi-point (CoMP) transmissions to utilize any excess resources to maximize the network performance. In contrast to the literature, in which CoMP is usually used only to support edge users. We divide our joint problem into three sub-problems: user association, radio resource allocation, and power allocation. We propose matching game based low complexity algorithms to tackle the first two subproblems. For the power allocation sub-problem, we propose a novel technique to convexity the non-convex original problem to obtain the optimal solution. Given the conducted simulations, our proposed algorithms proved to enhance the network average weighted sum rate significantly, compared to the state of the art algorithms in the literature.

In this paper, physical-layer security (PLS) for an underlay cognitive radio network (CRN) over cascaded Rayleigh fading channels is studied. The underlying cognitive radio system consists of a secondary source transmitting to a destination over a cascaded Rayleigh fading channel. An eavesdropper is attempting to intercept the confidential information of the secondary users (SUs) pair. The secrecy is studied in terms of three main security metrics, which are the secrecy outage probability (SOP), the probability of non-zero secrecy capacity (Prnzc), and the intercept probability (Pint). The effects of the path loss and the variation of the distances from the SU transmitter over the secrecy are also analyzed. Results reveal the great effect of the cascade level over the system secrecy. In addition, the effect of varying the interference threshold that the PU receiver can tolerate over the secrecy of the SUs pair is studied. The effect of the channel model parameters of both the main and the wiretap channels is investigated using both simulation and analytical results.

In this paper, the performance of a digital communication system subject to $\kappa-\mu$ Extreme fading conditions is investigated. Specifically, exact and novel analytical expressions for the normalized average channel capacity and average error rate are derived assuming arbitrary fading parameters. An asymptotic error analysis is also performed in order to get further insights for the system performance. Finally, our analytical results are corroborated through Monte Carlo simulations. Although the derived expressions of the exact performance metrics include an infinite series, it is worth noting that they all converge rapidly and steadily after a few terms. In light of this, a truncation error analysis on one of the underlying performance metrics under investigation is provided and it is shown that the relative error is less or equal to 0.001% for as many as 10 terms.

In this article, we study the uplink coverage and the uplink network throughput of an ultradense network (UDN) where human-type communication (HTC) users and machine-type communication (MTC) devices coexist. We employ non-orthogonal multiple access (NOMA) radio access to address the massive connectivity requirements of MTC while satisfying the high data rate requirements of HTC. To this end, stochastic geometry is utilized to model the network exploiting the inherent randomness of the proposed scenario. The enormous number of small cells in UDN along with NOMA can provide a common ground to satisfy the diverse requirements of both MTC and HTC. The distinguishing features of both UDNs and MTC are considered in modeling the network where stretched exponential path-loss (SEPL) is used to capture short-link distances. Moreover, the truncated channel inversion power control is employed in both HTC users (HTCUs) and MTC devices (MTCDs) to mitigate the uplink intercell interference. The results show the significant impact of various system parameters on the network performance. Closed-form and easy-computable expressions are derived for the considered performance metrics, and are assessed by Monte Carlo simulations.

In this paper, we maximize the benefits from the ultra low power wideband sensing approach based on FFT-based 1-bit quantization by addressing the practical limitations to this method. Unlike the conventional architecture that assumes a fully synchronized and coordinated Primary User (PU) network, the proposed system relaxes these limitations by providing an analytical framework for an uncoordinated asynchronous FFT-based 1-bit quantization system. For this system, we analytically derive the sub-band power which is the main parameter for the closed-form expressions representing the false alarm and detection probabilities. Further, improving the system performance through cooperative sensing is considered. While respecting the decision fusion cooperation, the optimum threshold for the generalized FFT-based 1-bit quantization system is derived such that the aggregate error rate is minimized. In addition to its significant power and complexity reduction, the presented analysis expands the use of the FFT-based 1-bit quantization wideband sensing approach in practical deployments. The sensing performance and the analytical results are assessed through comparisons with respective results from computer simulations.

This paper investigates the secrecy performance of a multiple-input multiple-output (MIMO) system with transmit antenna selection (TAS) and maximal-ratio combining (MRC) over generalized α-μ fading. In the underlying wireless communication system, a single antenna is selected to transmit confidential messages to a multi-antenna receiver in the presence of a passive multi-antenna eavesdropper. In our analysis, new closed-form expressions are obtained for the secrecy outage probability (SOP) and the probability of strictly positive secrecy capacity (SPSC), and are subsequently corroborated through Monte Carlo simulations to assess their validity. Moreover, asymptotic studies of the secrecy outage probability are derived and explicitly show the effects of various system parameters on the secrecy diversity gain.

With the deployment of machine-to-machine (M2M) communications, it is expected that the number of devices will enormously increase. When these devices attempt to concurrently access the network, a radio access network overload problem arises. In this case, the conventional random access procedure used in Long Term Evolution-Advanced (LTE-A) networks is rendered inefficient due to the frequent collisions that lead to excessive delay and resource wastage. In this paper, we propose an efficient scalable overload control algorithm for M2M with massive access. The proposed algorithm can allocate the uplink resources within bounded contention time in a distributed manner. Hence, it can achieve full resource utilization that leads to reduced: access delay, energy consumption, and blocking probability. Additionally, we provide a method for estimating the number of backlogged devices in the network. The performance of the proposed algorithm is evaluated analytically and using simulations. To prove its effectiveness, the performance of the proposed algorithm is compared to the dynamic-access class-barring scheme, where the results depict the superiority of the proposed scheme. Finally, a binary integer programming problem is formulated, where we show that the achieved access delay using the proposed algorithm approaches the optimal value.

In this paper, we investigate the performance of a relay selection scheme in a buffer-aided multi-relay network with coded transmissions. We consider: (1) two different coding schemes, i.e., Turbo codes and distributed Turbo codes (DTC) and (2) quasi-static Rayleigh fading channels, where all the links are independent but asymmetric. For each of the aforementioned schemes, we analyze the performance of the system in terms of the average throughput and average delay. In addition, simple and explicit approximations of the asymptotic throughput for infinite buffer size and the high signal-to-noise ratio (SNR) regime are obtained. Our analysis of the asymptotic throughput provides the maximum achievable diversity gain of the system, and the accuracy of the derived analytical framework is assessed when compared to the Monte-Carlo simulations.

This paper investigates the secrecy performance of multiple-input multiple-output systems under generalized α - μ fading conditions. To this end, we focus on two distinct scenarios: 1) the transmitter has knowledge of the channel state information (CSI) of the eavesdropper channel and 2) the transmitter is not aware of the CSI of the wiretap link. By considering transmit antenna selection in the underlying system, we develop closedform analytical expressions for the lower bound of the secrecy outage probability (SOP) and the probability of the strictly positive secrecy capacity. Furthermore, two novel approaches are proposed to derive an analytical expression of the average secrecy capacity (ASC). First, the ASC is expressed as a function of the average capacity of the desired link and an interaction term regarded as the ASC loss. Second, the ASC is expressed in terms of the average capacities of the desired and eavesdropper links, plus an interaction term that can be regarded as some sort of ASC gain due to the statistical independence between the desired and eavesdropper links. In addition, asymptotic studies of the SOP and the ASC at high signal-to-noise ratio are carried out, which precisely reveal the secrecy diversity and array gains.

Extended coverage is an essential requirement of the successful deployment of Machine-Type Communication (MTC) in harsh scenarios. This would require the MTC nodes to transmit their measurement reports with a high transmit power. At the same time, it is desirable to minimize the transmit power of the MTC nodes for the sake of a longer battery lifetime. These contradicting targets make the uplink coverage one of the main limiting factors to the fruition of MTC applications. In this paper, we analyze the uplink coverage of MTC considering the distinguishing features of a dense network including the high density of cells and the short distances between the cells and the MTC nodes. We model the path loss as a stretched exponential path-loss (SEPL) to capture the short link distances. In addition, a truncated channel inversion power control is considered to satisfy the strict requirements on the power consumption of MTC nodes. The accurate and tractable results unveiled the impact of the system parameters on the network performance. Particularly, the uplink coverage significantly improved at moderate cell density, reasonable bandwidth, and low transmission power. Interestingly, our results reveal that the maximum transmit power has no impact on the uplink coverage in the considered scenario. Fortunately, this allows for a longer lifetime for the battery-powered devices of future IoT applications.

The diverse applications of machine-to-machine (M2M) communication are going to lead to exponential growth in M2M traffic. Associating with M2M deployment, a massive number of devices are expected to access the wireless network concurrently. Hence, a network congestion is likely to occur. The conventional random access mechanism used in Long Term Evolution-Advanced (LTE-A) networks lacks the capability of handling such an enormous number of access attempts due to the frequent collisions. Therefore, designing an efficient medium access schemes is critical for the survival of M2M networks. In this paper, we provide a scalable overload control algorithm that provides access to M2M devices in a distributed manner. Particularly, we discuss some cases of non-idealities that could occur in real situations and propose possible solutions for each case. Additionally, we propose an updated version of the algorithm that includes a traffic regulation mechanism which effectively reduces the energy consumption of the devices. Simulation results are presented to show the effectiveness of the proposed modifications.

The future Machine Type Communication (MTC) forces new requirements on cellular networks such as low-power, low-cost, narrow band, and extended coverage. To support these features, the 3GPP LTE-A introduced a new user equipment (UE) category, namely CAT-M1 raising new challenges for the system designers. For instance, the device should be able to setup a call at a signal-to-noise ratio (SNR) of -15 dB in the extended coverage mode with only one receive antenna and almost no frequency diversity. A new Physical Downlink Control Channel (PDCCH), namely MPDCCH, is introduced to satisfy the new system requirements. The new design relies on signal repetition to enhance the detection performance under very low SNRs. While the performance of this control channel will mainly depend on the channel estimation techniques used for coherent equalization during transmission, the choice of such technique will affect both the complexity and the performance of the UE. In this paper, we propose a channel estimation for MPDCCH that depends on linear processing of the pilot symbols and first-order polynomial interpolations. In order to enhance the performance of this technique we use a de-noising step using decision-feedbacks to help the UE reduce the power consumption of the reception and detection of the MPDCCH. The proposed scheme is able to use less than 30% of the supported repetitions in enhanced coverage modes to achieve the required detection error rate at moderate and low SNRs.

In this paper, we analyze the performance of the generalized selection combining with Gaussian distributed weighting errors for physical-layer security of a wireless communication system. Exact closed-form expressions for the secrecy performance in terms of the secrecy outage probability and the probability of non-zero secrecy capacity are derived. Moreover, asymptotic expressions are obtained and provide insights on how the channel parameters affect the secrecy performance. Monte-Carlo simulations are conducted to assess the accuracy of our analytical framework.

In this paper, we investigate the physical-layer (PHY) security problem of a wireless communication system over mixed generalized fading channels. We consider the transmission of confidential information between a transmitter and a legitimate receiver over an α-μ fading channel in the presence of an eavesdropper who is subject to κ-μ fading. Both the α-μ and κ-μ distributions have been considered in the literature to model the generalized small-scale fading and such a mixed fading propagation environment can be encountered in practical systems where one channel is described for its non-linearity and clustering properties whereas the other has a line-of-sight (LOS) component. In particular, novel analytical expressions for the exact and asymptotic average secrecy capacity (ASC) are derived. Finally, Monte-Carlo simulations are performed to assess the accuracy of the proposed mathematical analysis.

Incremental relaying is a spectral-efficient relaying approach which introduces itself as one of the possible solutions to the spectrum scarcity problem for future generations of cellular communications systems. In this paper, we study threshold amplify-and-forward incremental relaying for correlated fading channels, i.e., fading channels for which the channel gain amplitude varies during the time interval of a block transmission; however, the variation occurs in a correlating fashion. From this aspect, these channels are different from both block-fading and fast-fading channels. Due to the correlating nature of the channels, it is possible to compress channel state information (CSI) sequences and employ the compressed CSI sequences as side information to carefully select the bits transmitted by the relay. This idea is already employed in previous works to increase the spectral efficiency; however, those works consider block-fading channels and also assume maximal ratio combining (MRC) at receiver. In case of MRC, explicit expressions for the end-to-end signal-to-noise ratio (SNR) exist, which enable packet error rate analysis by aid of Moment Generating Functions of the SNR. However, to apply MRC, the receiver requires all channel gains (including source-relay channel gain). In case of correlated channels, communicating all real-valued channel gains, requires a large overhead. This fact renders applicability of MRC for correlated channels. We apply the more practical Equal-Gain Combining method. We derive series approximations for symbol error rate, given M-QAM and MPSK modulations, and assuming Nakagami-m fading channels. We also analyze the spectral efficiency by assuming Markov models for the channels. We compare the spectral efficiency in case of correlated and fast-fading channels and show that, unlike fast-fading channels, correlated fading channels allow for a spectrally efficient scheme.

The distinct features of Ultra-Dense Networks (UDNs), namely, the close proximity of the users to the serving base stations (BSs), the high idle mode probability and the increasing probability of Line-of-Sight (LOS) links to the serving BS, impose a set of requirements on the realistic and accurate modeling of the performance of such networks. In this paper, we consider modeling the path loss by a stretched exponential model which accurately addresses the short distances (5m-350m) between the (serving/interfering) BSs and the users in UDN. Moreover, we study the impact of turning off inactive BSs, as an effective interference mitigation scheme, on the performance of the network in terms of the coverage probability, the network throughput, and the area spectral efficiency. We employ tools from stochastic geometry to model the network as a Homogeneous Poisson Point Process (HPPP). Also, Rayleigh channel fading is assumed for tractability purposes. The results show the significant impact of the users' density on the network performance where the system's interference is upper-bounded mainly by the density of the active users, thanks to turning off the inactive BSs.

In this paper, a cross-layer-based sensor node selection scheme in a cooperative network with equally spatially correlated channels is proposed. The equally correlated model can be used as a worst-case benchmark or as a rough approximation by assuming equal correlation coefficients for all channels. By considering the physical and link layer characteristics, one or multiple sensor nodes are selected to maximize the link layer throughput. Based on the channel characteristics, the best performance is achieved by using one cooperative sensor node in poor links and multiple relays/sensors in high quality links. The performance of the proposed scheme is compared with the capacity-based scheme and considerable improvement in performance is achieved in both correlated and uncorrelated channels. The proposed scheme is examined for both decode-and-forward (DF) and amplify-and-forward (AF) relaying, and also for relay selection and subcarrier allocation in an orthogonal-frequency-division multiplexing (OFDM)-based systems. In all cases, the cross-layer technique is shown to offer significant improvement relative to physical-layer optimization techniques.

In this paper, we study the average downlink rate in Ultra-Dense Networks (UDNs) considering the idle mode capability of the base stations (BSs). Wireless networks are densified by deploying a surplus of small cells in order to provide uniform coverage and high capacity. Consequently, the density of the small cells outpaces that of the active users in UDNs. Idle mode capability permits the idle BSs, due to lack of connected users, to turn off in order to mitigate their interference. We employ tools from stochastic geometry to compute the average downlink rate considering a general fading channel in both signal and interference links. In UDN, the likelihood of Line-of-Sight (LOS) transmission in some scenarios is high due to the closeness of the serving cells to the associated users. This propagation environment requires the consideration of Rician channel rather than Rayleigh channel in this context. The analytical results show a high degree of accuracy which is confirmed by extensive simulations in different combinations of the system parameters including small cell density, active users density, path loss exponent, and fading channels. Moreover, we simulate the impact of the association of a user to many BSs on the average downlink rate.

This paper provides an analytical study on the bit-error rate (BER) of the turbo-coded cooperative relaying in spectrum-sharing systems subject to Rayleigh fading. It is assumed that a single relay that maximizes the received signal- to-noise ratio (SNR) is selected in a secondary multi-relay network. We consider that the transmit power conditions of the underlay system is controlled by the joint power constraint of the interference in the primary network and the maximum allowable transmission power at the secondary network. Moreover, the proposed scheme takes into account the effect of primary user (PU) interference on the neighboring secondary network. First, we derive an expression of a tight upper bound for the BER of the secondary network. Then, we investigate the cooperative diversity gain of the underlying scheme. Finally, the proposed analysis is validated by Monte-Carlo simulations.

The random access (RA) procedure used in Long Term Evolution-Advanced (LTE-A) suffers from frequent collisions, and thus it is inefficient for machine-to-machine (M2M) networks with massive access. In such networks, network overload problem arises as a performance bottleneck that leads to high resource wastage and severe access delay. Therefore, developing efficient overload control algorithms plays an essential role in future M2M communication. In this paper, we introduce a distributed scalable algorithm that is able to efficiently allocate the available network resources to massive number of devices with bounded delay and reduced overhead. Additionally, the algorithm overcomes the resource wastage problem resulted from RA collisions, and thus, it achieves full resource utilization. Our simulation results show that the proposed algorithm outperforms the existing dynamic access class barring (DACB) algorithm in terms of service time, access delay, and blocking probability.

Recent forecasts predict a huge increase in the number of wireless devices that will connect to the Internet through the Internet-of-Things (IoT) framework, which depends mainly on machine-to-machine communication (M2M) and machine type communication (MTC). The current wireless systems are adopting new changes to face the newly emerged challenges from MTC and IoT. The 3GPP standard for LTE-A recently included new categories of user equipment (UE) to support MTC. The new categories can provide low cost, low power, and extended coverage modes. In this paper, we present an overview of the new specifications of different physical channels of the LTE-A CAT-M in release 13 (Rel-13). We investigate the operation of the techniques used in the new specifications, modes of operation, and discuss some of the implementation challenges. We also propose and assess the performance of a low complexity channel estimation technique in low Doppler channels associated with the MTC applications.

In this paper, we study the impact of the coexistence of Machine-to-Machine (M2M) communication and Human-to-Human (H2H) communication on the network performance in Ultra-Dense Networks (UDNs). The performance evaluation of the network considers the downlink coverage, the average cell load of H2H users and the average uplink cell load of M2M devices. Using tools from stochastic geometry, we develop tractable expressions for the considered performance metrics. Furthermore, we investigate two association schemes to address the severe interference in UDNs, namely, connect to closest (de) base station and connect to active (CIA) base station. Considering the distinguishing features of UDNs, we model the path loss by a Stretched Exponential Path Loss (SEPL) model to address the close proximity of users to the base stations (BSs) and the high probability of Line-of-Sight (LOS) transmission as well. The simulation results show an accurate match with the analytical results. The network coverage significantly improves in C2A association scheme with no impact on the average cell load of H2H users. On the other hand, the average uplink cell load of M2M devices increases in C2A association.

In this paper, we study cognitive radio and propose mathematical and behavioral modeling of the LTE downlink signal of primary users in order to facilitate spectrum decisions of the secondary users. Auto-regressive integrated moving average (ARIMA) time series model, non-linear autoregressive exogenous input (NARX) neural network (NN) are investigated and a hybrid system is proposed. Using the proposed model instead of the ARIMA model results in a decrease in the average mean square error (MSE) by 23.23% in addition to a decrease of 9.68% in the number of the classifier's misclassification. The hybrid model yields a 29.25% improvement in the average MSE compared to the random NN, it also offers an advantage of 15.15% decrease in the number of misclassifications. Results show that using predictive models and previous instances of sensed data, the behavior of the primary user could be captured and the secondary user could decide about usable parts of the spectrum accordingly.

In this paper, we propose a general mathematical framework to compute the average downlink rate in a multiple connectivity context considering ultra-dense network (UDN) environment. UDN is a dense small cells network featured by the high density of small cells that may exceed the density of active users. In multiple association, a user connects to M base stations (BSs) that provide the maximum average received power forming a multicell. This provides the user with a “data-shower,” where the user's traffic is split into multiple paths, which helps overcoming the capacity limitations imposed by the backhaul links. The developed framework significantly simplifies the computation of the average downlink rate of the individual connections to the cells of a multicell. Moreover, the accuracy of the mathematical framework is confirmed by extensive simulations. The simulation results show a perfect match with the numerical results computed from the mathematical framework in different combinations of the system parameters including multicell size, small cells density, active users density, pathloss exponent, and fading channel distribution of the signal link.

We propose an ultra low power wideband spectrum sensing architecture by utilizing a one-bit quantization at the cognitive radio (CR) receiver. A window-based autocorrelation is utilized to provide the power spectral density of the quantized signal. Closed-form expressions are derived for the 1-bit quantized correlation. It is shown that the introduced method can still provide full information about the sparse spectrum even at low signal-to-noise ratios (SNR). An optimized detection algorithm is presented to sense whether an individual sub-band is occupied or vacant. The sensing performance is justified through computer simulations. When compared to other methods, in addition to the significant saving in power and complexity, results indicate that the proposed method provides better performance even though an aggressive quantization has been applied.

In this paper, an optimal algorithm is proposed for the joint power allocation and relay assignment problem in OFDM relay networks. The network model consists of single-source single-destination with N half-duplex decode-and-forward (DF) relays. The source node uses orthogonal frequency division multiplexing (OFDM) to transmit information to relays where each serves one subcarrier. Our objective is to find the best subcarrier-relay assignment and power allocation, that maximizes the minimum received signal-to-noise ratio (SNR). This non-convex optimization problem could be solved by exhaustive search with complexity order of O(N!). Suboptimal methods, that solve the relay assignment problem and power allocation separately, have less time complexity in the expense of performance degradation. We propose a simple algorithm to jointly solve the subcarrier-relay assignment and power allocation problem. It is shown that the proposed algorithm finds the optimal solution with complexity order of O(N4).

In this paper, we address the power allocation problem in an underlay cognitive network that employs multiple non-orthogonal amplify-and-forward relays. The power allocation at the relaying nodes is designed to maximize the energy efficiency of the secondary network. We adopt the power-normalized signal-to-noise ratio (PN-SNR) as an efficiency measure. In this context, we formulate an optimization problem to design the power allocation adhering to an interference constraint forced by the primary network. Moreover, each relay has a maximum allowable transmission power to prolong the lifetime of the relaying nodes. Using a series of transformations, the problem is shown to be a product of two quadratic fractional functions, which is non-convex. We propose an algorithm to solve the problem using a transformation into a 1-D search problem. Simulation results show that the proposed power allocation is more efficient than the SNR-maximizing allocation and the equal power allocation and barely affects the outage probability of the system.

The cell association is of a paramount effect on the operation of cellular network. In Ultra-Dense Networks (UDN), different types of small cells are deployed with extremely large densities. This comes with a great advantage of dense reuse of spectrum. In this paper, we investigate the downlink association of a given user equipment (UE) to multiple small cells, which we termed multiple association. In multiple association, a user connects to M ≥ 1 small cells forming what we call MultiCell. Consequently, this overcomes the backhaul limitation of individual cells. Specifically, we derive the idle mode probability in the proposed setting, and based on that we derive an analytical expression for the average ergodic downlink rate of the link between the typical user and its jth nearest cell. Additionally, we exploit the aforementioned findings to study the area spectral efficiency in multiple association, and to investigate its relation to the main system parameters, namely: small cells density, users density, and MultiCell size M. We simulate the proposed model to assess the accuracy of the analytical results. Our results provide a mathematical framework, and pave the way to consider the association of a user to multiple small cells.

In this paper, we study the outage probability of the amplify-and-forward (AF) cognitive relaying in spectrum-sharing environments with outdated channel state information (CSI) over Rayleigh fading channels. A selection of the best relay in the secondary system with outdated channel information is considered. We derive a closed-form expression for the tight lower bound on the end-to-end outage probability using the cumulative density function (CDF) approach. Furthermore, the asymptotic behavior of the outage performance is investigated and reveals the diversity order of the underlying scheme. Finally Monte-Carlo simulations are provided to verify the analytical framework.

We propose a relay subset selection method for two-hop Wireless Sensor Networks (WSNs) where the source-relay links are modeled as time-varying fading channels. Assuming perfect channel estimation at relays, the channel state is quantized to binary levels, called as Good and Bad states, and is modeled by a Gilbert-Elliott channel. The relays compress their quantized channel state information and transmit to a fusion centre. The fusion centre selects the smallest possible subset such that each transmitted source bit is received in a Good channel state by at least one of the relays in that subset. We show through simulations that selecting this subset for relaying the information to the source, reduces the transmission power compared to a conventional all-relay transmit scheme, while maintaining the end-to-end bit error rate below a desired threshold.

In this paper, we propose a new cooperative spectrum sensing scheme for cooperative energy detection, named multi-selective scheme. The proposed cooperative sensing scheme is based on order statistics of the reporting links between the cooperative nodes and fusion center where the links with high signal-to-noise ratios (SNRs) are selected as reliable reporting links. The performance of the proposed scheme is compared to other existing schemes in terms of the probability of detection and probability of false alarm over independent identical distributed (i.i.d.) Rayleigh fading channels. These reliable reporting links send their hard/local decisions to the fusion center where final decision is made using N-out-of-K rule. The performance of our proposed spectrum sensing scheme is evaluated using both analytical and simulation results where it shows a significant performance improvement in-terms of the probability of detection and false alarm compared with some other existing schemes.

This paper presents an enhanced scheme for the dynamic-frequency-hopping (DFH) technique introduced in the IEEE 802.22 standard for wireless regional area networks (WRAN). The performance of the DFH in the IEEE 802.22 standard is analysed in the presence of a Rayleigh fading channel implementing a multiple-input multiple- output (MIMO) system. We show the effect of interference caused by the Primary User (PU) appearance during the Secondary User (SU) data transmission. The system bit error rate is evaluated in order to demonstrate this effect. To overcome the limitations of the DFH on the system performance, we propose a modified spectrum sensing technique where spectrum monitoring is integrated with DFH to leverage higher throughput gains for the secondary network. The enhanced method is analyzed and simulated for a MIMO system.

This paper presents an analytical study on the outage probability (OP) of cognitive amplify-and-forward (AF) with underlay spectrum-sharing systems subject to Rayleigh fading. In the underlying scheme, we consider a partial relay selection with outdated channel state information (CSI) and a spectrum-sharing system where the transmit power conditions are controlled by the combined interference power in the primary network and the maximum allowable transmission power at the secondary network. We first derive the end-to-end OP in closed form and then present asymptotic analysis for the high signal-to-noise ratio (SNR). Finally, Monte-Carlo simulations are provided to verify the accuracy of the theoretical analysis.

The main features for future Machine Type Communication (MTC) cellular networks enable low-power, low-cost, narrow band, and extended coverage system. To support these features, new challenges for the system design have emerged. For instance, the device shall be able to setup a call at a signal to noise ratio (SNR) of -15dB in the extended coverage mode with only one receive antenna and almost no frequency diversity. For these reasons, we present an evaluation to the conventional cell search and initial synchronization algorithms subject to these new hard requirements. The performance of most of the algorithms can be enhanced by utilizing time averaging on the account of increasing the processing time. By simulating exact LTE-MTC system, the performance of various algorithms is obtained with the expected time budget to meet LTE-MTC specifications, if applicable.

We propose a power-efficient transmission scheme for wireless sensor networks. In this scheme, the sensor nodes compress their corresponding source-relay channel state information (CSI) and transmit this compressed CSI sequence along with a selected subset of their received bits from the source, which we refer to as the good bits. We assume slowly varying fading channels between source and relays and analytically derive the compression rate of the CSI sequence. We then study the relay-fusion centre link and find an optimal threshold for our proposed scheme, based on the target bit error rate and the packet delivery ratio of the network. Combining the threshold optimization and the reliable bit transmission schemes, we study the total number of transmitted bits for our proposed system. We show that for slowly varying fading channels, our proposed scheme considerably reduces the number of transmitted bits, and consequently the transmission power of the relay nodes compared with a conventional scheme where all bits are transmitted to the fusion center. We also compare the performance of our proposed scheme with a special decode and forward (SDF) scheme previously introduced in the literature. In order to have a fair comparison, we modify the SDF scheme, such that the modified scheme includes the originally proposed SDF scheme as a special case. We provide detailed comparisons and discussions on the achieved bit error rate, energy efficiency, and feasibility of the proposed and the modified SDF schemes.

Abstract In this paper, we present 64/128/256/512‐point inverse fast Fourier transform (IFFT)/FFT processor for single‐user and multi‐user multiple‐input multiple‐output orthogonal frequency‐division multiplexing based IEEE 802.11ac wireless local area network transceiver. The multi‐mode processor is developed by an eight‐parallel mixed‐radix architecture to efficiently produce full reconfigurability for all multi‐user combinations. The proposed design not only supports the operation of IFFT/FFT for 1–8 different data streams operated by different users in case of downlink transmission, but also, it provides different throughput rates to meet IEEE 802.11ac requirements at the minimum possible clock frequency. Moreover, less power is needed in our design compared with traditional software approach. The design is carefully optimized to operate by the minimum wordlengths that fulfill the performance and complexity specifications. The processor is designed and implemented on Xilinx Vertix‐5 field programmable gate array technology. Although the maximum clock frequency is 377.84 MHz, the processor is clocked by the operating sampling rate to further reduce the power consumption. At the operation clock rate of 160 MHz, our proposed processor can calculate 512‐point FFT with up to eight independent data sequences within 3.2~μsmeeting IEEE 802.11ac standard requirements. Copyright © 2015 John Wiley & Sons, Ltd.

Abstract This paper presents an integrated scheme for the dynamic‐frequency‐hopping (DFH) technique provided in literature for the IEEE 802.22 standard supporting wireless regional area networks (WRANs). The performance of DFH is analyzed thoroughly for various channel models and for a multiple‐input multiple‐output systems. The core of this research is based on the coexistence of digital terrestrial TV broadcasting and the WRANs in the TV white space. The proposed technique aims at protecting the incumbent users from interfering with the cognitive broadband access in the TV spectrum. In order to achieve this, spectrum sensing is performed in the intended working channel in DFH while spectrum monitoring with an energy‐ratio (ER) algorithm is applied during the WRAN data transmission in the working channel. Hence, in theDFH‐ERalgorithm, the reappearance of a digital terrestrial TV signal in a band occupied by the WRANs would be detected immediately. This will provide interference free performance for the licensed signal as well as reliable data transmission for the unlicensed ones. Both analyses and simulation results of the proposed DFH‐ER technique compared with the conventional DFH scenario exemplify the enhancement of the WRAN data transmission while protecting the digital terrestrial TV users. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract Recently, cooperative relaying techniques have been integrated into spectrum‐sharing systems in an effort to yield higher spectral efficiency. Many investigations on such systems have assumed that the channel state information between the secondary transmitter and primary receiver used to calculate the maximum allowable transmit secondary user transmit power to limit the interference is known to be perfect. However, because of feedback delay from the primary receiver or the time‐varying properties of the channel, the channel information may be outdated, which is an important scenario to cognitive radio systems. In this paper, we investigate the impact of outdated channel state information for relay selection on the performance of partial relay selection with amplify and forward in underlay spectrum‐sharing systems. We begin by deriving a closed‐form expression for the outage probability of the secondary network in a Rayleigh fading channel along with peak received interference power constraint and maximum allowable secondary user transmit power. We also provide a closed‐form expression for the average bit‐error rate of the underlying system. Moreover, we present asymptotic expressions for both the outage probability and average bit‐error rate in the high signal‐to‐noise ratio regime that reveal practical insights on the achievable diversity gain. Finally, we confirm our results through comparisons with computer simulations. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract Cognitive radio networks have emerged to improve the utilization of the scarce spectrum. In this paper, we propose a distributed resource allocation algorithm that allocates resources opportunistically to the secondary users in a multiple‐input multiple‐output environment. In order to reduce the complexity and cost, antenna selection schemes are employed to allow the secondary communication using a single radio frequency chain. The proposed algorithm is proved theoretically and using simulations, to give a performance very close to that of a centralized one with lower delay and overhead. Furthermore, we introduce two techniques for the proposed algorithm based on the allowable data rates referred to as limited and maximum rates. We derive closed‐form expression for the consumed power and tight upper bounds for the average throughput achieved by each technique. A comparison between the proposed techniques is also provided. Both simulations and analytical results show that the proposed algorithm achieves high throughput with low complexity. Moreover, the results show that the tightness of the bounds improves with the diversity order. Finally, the proposed techniques are compared with two suggested random schemes to investigate their effectiveness. Copyright © 2016 John Wiley & Sons, Ltd.

-This paper introduces a novel spectrum monitoring algorithm for Orthogonal Frequency Division Multiplexing (OFDM) based cognitive radios so that the primary user activity can be detected during the secondary user transmission. The presented technique fully utilizes the performance of both primary and secondary networks. This is done by sensing the variations in signal power over a number of reserved OFDM sub-carriers so that the reappearance of the primary user is quickly detected and the throughput of both primary and secondary networks are kept high. Both analysis and simulation show that the energy ratio algorithm not only effectively and accurately detects the appearance of the primary user in frequency selective channels, but also offers immunity to the traditional OFDM challenges like the sensitivity of Inter Carrier Interference (ICI) effects.

In this paper, we address the problem of maximizing the received signal-to-noise ratio (SNR) of a relay-assisted secondary network. In particular, a pair of cognitive radio nodes communicate through a cluster of K non-orthogonal amplify-and-forward relays sharing the spectrum of a primary network in an underlay fashion. The interference at the primary receiver due to cognitive nodes transmissions must be below a tolerable level leaving the primary activity unaffected. We formulate an optimization problem to choose the transmission power of the secondary transmitter and the relays while adhering to the interference constraint on the primary network and imposing a maximum limitation upon the power consumption at every secondary node. While the optimization problem is nonconvex, we propose a simple iterative algorithm to achieve the solution. We present the performance of the proposed power allocation for different system parameters. Simulation results reveal a significant improvement of the achievable throughput of the proposed power allocation over equal power allocation.

One of the key challenges of cognitive radio networks is the design of efficient resource allocation schemes. Here we propose a distributed resource allocation algorithm for an underlay multiple-input multiple-output (MIMO) cognitive radio network. To reduce the complexity and cost, we introduce two different antenna selection techniques to allow the secondary communication via a single radio frequency (RF) chain. We show that the proposed distributed algorithm approaches the centralized performance while reducing the delay and overhead. Moreover, the simulation results compare the performance of the two proposed techniques showing their merits and demerits. The results show that the proposed algorithm achieves high throughput with low complexity.

In this paper, the best relay selection in CSI-assisted variable gain amplify-and-forward (AF) with outdated channel state information (CSI) in the presence of co-channel interference (CCI) over Rayleigh fading channels is investigated. We first derive the cumulative density function (CDF) of the effective signal-to-interference-plus-noise ratio (SINR) of both the best source-to-relay and relay-to-destination links. Subsequently, a closed-form expression for the lower bound on the end-to-end outage probability (OP) of the system under study is obtained. Moreover, we present and discuss asymptotic expressions of the OP, which reveal the diversity gain of the underlying system. In particular, it is shown that for varying interference-to-noise ratio (INR), the diversity order is reduced to zero regardless of the nature of the desired CSI (i.e., outdated or ideal), and to full diversity or diversity one for fixed INR with ideal CSI or outdated CSI, respectively. Finally, Monte-Carlo simulation results are presented to validate the accuracy of the proposed analytical framework.

This paper presents a spectrum monitoring algorithm for Orthogonal Frequency Division Multiplexing (OFDM) based cognitive radios by which the primary user reappearance can be detected during the secondary user transmission. The proposed technique reduces the frequency with which spectrum sensing must be performed and greatly decreases the elapsed time between the start of a primary transmission and its detection by the secondary network. This is done by sensing the change in signal strength over a number of reserved OFDM sub-carriers so that the reappearance of the primary user is quickly detected. Moreover, the OFDM impairments such as power leakage, Narrow Band Interference (NBI), and Inter-Carrier Interference (ICI) are investigated and their impact on the proposed technique is studied. Both analysis and simulation show that the energy ratio algorithm can effectively and accurately detect the appearance of the primary user. Furthermore, our method achieves high immunity to frequency-selective fading channels for both single and multiple receive antenna systems, with a complexity that is approximately twice that of a conventional energy detector.

In this paper, we study the performance of time hopping pulse position modulation for impulse radio ultra-wideband. We consider relay network applying decode-and-forward protocol. The channels between nodes adopt the IEEE 802.15.4a norms. The bit error rate performance is analyzed considering the effect of interference. Our results show significant improvement due to the diversity gain provided by the relay nodes. However, the performance is limited when multiple access interference (MAI) is present. To combat the MAI effect and further improve the detection reliability, we propose to use antenna selection at the relay. The relay receiver is assumed to be equipped with multiple antennas, and only the best antenna is selected. This is shown to improve the performance in the presence of MAI and improve the diversity gain

We investigate a cross-layer relay selection scheme based on Q-learning algorithm. For the study, we consider multi-relay adaptive decode and forward (DF) cooperative-diversity networks over multipath time-varying Rayleigh fading channels. The proposed scheme selects relay subsets that maximizes the link layer transmission efficiency without having knowledge of channel state information (CSI). Results show that the proposed scheme outperforms the capacity based cooperative transmission with the same number of reliable relays in terms of transmission efficiency gain. Furthermore, the proposed scheme is shown to offer high bandwidth efficiency from low to high signal-to-noise ratios (SNRs).

We consider a wireless sensor network (WSN) with identically distributed nodes, and a two phase cooperative protocol where the source transmits and is overheard by multiple relays which in turn transmit to the destination or fusion center (FC). We introduce a selection scheme that will pick a subset of the relays that overhear the message and transmit to the FC. This scheme will aim at making the least number of relays active while minimizing the outage probability and sending the least amount of information enough to reconstruct the message at the FC. The reduced amount of information being transmitted through the network along with an even distribution of active relays leads to a more energy efficient system.

In this paper, we derive the exact expression of the finite-signal to noise ratio (SNR) diversity-multiplexing tradeoff (DMT) for Rayleigh fading multiple-input multiple-output (MIMO) channels with dual correlated antennas at the transmitter (2×Nr) and/or at the receiver (Nt×2). We first derive the exact outage probability versus SNR. While finite-SNR DMT and outage probabilities are usually only estimated, we show that the numerical results of our derived outage probability and finite-SNR DMT are identical to those obtained using Monte Carlo simulations. Furthermore, it is shown that achievable diversity gains at realistic SNRs are significantly lower than asymptotic values and that the DMT degrades as the spatial correlation increases. Space-time codes (STCs) for MIMO systems are conventionally designed to achieve the asymptotic DMT frontier. This finite-SNR DMT could provide a new insight to design STCs for practical MIMO systems optimized at realistic SNRs and propagation environments.

We analyze the throughput performance of cognitive ad-hoc networks. The designed network use modified IEEE 802.11 MAC protocol for media access purpose. Also, cognitive communication is limited by the interference imposed on primary users for the designed cognitive network. First, we determine the channel availability probability for this opportunistic network. We then use this probability to propose the embedded Markov model in the cognitive nodes. Using this Markov model we determine the normalized throughput of cognitive nodes. The normalized throughput, variance in throughput and percentage of dropped packets performance reveals that number of cognitive nodes, transmit power, packet length, primary users' interference constraint, and number of antennas used have significant effect on the channel availability and throughput.

In this paper, we study the performance of relay selection in variable-gain amplify-and-forward (AF) turbo-coded cooperative network in the presence of channel estimation errors. In particular, we assume M-ary phase shift keying (MPSK) modulation and consider a dual multi-relay cooperative scenario where the relay selection requires global knowledge (end-to-end) of the relaying link. We derive the probability density function (PDF) of the received signal-to-noise ratio (SNR) for the underlying relaying link, and an upper bound expression of the average pairwise error probability (PEP). Furthermore, we present the diversity analysis study in which we show that the effect of erroneous channel estimates reduce the diversity order at high SNR to zero. Using these results, we evaluate the performance of the coded cooperation scenario under study in terms of bit-error rate (BER). Monte-Carlo simulation results are presented to validate the accuracy of the analytical results.

In this paper, we consider a cognitive radio network (CRN) with multiple primary users (PUs) and multiple SUs and propose a cooperative spectrum sensing and transmission for some selected SUs in order to avoid interference with PUs. The distributed collaboration strategy is done through a coalitional game and we present a distributed algorithm for the coalition formation. In the proposed scheme, the SUs that sense a particular channel with non-identical sensing times, form a coalition in which a coalition head is selected to determine whether or not the channel is idle. If a channel is not active, then the SU chosen as coalition head can transmit its packets through a cooperative scheme in the data transmission slot. Our simulation results show that the proposed scheme can significantly improve the average throughput compared to its non-cooperative counterpart. Moreover, it is shown that the proposed scheme is energy efficient in comparison to a scheme with fixed and identical sensing times.

Cooperation with channel coding also known as coded cooperation has been shown to be an efficient scheme for providing significant performance gains. However, relay selection in coded cooperation systems is seldom available in the existing literature. It is widely known that relay selection schemes in cooperative communications are dependent on the quality of the channel state information (CSI). In this paper, we study the impact of using outdated channel state information (CSI) for opportunistic relay selection (ORS) in turbo-coded cooperation over Rayleigh fading channels. A closed-form expression for the exact outage probability as a function of the correlation between the actual and delayed or outdated CSI is derived. Furthermore, diversity analysis is presented and it is noted that the diversity order is reduced to two regardless of the number of available relays, when the correlation factor is not equal to unity.

In this paper, we consider relay selection for turbo coded cooperative networks subject to Nakagami-m fading when the channel state information (CSI) is known at the receiver but is not necessarily ideal. This non-ideality may be due to a feedback delay caused by the difference between the instantaneous CSI during the transmission and the CSI at the time of relay selection, resulting in outdated CSI phenomena. The impact of the outdated CSI on the proposed scheme is well investigated. A closed-form expression for the exact outage probability is derived as well as its asymptotic expression in the high signal-to-noise (SNR) regime. Moreover, upper bounds on the bit-error rate (BER) are presented and a study of the diversity order reveals that for ideal CSI, full diversity in the number of relays and fading parameters m is achieved as opposed to outdated CSI where the achievable diversity is equivalent to the diversity of a coded cooperative network with a single relay.

We propose algorithms to address the channel allocation and fairness issues of multi band multiuser cognitive ad-hoc networks. Nodes in the network have unequal channel access probability and have no prior information about the offered bandwidth or number of users in the multiple access system. In that nodes use reinforcement learning algorithm to predict future channel selection probability from the past experience and reach an equilibrium state. Proof of convergence of this multi party stochastic game is provided. Furthermore, analytical throughput for such system is determined. Finally, numerical results are presented for performance evaluation of the proposed channel allocation algorithms.

Early detection of primary users presence is one of the most important tasks for cognitive communication. Also, in cognitive settings cognitive nodes may receive signals from primary users and from other cognitive users simultaneously. For such scenario, we propose primary user signal detection using modulation class identification method. We consider multiple transmit and multiple receive antennas for cognitive nodes. We employ Artificial Neural Network (ANN) for the modulation identification purpose. The proposed algorithm works as higher order moments and cumulants are calculated from the received signal samples at each of the receiving branches of cognitive nodes. After this step, these features are fed to the ANN to determine the presence of primary users. Final identification decision is drawn using the decision from all receiving branches. We also present numerical results of our algorithm and compare these results with the theoretical results of the energy detection algorithm.

We study the performance of distributed turbo-coded cooperative networks with multiple dual-hop relays over both identical and non-identical Nakagami-m slow fading channels. All the relays are equipped with error detection capabilities and only the reliable relays retransmit to the destination. In this paper, we derive the union bounds on the bit-error rate (BER) of the proposed scheme using the transfer function bounding and the limit-before-average techniques. Moreover, through the derived pairwise-error probability (PEP), we analyze the asymptotic behavior of this system at high signal-to-noise ratio (SNR). It is noted that the achievable diversity order depends on the number of cooperating relays and the fading parameters. Finally, performance evaluation of the proposed scheme is presented via error bounds and Monte Carlo simulations.

In this paper, we investigate a cross-layer approach for optical multiple input multiple output (O-MIMO) systems, using mode group diversity multiplexing (MGDM). More specifically, by adapting the selection criteria of the sub-optimal antenna used in MIMO radio systems, we propose a new strategy to improve the protocol performance in indoor optical networks. By using the Bell Laboratories Layered Space-Time (BLAST) architecture (Physical layer) and the application of Go-Back-N (at link layer), we show a significant increase of the link throughput, compared with that of the capacity based algorithm. The simulation results show that the combination of transmitters according to the criterion of selection increases the total data rate of the system using multi mode fibers (MMF).

We investigate a power assignment scheme in multi-relay adaptive decode-and-forward (DF) cooperative-diversity networks over non-identical Rayleigh fading channels, that integrates truncated automatic repeat-request (ARQ) at the link layer. At the cooperative level, only the reliable relays participate in the retransmission phase if necessary. In this paper, we aim at maximizing the system throughput via power optimization and we show that this can be done by minimizing the symbol-error rate (SER) of the retransmission. We derive the closed- form expressions for the exact SER of the multi-relay adaptive DF over non-identical Rayleigh fading channels as well as the asymptotic bounds for the SER. Results show that the analytical bounds for the SER corroborate with the simulated SER at high signal-to-noise ratio (SNR) and maximum throughput is achieved through optimal power allocated at the source and various reliable relays.

We investigate the performance of an antenna selection algorithm applied with precoding to improve the throughput performance of cognitive multiple-input multiple-output (MIMO) radios. We assume cognitive MIMO radios coexist in the same frequency band with the primary users. In such event, for concurrent spectrum access, cognitive nodes use beamforming techniques to cancel the mutual information between cognitive and primary users. In that, cognitive nodes exploit the degrees of freedom offered by MIMO systems to beamform the transmitted signal in an attempt to cancel interference at primary users. In the cross-layer design, cognitive nodes maximize an objective function for the link layer throughput where precoding is applied at the physical layer on the transmitted spatial multiplexed signals. We present a closed form solution for the overall throughput in-terms of the physical layer parameters. Numerical results are presented to show the efficacy of the proposed scheme for different network settings.

In this paper, we investigate a cross-layer relay selection approach for cooperative-diversity networks that employ the truncated automatic repeat request (ARQ) protocol. The key feature of the proposed scheme is that the criterion for the selection of relay subsets is the maximization of the link-layer throughput that takes into account both the physical and link layers characteristics. Results show that the proposed cross-layer approach which assigns transmission to relay combination that sees better channel conditions, outperforms the capacity-based cooperative transmission with the same number of reliable relays in terms of throughput gain. Furthermore, a joint optimization of constellation size and packet length is included to enhance the system throughput for both the cross-layer relay selection and capacity-based relay selection.

In this paper, we address the performance of cooperative code-division multiple-access (CDMA) systems using amplify-and-forward (AF) relaying over independent nonidentical (i.ni.d) Nakagami-m fading channels. The outage probability of the system is investigated using the moment generating function (MGF) of the total signal-to-noise ratio (SNR) at the base station. Since it is complicated to derive a closed form expression of the outage probability, we derive an approximation for the probability density function (PDF) of the total SNR which in turn enables us to derive the asymptomatic outage probability for any value of the fading parameter m. Simulation results are presented to assess the accuracy of our analytical results. Furthermore, using the derived outage probability, we investigate the diversity advantage of the cooperative system under different system settings.

Modulation type is one of the most important characteristics used in signal waveform identification and classification. Spatial correlation is a crucial factor for practical multiple-input multiple-output (MIMO) systems. This paper addresses the problem of blind digital modulation identification in spatially-correlated MIMO systems. The proposed algorithm is verified using higher order statistical moments and cumulants of the received signal. The purpose is to discriminate among different M-ary shift keying linear modulation schemes without any priori signal information. This study employs several MIMO techniques to identify the modulation with and without channel state information (CSI). The proposed classifier shows a high identification performance in acceptable signal-to-noise ratio (SNR) range.

Abstract In this paper, we investigate the performance of a cross‐layer (physical and MAC) design for multiple‐input multiple‐output (MIMO) system that aims at maximizing the throughput of ad hoc networks by selecting the optimum antenna combination. Employing this cross‐layer design is shown to improve the overall network performance relative to the case where no antenna selection (AS) is used. To solve the node blocking problem associated with the IEEE 802.11 medium‐access control (MAC) protocol, the proposed protocol leverage the available degrees of freedom offered by the MIMO system to allow neighboring nodes to simultaneously communicate using the zero‐forcing (ZF) Bell‐labs layered space‐time (BLAST) architecture. Using the cross‐layer design, neighboring nodes share their optimum antenna selection (AS) information through control messages. Given this shared information, nodes set their decisions on the number of selected antennas based on the available spatial channels that guarantees collision‐free transmissions. At the destination node, the ZF receiver is employed to extract the desired user data while treating the data from neighboring users as interference. The performance of the proposed cross‐layer design is examined through simulations, where we show that the network throughput is significantly improved compared to conventional MAC protocols. Copyright © 2010 John Wiley & Sons, Ltd.

We investigate the performance of cooperative diversity when employed in convolutionally coded direct-sequence code-division multiple-access (DS-CDMA) systems over frequency-selective fading channels. The performance of the coded system is evaluated through bit error rate upper bounds and compared with simulations. In our analysis, we obtain closed form expressions for the bit error rate upper bounds for multi-relay cooperative systems employing multiuser detection at both the relay and base station receivers. We show that the full benefits of cooperative diversity cannot be achieved if no interference suppression is employed at both the cooperative user and base station receiver. Both simulation and analytical results are presented to validate and demonstrate the performance gain with different system parameters.

We propose algorithms to address the spectrum efficiency and fairness issues of multi band multiuser Multiple-Input and Multiple-Output (MIMO) cognitive ad-hoc networks. To improve the transmission efficiency of the MIMO system, a cross layer antenna selection algorithm is proposed. Using the transmission efficiency results, user data rate of the cognitive ad-hoc network is determined. Objective function for the average data rate of the multi band multiuser cognitive MIMO ad-hoc network is also defined. For the average data rate objective function, primary users interference is considered as performance constraint. Finally, numerical results are presented to show the efficiency of the proposed antenna selection and channel allocation algorithms.

The performance of decode-and-forward (DAF) cooperative code-division multiple-access (CDMA) systems is analyzed in Nakagami fading channels. A closed-form expression for the cumulative distribution function (CDF) is derived based on of the sum of two independent non-identical distributed gamma random variables (RV). This expression is then used to obtain the outage probability. In our analysis we consider both perfect and non-perfect inter-user channels. We also investigate the behavior of the system at high signal-to-noise ratio (SNR) where we obtain asymptotically the achievable diversity order for various system parameters. Simulation results are presented to assess the accuracy of our analytical results.

Cooperative diversity is a powerful tool that can be used to improve the performance of wireless networks. The use of directional antennas has also shown to offer an effective way for efficient bandwidth utilisation. In this study, the authors investigate the effect of both the transmission power and the number of cooperative relays on the maximum achievable throughput in energy-constrained cooperative ad hoc networks with directional antennas. In particular, the authors develop an analytical model for the network throughput in terms of the number of cooperative relays and nodes' transmission power. Using our model, we determine the optimum number of relays for a given transmission power, and the optimum transmission power for a given number of relays. Furthermore, we propose a cooperative relaying protocol that utilises the above optimal values to maximise the achievable throughout in such energy-constrained networks. The obtained analytical results as well as the performance of the proposed protocol are validated by simulations over a Rayleigh fading channel.

In this study, multi-relay decode-and forward (DAF) cooperative networks employing convolutional coding are studied for asynchronous direct-sequence code-division multiple-access (DS-CDMA) systems over frequency-selective slow fading channels. The authors show that the full benefits of coded cooperative diversity cannot be achieved if no multi-user interference suppression is employed at the cooperative end. The authors consider two scenarios; perfect and imperfect inter-user channels. In that, the bit-error-rate performance of the cooperative system is investigated for an uplink transmission where a decorrelator detector is used at both the relay and base station receivers. Both simulation and analytical results are presented to demonstrate the diversity gains of the convolutionally coded cooperative network.

In broadband downlink transmission, orthogonal frequency-division multiplexing (OFDM) combined with code-division multiple access (CDMA) is a prospective technique for high-data rate transmission in future wireless communication systems. By adding spatial diversity, multiple-input and multiple-output orthogonal frequency and code division multiplexing (MIMO-OFCDM) offers superior performance relative to both traditional OFDM systems and single-input and single-output OFCDM (SISO-OFCDM) systems. In this study, the authors present an analytical study and investigation of a MIMO-OFCDM downlink system that hires orthogonal variable spreading factor codes to spread each transmitted symbol in both time and frequency domains. Different gain combining schemes are employed in the frequency domain to recover the data symbols of the desired code channels, and space–time block coding is used to achieve spatial diversity. The more general Ricean fading channel is used to model the MIMO channel. The OFCDM system employs Alamouti transmit diversity scheme with multiple receive antennas. For systems without multi-code interference (MCI), analytical bit-error rate results are obtained and compared with simulation results. The authors also investigate the effect of correlation in frequency domain, where we verify that minimum mean-square error frequency combining is more robust to MCI than equal-gain combining.

We propose algorithms to address the spectrum efficiency and fairness issues of multi band multiuser Multiple-Input and Multiple-Output (MIMO) cognitive ad-hoc networks. To improve the transmission efficiency of the MIMO system, a cross layer antenna selection algorithm is proposed. Using the transmission efficiency results, user data rate of the cognitive ad-hoc network is determined. Objective function for the average data rate of the multi band multiuser cognitive MIMO ad-hoc network is also defined. For the average data rate objective function, primary users interference is considered as performance constraint. Furthermore, using the user data rate results, a learning-based channel allocation algorithm is proposed. Finally, numerical results are presented for performance evaluation of the proposed antenna selection and channel allocation algorithms.

In this paper, we present an iterative joint channel estimation and data detection technique for multiple-input multiple-output (MIMO) code-division multiple-access (CDMA) systems over frequency-selective fading channels. Based on the expectation-maximization (EM) algorithm, the proposed iterative receiver achieves a performance close to the optimum maximum-likelihood (ML) receiver. In addition, the performance of the proposed receiver is optimized through weight coefficients using the minimum mean-square error (MMSE) criterion. Compared to the single-user bound, our results show that the proposed receiver can mitigate the multiple-access interference and attain the full system diversity. Furthermore, our simulation results confirm that the proposed receiver is near-far resistant and offers fast convergence in severe near-far scenarios.

In cdma2000 1xEV, each mobile station determines its data rate based on a reverse activity bit (RAB) broadcasted by the base station and a set of up/down probabilities. In this paper, we propose a new rate control scheme that allows mobile stations to choose between two optimal rates determined by the rise over thermal (RoT) constraints. The base station determines the RAB signal based on the current RoT and estimated parameters of the next frame transmission. The proposed scheme is modelled using a Markov process. Both our simulation and analytical results confirm that the proposed scheme achieves better performance than previously proposed rate assignment schemes.

Modulation type is one of the most important characteristics used in signal waveform identification and classification. In this paper, an algorithm for blind digital modulation identification for multiple-input multiple-output (MIMO) systems is proposed. The suggested algorithm is verified using higher order statistical moments and cumulants of the received signal. A multi-layer neural network trained with resilient backpropagation learning algorithm is proposed as a classifier. The purpose is to discriminate among different M-ary shift keying linear modulation types and the modulation order without any priori signal information. This study covers different MIMO systems with and without channel state information (CSI). The proposed classifier is evaluated through the probability of identification where we show that our proposed algorithm is capable of identifying the modulation scheme with high accuracy in excellent signal-to-noise ratio (SNR) range.

The performance of a cross-layer (physical and MAC) design for multiple-input multiple-output (MIMO) system that maximizes the throughput of ad-hoc networks by selecting the optimum antenna combination is investigated. This cross-layer design is shown to improve the overall network performance relative to the case with no antenna selection. To further improve the overall network throughput, we minimize the effect of node blocking in the IEEE 802.11 medium-access control (MAC) protocol. The proposed protocol leverage the available degrees of freedom offered by the MIMO system to allow neighboring nodes to simultaneously communicate using the zero-forcing Bell-labs layered space-time (BLAST) architecture. The performance of the proposed cross-layer design is examined through simulations to show the throughput advantage relative to conventional MAC protocols.

In this paper, we present an iterative joint channel estimation and data detection technique for multiple-input multiple-output (MIMO) code-division multiple-access (CDMA) systems over Rayleigh fading channels. The proposed receiver performs the channel estimation and data detection using the expectation-maximization (EM) algorithm. We derive a closed-form expression for the optimized weight coefficients of the EM algorithm which is shown to provide a large performance improvement relative to the conventional equal-weight EM-based signal decomposition. Our results show that the receiver can achieve near-optimum performance with modest complexity using very few training symbols. Furthermore, our simulation results confirm that the proposed receiver is near-far resistant and offers fast convergence in severe near-far scenarios.

We propose channel selection algorithm for cognitive ad-hoc radios using noncooperative game theory and present simulation results that show, the proposed algorithm distributes cognitive nodes according to the bandwidth ratio of the channels.

Efficient utilisation of bandwidth and high data rates have a great impact on the performance of ad hoc wireless networks. The use of cooperative diversity, where neighbouring stations may act as relay nodes to transfer the source data to the desired destination node through an independent relay channel, has shown to provide diversity gain and consequently improve the achievable bit rate. However, this is usually attained at the expense of loosing some wireless resources such as bandwidth. On the other hand, the use of directional antennas has shown to offer an effective way for efficient bandwidth utilisation. Here, the authors propose a new channel access protocol for transmission over cooperative ad hoc networks. The proposed protocol, which aims at minimising the number of blocked nodes and consequently improving the system throughput, employs directional antennas at both the source and relay stations. The network performance under the proposed settings is modelled using continuous Markov chains. The steady-state transmission blocking probability and the average network throughput are obtained by analysing the derived Markov model. The analytical results, which are validated through simulations, show the improvement in performance compared to the carrier sense multiple access with collision avoidance protocol that employs omnidirectional antennas.

In this paper, we investigate the performance of superimposed training-based channel estimation techniques considering an asynchronous code-division multiple-access (CDMA) uplink transmission over frequency-selective fading channels. In that, we analyze the performance of a channel estimation and data detection scheme based on superimposed training for space-time spreading (STS) systems. Our results show that the scheme enhances the performance of the space-time system by eliminating the interference effect from both the channel and data estimates using two decorrelators; channel and data decorrelators. Compared with other conventional estimation techniques, the underlying system is more robust to channel estimation errors. Furthermore, both simulations and analytical results indicate that full system diversity is achieved.

In this paper, we propose a new channel access protocol for transmission over cooperative ad-hoc networks. The proposed protocol, which aims at minimizing the number of blocked nodes and consequently improving the system throughput, employs directional antennas at both source and relay stations. The network performance under the proposed settings is modelled using continues Markov chains. Steady state transmission blocking probability and average network throughput are obtained by analyzing the Markov model. Both analytical and simulation results show the improvement in performance compared to carrier sense multiple access with collision avoidance protocol that employs omnidirectional antennas.

In this paper, we investigate a cross-layer transmit antenna selection (T-AS) approach for multiple-input multiple- output spatial multiplexing (MIMO-SM) systems employing decision-feedback detector (DFD), over spatially correlated Ricean channels. The selected transmit antennas are those that maximize the link layer throughput of MIMO channels. A closed- form expression for the system throughput with perfect channel state information (CSI) is derived. Extensive simulation results are provided for the system performance assessment, showing that the cross-layer T-AS scheme always assigns the transmission to the antenna combination which sees better channel conditions, resulting in a substantial improvement over the optimal capacity- based T-AS approach.

In this paper, we investigate a cross-layer transmit antenna selection (T-AS) approach for multiple-input multiple-output spatial multiplexing (MIMO-SM) systems, that employ decision-feedback detector (DFD), over Ricean flat-fading channels. The selected transmit antennas are those that maximize the link layer throughput of MIMO channels. A closed-form expression for the system throughput with imperfect channel state information (CSI) is derived. Extensive simulation results are provided for the system performance assessment, showing that the cross-layer T-AS scheme always assigns the transmission to the antenna combination which sees better channel conditions, resulting in a substantial improvement over the optimal capacity- based T-AS approach. Our results show that the capacity-based T-AS is more robust to imperfect channel estimation. However, in all cases, the cross-layer T-AS delivers higher throughput gains than the capacity-based T-AS.

The probability of bit error of multiple-input multiple-output spatial multiplexing (MIMO-SM) systems employing linear zero-forcing (ZF) receivers is addressed. In this paper, we first present a near exact approximation for the average bit error rate (BER) of each substream with quaternary phase-shift keying (QPSK) in spatially correlated Ricean distributed channels. Then a closed-form expression for the optimal transmit correlation coefficient of two-input two-output spatial multiplexing (TITO-SM) systems is derived. Extensive simulations are presented to validate the accuracy of the analytical results.

In this paper, the performance of cooperative networks introduced in the literature is analyzed for asynchronous direct sequence code-division-multiple-access (DS-CDMA) systems in frequency-selective slow fading environment. We show that the full benefits of cooperative diversity cannot be achieved if no multiuser interference suppression is employed at the cooperative end. We consider two scenarios; perfect and imperfect inter-user channel. The bit-error-rate performance of the cooperative system is investigated for an uplink transmission where a decorrelator detector is used at both the relay and base station receivers. The proposed receiver has the advantage of mitigating the effects of multipath fading and multiple-access interference. Both simulation and analytical results are presented to demonstrate the diversity gains of cooperative networks.

The bit error rate (BER) performance of the zero-forcing (ZF) receiver over transmit-correlated flat Ricean fading channels is investigated. In particular, for a multiple-input multiple-output (MIMO) channel with M transmit and N receive antennas, an approximation for the average BER of each substream is derived. Then the system performance in receive-correlated flat Ricean fading channels is addressed. In this case, it is shown that the performance, when N=M, is the same as that of transmit-correlated flat Ricean fading channels. A closed-form expression for the optimum transmit correlation coefficient, which achieves the maximum capacity (i.e. uncorrelated case), is also derived. As a result, a significant capacity gain is achieved by exploiting the knowledge of the Ricean channel. Extensive simulations are presented to validate and demonstrate the performance gain with different system parameters.

The performance of direct-sequence code division multiple access (DS-CDMA) using space–time spreading system, over frequency-selective fading channels, is investigated. The underlying transmit diversity scheme, previously introduced in the literature, is based on two transmit and one receive antenna. It was shown that when employed in flat fast-fading channels, the received signal quality can be improved by utilising the spatial and temporal diversities at the receiver side. We study the problem of multiuser interference in asynchronous CDMA systems that employ transmit/receive diversity using space–time spreading. To overcome the effects of interference, a decorrelator detector is used at the base station. Considering binary phase-shift keying transmission, we analyse the system performance in terms of its probability of bit error. In particular, we derive the probability of error over frequency-selective Rayleigh fading channels for both fast and slow-fading channels. For the fast-fading channel, both simulations and analytical results show that the full system diversity is achieved. On the other hand, when considering a slow-fading channel, we show that the scheme reduces to conventional space–time spreading schemes where the diversity order is half of that of fast-fading.

In this paper, we consider antenna/relay selection for coded cooperative networks in an effort to improve their end-to-end performance by improving the detection reliability at the relay nodes. Considering amplify-and-forward (AF) relaying, we analyze a previously proposed distributed coded cooperation scheme in conjunction with antenna/relay selection. Specifically, we derive upper bounded expressions for the bit error rate assuming M-ary phase shift keying (M-PSK) transmission. Our analytical results show that the maximum diversity order of the system is maintained for the entire range of symbol error rate of interest, unlike the case without antenna/relay selection. Several numerical and simulation results are presented to demonstrate the efficacy of the proposed scheme.

In this paper, we investigate a cross-layer transmit antenna selection (AS) approach for the decision-feedback detector (DFD) over spatially correlated flat Ricean fading multiple-input multiple-output (MIMO) channels. Closed-form expressions for the system throughput with both perfect and imperfect channel estimation are derived. Considering a training-based channel estimation technique, we show that the capacity-based AS is more robust to imperfect channel estimation. However, in all cases, the cross-layer AS delivers higher throughput gains than the capacity-based AS.

In this paper, we investigate a transmit antenna selection (TAS) approach for the decision-feedback detector (DFD) over Rayleigh fading channels. In particular, for a multipleinput multiple-output (MIMO) channel with M transmit and N (N ⩾ M) receive antennas, we derive a lower bound on the outage probability for the TAS approach. The selected transmit antennas are those that maximize the post-processing signalto- noise ratio (SNR) at the receiver end. It is shown that the proposed TAS approach achieves a performance close to optimal selection based on exhaustive search, introduced in the literature, but at a lower complexity. Simulation results are presented to validate and demonstrate the performance gain of the proposed TAS approach.

Space-time spreading has been employed to exploit the spatial diversity in multiple-input multiple-output (MIMO) code-division multiple-access (DS-CDMA) systems. In the presence of multiuser interference, resulting from the cross-correlation between users' code sequences, the full system diversity cannot be achieved when using the conventional matched receiver. In this paper we investigate the performance of space-time spreading (STS) and transmit diversity in the uplink of a MIMO DS-CDMA system over Nakagami-m fast-fading channels. The space-time system employs N = 2 transmit antennas and L receive antennas at the user side and base-station, respectively. We analyze the performance of the system when a linear decorrelator detector is employed to mitigate the effect of multiuser interference. In our analysis, we start by finding the probability density function (pdf) of the signal-to-interference ratio (SINR) at the output of the space-time combiner. Using this pdf, we derive a closed form expression for the bit-error rate (BER) for binary phase-shift keying (BPSK) transmission. The accuracy of the derived pdf and BER expression are verified using simulations, for different values of fading figure m and number of users.

In this paper, we consider distributed coding for wireless cooperative networks with antenna/relay selection. The aim of this of work is to find ways to improve the reliability of the source-relay link in an effort to maintain the diversity order available in the system. To this end, we propose to use antenna selection at the relay node whereby the antenna with the best instantaneous received signal to noise ratio is selected. This assumes that the relay node is equipped with multiple antennas, but only one radio frequency (RF) chain is employed. The concept of antenna selection can be extended to relay selection. That is, among the available relay nodes, the one with the best source-relay link reliability is selected. Assuming decode-and-forward (DF) relaying, we analyze the antenna/relay selection in conjunction with a previously proposed distributed coded cooperation scheme based on convolutional codes. Specifically, we derive an upper bounded expression for the symbol error rate assuming M-ary phase shift keying (M-PSK) transmission. Our analytical results show that the maximum diversity order of the system is maintained for the entire range of bit error rate of interest, unlike the case without antenna selection. Several numerical and simulation results are presented to demonstrate the efficiency of the proposed scheme.

We introduce coded cooperation diversity using relay nodes in the decode-and-forward (DF) mode, in which the codewords of the source node are partitioned and transmitted through independent fading channels to both relay and destination nodes, to achieve remarkable gains over a noncooperative system. The main difference between the distributed and the noncooperative space-time coding is that the link between the source and the relay node is not error-free in the distributed case, as opposed to the noncooperative case. In this work, we take into account the errors in the source-relay link and derive upper bounded expression for the symbol error rate on DF relaying in the case of M-ary phase shift keying (M-PSK) transmission. Our analytical results show that the maximum diversity order is achieved provided that the source-relay link is more reliable than the other links. Otherwise, the diversity degrades. This is unlike the case when the relay nodes operating in the DF mode are based on symbol-by-symbol decoding and forwarding. We present several numerical examples to support our analytical expression.

In this paper, we propose a new joint channel and data detection scheme based on the superimposed training technique for space-time spreading systems. The proposed scheme enhances the performance by eliminating the multiple access interference from both the channel and data estimation by employing two decorrelators: channel and data decorrelators. On a frequency-selective fading channel, our simulation results show that the proposed scheme outperforms other conventional joint channel and data estimation techniques. Moreover, unlike other conventional detection techniques, our proposed estimation technique is shown to achieve the full system diversity.

Abstract Cooperative diversity is proposed to combat the detrimental effects of channel fading. In this paper, we investigate the effectiveness of cooperative diversity in interference limited ad hoc networks. The negative effects due to relay blocking on the network throughput are investigated. We show that the relay blocking problem is mainly dependent on the relay selection criterion. To overcome this problem, we propose a new cooperative diversity technique based on a modified IEEE 802.11 Medium Access Control (MAC) protocol. The throughput performance of the proposed MAC protocol is analyzed using a random structured network where nodes are assumed to be equipped with multiple antennas. In our simulations, we consider both single‐ and multiple‐relay scenarios over fading channels. Copyright © 2007 John Wiley & Sons, Ltd.

Abstract The use of directional antennas in mobile ad hoc networks (MANETs) has shown to offer large throughput gains relative to omnidirectional antennas. When used in ad hoc networks, directional medium‐access‐control (DMAC) protocols usually require all nodes, or part of nodes, to be aware of their exact locations. This location information is typically provided using a global positioning system (GPS). Although GPS systems are designed to be as nearly accurate as possible, there are still estimation errors that can cause a relatively large deviation from the actual GPS receiver position. In this paper, we investigate the effect of inaccurate node position estimation on the throughput of these protocols. Our results clearly indicate that the advantages of DMAC protocols diminish if the available position information is not accurate enough. As an alternative, we propose an efficient DMAC protocol that utilizes signal parameter estimation via the rotational invariance technique (ESPRIT) for direction‐of‐arrival (DOA) estimation; alleviating the need for GPS and, hence, avoiding the degrading associated with typical GPS position estimation errors. Moreover, unlike GPS‐based protocols, our protocol is suitable for both outdoor and indoor applications. Under different operating conditions and channel models, our simulation results show the throughput improvement achieved using the proposed protocol relative to the IEEE 802.11. Copyright © 2007 John Wiley & Sons, Ltd.

Asynchronous direct-sequence code-division multiple-access (DS - CDMA) using space-time spreading system is investigated over frequency-selective fast-fading channels. The underlaying transmit diversity scheme, previously introduced in the literature, is based on two transmit and one receive antenna. It was shown that when employed in flat fast-fading channels, the received signal quality can be improved by utilizing the spatial and temporal diversities at the receiver side. Here, the bit-error-rate (BER) performance of the underlying system is investigated for an uplink transmission where a decorrelator detector is used at the base station receiver. In particular, we derive a closed form expression for the probability of error over frequency-selective fast-fading channels. The analytical BER is derived as a function of both the number of multipath and users. The BER results show that the space-time spreading scheme exploits both the temporal and spatial diversity in a time-varying multipath channel. The results also show the accuracy of the derived expression when compared with simulation results for different number of users.

In this paper, we investigate the throughput performance of cooperative Medium-Access-Control (MAC) protocol in interference limited ad hoc networks and in the presence of position estimation errors. The throughput performance of the cooperative MAC protocol is analyzed using a random structured network where relay nodes are equipped with adaptive antennas to relay the received signal from the source node to the destination node. In the literature, a perfect position estimation of all nodes is commonly assumed. Here, we focus on the throughput performance of the cooperative network when taking into consideration the effect of directional-of-arrival (DOA) error caused by imperfect global-positioning system (GPS) position estimation. Our results show that using adaptive antennas at the relay becomes advantageous when the DOA error is less than 20 degrees. We noted that increasing the number of antennas (at the relay station) can improve the throughput performance but, on the other hand, the effect of node position error becomes more substantial.

The use of directional antennas in mobile ad hoc networks has shown to offer large potential throughput gains relative to omnidirectional antennas. When used in ad hoc networks, directional medium-access-control (DMAC) protocols usually require all nodes, or part of nodes, to be aware of their exact locations. This location information is typically provided using a global positioning system (GPS) which, typically, requires a line of sight in order to avoid the large signal attenuation and hence is not suitable for indoor applications. Moreover, as the inaccuracy associated with the GPS position estimation increases, the system throughput dramatically degrades. In this paper, we propose an efficient two-channel two-mode DMAC protocol. Our protocol employs two frequency division multiplexed channels: channel one used for omni-mode transmission and channel two for directional mode transmission. Signal parameter estimation via the rotational invariance technique (ESPRIT) is used for direction-of-arrival (DOA) estimation. By avoiding the reliance on GPS for obtaining the position information, our protocol is suitable for both outdoor and indoor applications. Under different operating conditions and channel models, our simulation results clearly show the throughput improvement achieved using the proposed protocol relative to the IEEE 802.11.

In this paper, we investigate the performance of a coded cooperation diversity using relay nodes in the decode-and-forward (DF) mode, in which the codewords of the source node are partitioned and transmitted through independent fading channels to both the relay and destination nodes. The application of such a cooperative coding scheme is shown to offer remarkable gains over a noncooperative system. Unlike noncooperative space-time coding, in distributed cooperative coding, the link between the source and the relay node is not error-free. In this work, we take into account the errors in the source-relay link and derive the outage probability of DF relaying for binary phase shift keying (BPSK) transmission. Our analytical results show that the proposed scheme achieves full diversity. This is different from conventional relay systems where the relay nodes operating in the DF mode are based on symbol-by-symbol decoding and forwarding. Numerical results are shown to support our analytical expression.

The performance of a space-time spreading (STS) transmit diversity scheme is examined over a frequency-flat and fast- fading Nakagami-m channel. Our focus will be on the uplink of the communication system where the STS system employs N=2 and L antennas at the user side and base-station (BS) respectively. Direct-sequence code-division multiple-access (DS-CDMA) is used in order to support many users and a linear decorrelator detector is used in order to combat the effect of multi-user interference. The modulation scheme considered is binary phase-shift keying (BPSK). In our study, we start by determining the probability density function (pdf) of the signal- to-interference and noise ratio (SNR) at the output of the ST combiner at the receiver side. Using this pdf, we derive a closed form expression for the bit error rate (BER) of the underlying system. The accuracy of the BER expression is verified when compared to simulations results for different values of the fading figurem.

Abstract In this paper, we study the use of channel coding in a direct‐sequence code‐division multiple‐access (DS‐CDMA) system that employs space‐time adaptive minimum‐mean square‐error (MMSE) interference suppression over Rayleigh fading channels. It is shown that the employment of adaptive antenna arrays at the receiver can assist in attenuating multiuser interference and at the same time speeds‐up the convergence rate of the adaptive receiver. In this work, we assess the accuracy of the theoretical results developed for the uncoded and convolutionally coded space‐time multiuser detector when applied to the adaptive case. It is found that the use of antenna arrays brings the receiver performance very close to its multiuser counterpart. Using performance error bounds, we show that a user‐capacity gain of approximately 200% can easily be achieved for the space‐time adaptive detector when used with a rate 1/2 convolutional code (CC) and a practical channel interleaver. This capacity gain is only 10% less than the gain achieved for the more complicated multiuser‐based receiver. Finally, we perform a comparison between convolutional and turbo coding where we find that the latter outperforms the former at all practical bit‐error rates (BER). Copyright © 2006 John Wiley & Sons, Ltd.

The performance of space-time transmit diversity is examined in a multiuser direct-sequence code division multiple access (DS-CDMA) system over fast-fading channels. The underlying space-time system employs two transmit and M receive antennas at the mobile user and receiver base station, respectively. We consider the performance of the space-time multiuser system when using the linear decorrelator detectorto combat the effect of multiuser interference. In our analysis, we derive a closed form expression for the probability of bit error in fast-fading channels. These theoretical results are then compared to simulations where it is shown that they are in excellent agreement. Our results demonstrate that, regardless of the system load, the full diversity order of 2NM for the fast- fading channel is always maintained and only a signal-to-noise ratio (SNR) loss is incurred.

Cooperative diversity based on ad hoc networking inherits a relay blocking problem which can cause network throughput degradation. In this paper, we propose a new cooperative diversity technique based on a modified IEEE 802.11 medium access control (MAC) protocol in interference limited ad hoc networks. The throughput performance of the proposed MAC protocol is analyzed for a random structured network where nodes are assumed to be equipped with multiple antennas. The proposed cooperative protocol makes use of directional antennas to reduce the effect of interference between adjacent nodes. We show that the relay blocking problem can be solved using our new cooperative protocol when each relay is equipped with directional antennas, and the TX-Relay-RX transmission mode is designed using two frequency channels. The throughput performance of the proposed protocol is analyzed for both single and multiple relay scenarios over fading channels.

The application of directional antennas in mobile ad-hoc networks (MANETs) has proved to offer large throughput gains relative to single-antenna systems. Recent works on directional medium-access-control (MAC) protocols have been only focused on their performance over additive white-Gaussian noise (AWGN) channels. In this paper, we evaluate the performance of directional MAC protocols when the channel is modeled as a slow-fading one. In this case, directional antennas are used for the transmission of data frames while control frames are sent using an omnidirectional antenna. Considering a slow-fading channel, our results show large throughput improvements when using directional MAC protocols relative to the IEEE 802.11 standard. Furthermore we show that the throughput loss on the fading channels relative to the AWGN channel, can be well compensated using antenna arrays. All our results show that the use of directional antennas at the mobile station can improve the channel efficiency in an ad-hoc network.

In this work we examine the probability of error performance for a multiuser DS-CDMA system that exploits spatial and temporal diversities over fast-fading Rayleigh channels using a space-time block coding scheme. We commence the analysis by finding the probability density function (pdf) for the signal to interference ratio (SIR). Then, using this pdf, a closed form expression for the probability of error using the decorrelator multiuser detector is obtained. We verify the accuracy of this expression by comparison with simulations for different number of users. Both theoretical and simulation results show that the diversity order is maintained irrespective of the system load.

We propose a tree structured variable length random binning scheme for lossless compression of binary memoryless sources. Previously proposed source coding schemes based on nested error correcting codes can be regarded as practical implementations of this random binning scheme. For sufficiently large data blocks, we prove that the proposed scheme asymptotically achieves the entropy limit. We also derive the distribution of the compression rate achieved by the tree structured random binning scheme. Comparing this distribution with the distribution obtained using a library of random binning schemes, we show that a nested code can achieve rates close to a library of codes but with much lower encoding/decoding complexity

Transmit diversity designed for fast-fading channels is examined in a multiuser direct-sequence code division multiple access (DS-CDMA) system over slowly-fading channels. The underlying space-time system employs two transmit antennas and M receive antennas at the user side and base-station receiver, respectively. The receiver employs a decorrelator multi-user detector. In our analysis, we derive a closed form expression for the bit error probability. These theoretical results, when compared to simulations, are shown to be very accurate. Both simulations and analytical results demonstrate that, regardless of the system load, the full diversity order of NM is always maintained and only a signal-to-noise ratio (SNR) loss is incurred when using a decorrelator detector at the receiver side. This SNR degradation is shown to be a function only of the number of users and independent of the number of transmit and/or receive antennas. Using our theoretical results, we show that the loss in SNR from the single-user bound can be well approximated by 10log(C/2) where C is a parameter of the interference level

The performance of space-time spreading transmit diversity designed for fast-fading channels is examined in a multiuser direct-sequence code division multiple access (DS-CDMA) system over quasi-static fading channels. The space-time system employs two transmit antennas and single receive antennas at the user side and base-station receiver, respectively. The receiver employs a decorrelator multiuser detector. In the analysis, we obtain a closed form expression for the bit error probability. It is shown that the performance of the space-time transmit diversity scheme, designed earlier for fast-fading channels, reduces to that of the existing schemes designed for slow-fading channels. Both simulations and analytical results demonstrate that, regardless of the system load, the full system diversity is maintained when a decorrelator detector is used at the receiver side.

We propose a tree structured variable length random binning scheme that enables an error correcting code to act as a source code. The existing source coding schemes based on turbo codes, low density parity check codes, and repeat accumulate codes can be regarded as practical implementations of this random binning scheme. We investigate the performance of lossless turbo source coding relative to the proposed tree structured random binning scheme. Our numerical results show that the compression rate achieved by lossless turbo source coding is far from the tree structured random binning bound. In that, we suggest improvements to enable short block length turbo source codes to achieve compression rates close to the tree structured random binning bound.

Cooperative diversity is proposed to combat the detrimental effects of channel fading. In this paper, we investigate the effectiveness of cooperative diversity in interference limited ad hoc networks. The throughput performance of ad hoc networks that employ cooperative diversity techniques is examined. The negative effects of relay transmission blocking and extra time delay due to using the relay node, on the network throughput are investigated. We show that the relay blocking problem is mainly dependent on the relay selection criterion. Furthermore, we examine a two-channel approach to solve the delay problem and hence improve the overall network throughput.

Lossless turbo source coding with decremental redundancy is an effective approach for compressing binary sources. A large block length lossless turbo source encoder offers compression rates close to the source entropy but with large latency. In this letter, we propose a lossless compression technique for binary memoryless sources using short block length turbo codes. To achieve compression rates close to the source entropy, we modify different components of the encoder. We focus on the design of the parity interleaver for different compression rates. Also, we replace the square shape puncturing array with a rectangular shape array that allows finer puncturing and hence improved compression rates. Finally, instead of a single code, we employ many codes operating in parallel. Given these modifications, we evaluate the encoding complexity of the proposed code

This paper investigates the use of convolutional coding in space-time minimum mean-square-error (MMSE) multiuser-based receivers over asynchronous multipath Rayleigh fading channels. We focus on the performance gain attained through error control coding when used with binary-phase-shift-keyed modulation (BPSK) and multiuser access based on direct sequence-code-division multiple access (DS-CDMA). In our analysis, we derive an approximation for the uncoded probability of bit-error in multipath fading channels. This bit-error rate (BER) approximation is shown to be very accurate when compared to the exact performance. For a convolutionally coded system, we obtain a closed form expression for the bit-error rate upper bound. This error bound is noted to be tight as the number of quantization levels increased beyond eight. Using our theoretical results, we obtain an estimate for the achieved user-capacity that accrues due to error control coding. It is found that using convolutional coding with 3-bit soft-decision decoding, a user-capacity gain as much as 300% can easily be achieved when complete fading state information plus ideal channel interleaving are assumed.

In this paper we investigate the multiuser performance of a space-time spreading scheme that uses space-time block codes (STBCs) to enhance the received signal quality by utilizing the spatial and temporal diversities at the receiver side. We study the performance of the transmit diversity scheme introduced in the literature for a DS-CDMA system using orthogonal and nonorthogonal spreading codes over Rayleigh fast-fading channel with two antennas at the transmitter and one in the receiver side. For the single user case, and using orthogonal spreading codes, we develop the performance analysis for the underlying transmit diversity scheme over fast-fading channels. We prove that using such a space-time spreading scheme can achieve a twofold of the diversity order obtained using existing space-time spreading schemes. To examine the effect of signal interference on the receiver performance, we consider the case of nonorthogonal codes where we employ a linear minimum-mean square-error (MMSE) multiuser detector as a suboptimum linear detector. The performance of the multiuser system is examined for a moderate number of users where we show that the diversity order is still maintained and only a SNR loss is incurred due to the residual interference. Finally, we compare the performance of the MMSE multiuser detector to a simple adaptive MMSE combining scheme.

Lossless turbo source coding with decremental redundancy is an effective approach for compressing binary sources. In this method, the message is encoded using a turbo code. Then the parities are heavily punctured using an iterative process and all non-punctured parities along with side information are sent to the decoder. To improve the performance, a single code can be replaced by a library of codes. The message is compressed using each code and the best result is sent to the decoder. The side information contains the number of punctured parities and the index of the applied code. Instead of transmitting the code index, we find a criterion to detect the code index using the transmitted parities, at the decoder. Compared to the case where the code index is transmitted, our method helps to achieve a better rate for short block length turbo source coders.

This paper investigates the use of convolutional coding in space-time minimum mean-square-error (MMSE) multiuser-based receivers over asynchronous multipath Rayleigh fading channels. We focus on the performance gain attained through error control coding when used with binary-phase-shift-keyed modulation (BPSK) and multiuser access based on direct sequence-code-division multiple access (DS-CDMA). In our analysis, we derive an approximation for the uncoded probability of bit-error in multipath fading channels. This bit-error rate (BER) approximation is shown to be very accurate when compared to the exact performance. For a convolutionally coded system, we obtain a closed form expression for the bit-error rate upper bound. This error bound is noted to be tight as the number of quantization levels increased beyond eight

Next generation wireless networks are characterized as heterogeneous networks, particularly in terms of its underlying technology. One of the challenges of these heterogeneous networks is to manage handoff. Mobile IP is chosen for managing the handoff to accommodate the all-IP vision of the future interconnected networks. However, the handoff management of the mobile IP is mainly for data services where delay is not of a major concern. Therefore, it would be considerable challenge to achieve low latency handoff for real-time services. In this paper, we propose a multicasting scheme for delay-sensitive applications. The proposed scheme is shown to reduce the latency introduced during the process of mobile IP-based handoff between heterogeneous networks. For low data rate applications, in which real-time voice services operate, the proposed scheme implements an adaptive packet size technique to reduce both the probability of packet loss and the handoff latency. We present simulation results to verify the improvements achieved using the proposed multicasting scheme as opposed to the standard mobile IP

In recent years, the use of directional antennas in wireless networks has been widely studied. Since the medium access control (MAC) protocol of the IEEE 802.11 standard is designed for the use of omnidirectional antennas, it cannot perform efficiently when directional antennas are used. In this paper, we study the performance of an efficient two-channel MAC protocol for ad hoc networks when equipped with directional antennas. The proposed protocol utilizes the large throughput offered by directional antennas using two frequency division multiplexed channels. The first channel is used for control information and the second for user data transmission. Based on this, the proposed MAC protocol operates in two main modes, the omnidirectional mode where one antenna is used for the transmission of users' control frames, and the directional mode where antenna arrays are used for the transmission of data frames. The proposed protocol is assessed using computer simulations based on randomly generated network topologies reflecting the random movement of nodes in the network. Based on these random topologies, we present performance comparisons with the existing MAC protocols using different system parameters. In all cases, the proposed MAC protocol is shown to offer a significant throughput improvement relative to the existing protocols

In this paper, we propose a modified linear parallel interference cancellation (PIC) structure using the blind adaptive minimum mean output-energy (MMOE) algorithm for direct-sequence code-division multiple-access (DS-CDMA) systems. Our aim is to improve both the convergence speed and the bit error rate (BER) performance of the conventional blind adaptive MMOE receiver in asynchronous AWGN channels. The proposed receiver structure makes use of the available knowledge of all users' spreading codes (i.e., at the base-station) to cancel multiple access interference (MAI) using a combined adaptive MMOE-PIC algorithm. It is shown that the proposed algorithm can significantly reduce the long training period requirement of the conventional adaptive MMOE receiver due to both the asynchronous transmission and the near-far problem. The BER of the proposed system is evaluated using the Gaussian approximation. Moreover, the performance of the proposed algorithm is compared with the conventional blind trained method. Both numerical and theoretical results show that the proposed receiver performs close to the standard minimum mean-squared error (MMSE) receiver whereas the adaptive MMOE detector suffers from a higher BER due to the imperfect filter coefficients.

In this letter, we consider the application of an iterative interference cancelation (IC) scheme to improve the speed of convergence of the adaptive minimum mean-squared error (MMSE) receiver for the reverse-link of a direct-sequence code-division multiple-access (DS-CDMA) system. Our aim is to reduce the overhead introduced during the receiver's training period. This will be achieved using an iterative interference cancelation algorithm such as the parallel interference cancelation (PIC) algorithm. The proposed iterative algorithm makes use of the available knowledge of all users' training sequences at the base-station receiver to jointly cancel multiple-access interference (MAI) and adapts to the MMSE optimum filter taps using the combined adaptive MMSE/PIC receiver. We employ the proposed iterative algorithm to both the least mean square and the recursive least squares algorithms where we show that a significant improvement in terms of convergence speed is achieved. Moreover, we demonstrate the near-far resistance of the proposed receiver.

We consider the application of iterative interference cancellation (IC) schemes to improve the convergence speed of the linear adaptive minimum mean-squared error (MMSE) receiver in a direct-sequence code-division multiple-access (DS-CDMA) system. Our aim is to reduce the overhead introduced during the training period. This will be achieved using an iterative interference cancellation algorithm such as the parallel interference cancellation (PIC) algorithm. The proposed system makes use of the available knowledge of the training sequences for all users (i.e., at the base station) to jointly cancel the multiple access interference (MAI) and adapts to the MMSE optimum filter taps using the combined adaptive MMSE/PIC. An examination of the adaptive MMSE/PIC receiver reveals that it is near-far resistant. Moreover, it is shown that using the least-mean-square (LMS) as a simple form of the adaptive MMSE receiver, the proposed algorithm requires only a few tens of symbols for convergence as compared to a few hundred training symbols needed for the conventional adaptive LMS receiver. Also employing a more complex, yet faster, a recursive-least squares algorithm (RLS) convergence is reached with few training symbols when PIC is used.

The performance of minimum mean squared error (MMSE) multiuser detection (MUD) using array processing and Reed-Solomon (RS) coding is analyzed for binary, direct sequence code division multiple-access (DS-CDMA) systems used over multipath channels. Wang and Poor (1999) discussed the performance of the space-time MMSE MUD in multipath channels with no FEC coding. In this paper we investigate the performance of a base station receiver that combines space diversity, multipath diversity MUD, and RS channel decoding. The improvement in the bit-error rate (BER) using channel coding is evident in our simulation results. It is shown that employing RS codes with space-time MMSE-MUD improves the system performance significantly compared to both the uncoded space-time MMSE-MUD and the coded space-time RAKE receiver. We also present the Gaussian approximation for MMSE-MUD used in multipath channels. This Gaussian approximation gives accurate results when compared to our simulation results. Based on the validity of this approximation, we define upper bounds for the coded system BER. These upper bounds, which are shown to be tight, can be a helpful tool in predicting the system performance with different system parameters.

In this paper we investigate the performance of a minimum mean square error (MMSE) multi-user detector (MUD) using antenna array processing and forward error correction channel codes (FEC) in a synchronous direct-sequence code-division multiple access (DS-CDMA) system over both additive white Gaussian noise (AWGN) and flat Rayleigh fading channels. Two different channel codes are considered in a binary phase shift-keyed (BPSK) modulation system; Reed-Solomon (RS) and convolutional codes. Through computer simulations, a comparison between the two codes shows that convolutional codes and soft decision decoding can offer at least 2 dB coding gain over the hard decision RS decoding in an AWGN channel and approximately a 5 dB gain in a flat fading channel at a BER of 10/sup -4/. In the paper, such results are compared with these in the text (Wicker 1995) for the single user case and AWGN channels.

The ATM performance over fiber-optic terrestrial networks is known to achieve high quality-of service (QoS) classes supporting different traffic types, and very low probability of bit errors, A probability of cell-loss on the order of 10/sup -10/ and a bit-error-rate (BER) on the order of 10/sup -8/ for data services are made possible on fiber-optic links. In contrast to the fiber-optic case, satellite transmission is often subjected to a large number of channel errors due to atmospheric noise and channel interference. Therefore, in order to achieve a fiber-optic like performance on the satellite channel, forward-error-correction (FEC) codes are essential for both the header and data fields of the ATM cell. This paper discusses two different FEC coding schemes for the ATM cell header. In addition, a FEC code design for data services (corresponding to AAL5) is presented. In the analysis we assume that there are no significant burst errors problem on the SATCOM link, and errors due to channel transmissions are mostly random errors.