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Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

Cognitive radio (CR) was coined to address evolving user needs two decades ago. Since then, it also has been considered a way to mitigate perceived spectrum scarcity in various application areas. In this article, the status of military CR research is assessed through a systematic literature review of 193 articles, from 2013 to 2020, using IEEE thematic topics, technology readiness levels, as well as comprehensive capability meta-model as analytical frameworks. The annual distribution of military CR research indicates continuing interest. The military CR research seems to be prolific on topics like waveform design and security, not forgetting a steady interest in CR networking topics. Significantly low numbers of papers address applications, services, and standardization. In general, military CR research seemed sporadic and scattered. Our data suggest that a transition from individual techniques, component-level subsystem research in the direction of systems engineering approach, and system-level studies has not yet happened, and the overall TRLs remain low. The absence of publicly articulated comprehensive treatment of the notion of military cognitive radio system is graphic and has led to disjointed, scattered research at the subsystem level. [ABSTRACT FROM AUTHOR]

This paper considers the downlink of a cognitive radio (CR) network formed by multiple primary and secondary transmitters, where each multiantenna transmitter serves a preknown set of single-antenna users. This paper assumes that the secondary and primary transmitters can simultaneously transmit their data over the same frequency bands to achieve high system spectrum efficiency. This paper considers the downlink balancing problem of maximizing the minimum signal-to-interference-plus-noise ratio (SINR) of the secondary transmitters subject to both the total power constraint of the secondary transmitters and the maximum interference constraint at each primary user due to secondary transmissions. This paper proposes solving the problem using the alternating direction method of multipliers, which leads to a distributed implementation through limited information exchange across the coupled secondary transmitters. This paper additionally proposes a solution that guarantees feasibility at each iteration. Simulation results demonstrate that the proposed solution converges to the centralized solution in a reasonable number of iterations. [ABSTRACT FROM AUTHOR]

This paper considers a general stochastic resource allocation problem that arises widely in wireless networks, cognitive radio, networks, smart-grid communications, and cross-layer design. The problem formulation involves expectations with respect to a collection of random variables with unknown distributions, representing exogenous quantities such as channel gain, user density, or spectrum occupancy. We consider the constant step-size stochastic dual subgradient descent (SDSD) method that has been widely used for online resource allocation in networks. The problem is solved in dual domain, which results in a primal resource allocation subproblem at each time instant. The goal here is to characterize the non-asymptotic behavior of such stochastic resource allocations in an almost sure sense. It is well known that with a step size of \epsilon , SDSD converges to an \mathcal {O}(\epsilon) -sized neighborhood of the optimum. In practice, however, there exists a trade-off between the rate of convergence and the choice of \epsilon $ . This paper establishes a convergence rate result for the SDSD algorithm that precisely characterizes this trade-off. Toward this end, a novel stochastic bound on the gap between the objective function and the optimum is developed. The asymptotic behavior of the stochastic term is characterized in an almost sure sense, thereby generalizing the existing results for the stochastic subgradient methods. For the stochastic resource allocation problem at hand, the result explicates the rate with which the allocated resources become near-optimal. As an application, the power and user-allocation problem in device-to-device networks is formulated and solved using the SDSD algorithm. Further intuition on the rate results is obtained from the verification of the regularity conditions and accompanying simulation results. [ABSTRACT FROM PUBLISHER]

Spectrum sharing and aggregation among authorized secondary users (A-SUs) are important tasks in operating effective cognitive radio networks. Furthermore, in protecting spectrum sharing/aggregation against unauthorized secondary users (UA-SUs), secondary user access control (SUAC) is needed, which is investigated in this paper. A jamming signal is injected to degrade the spectrum sensing performance of UA-SUs, while reliable spectrum sensing performance for A-SUs can be achieved through an oblique projection-based jamming cancellation method. An orthogonal frequency division multiplexing-based transmission model is considered in this paper. The generalized likelihood ratio test algorithm is used for both authorized and unauthorized SUs in spectrum sensing. Numerical results show the effectiveness of the proposed SUAC in degrading the spectrum sensing performance of the unauthorized SUs. [ABSTRACT FROM PUBLISHER]

Highway platooning of vehicles has been identified as a promising framework in developing intelligent transportation systems. By autonomous or semi-autonomous vehicle control and intervehicle coordination, an appropriately managed platoon can potentially offer enhanced safety, improved highway utility, increased fuel economy, and reduced emissions. This paper is focused on quantitative characterization of the impact of communication information structures and contents on platoon safety. By comparing different information structures that combine front sensors, rear sensors, and wireless communication channels, as well as different information contents such as distances, speeds, and drivers' actions, we reveal a number of intrinsic relationships between vehicle coordination and communications in platoons. Typical communication standards and related communication latency are used as benchmark cases in this paper. The findings of this paper provide useful guidelines in sensor selections, communication resource allocations, and vehicle coordination. [ABSTRACT FROM AUTHOR]

In traditional spectrum sensing, binary hypothesis testing has been used to detect whether a frequency band is being occupied by the primary user or not. In this correspondence paper, we investigate $M$ -ary hypothesis testing for spectrum sensing to further identify the signal type of the primary user. Linear cooperation among spatially distributed cognitive radios is applied to combine the observation statistics and make a final decision. The problem of data fusion is formulated as minimizing the total probability of misclassification subject to the constraint on the probability of successful classification. To deal with the non-convex problem formulated, we develop two solutions in this paper. In the first solution, we transform the problem into an unconstrained one, and adopt a zooming-based search algorithm to iteratively update multiple continuous variables until convergence. This solution requires searching all possible combinations of variables. To reduce the computation complexity and achieve closed-form analytical expressions, in the second solution, we decompose the original problem into multiple subproblems, each with the objective of minimizing an individual probability of misclassification. These subproblems can lead to closed-form expressions, and the weights are chosen as the best ones that correspond to the minimum total probability of misclassification. The proposed solutions are examined numerically, and the results show that the decomposition-based solution can achieve performance comparable to the zooming-based one but with much less complexity. [ABSTRACT FROM AUTHOR]

A generalized framework of spectrum sensing disruption for a power-limited adversary is proposed in this paper. In the literature, a conventional sensing attack typically assumes that the adversary has perfect knowledge of the spectral usage status. The framework in this paper considers a more general case where there are uncertainties in the estimates at the adversary. These uncertainties are modeled utilizing the probability of detection and the probability of false alarm. Then, the sum of the conditional probabilities of false detection at the secondary within the spectral range of interest, conditioned on the adversary’s estimated spectrum usage status, is maximized. It is shown that the optimal sensing attack, given perfect estimation is a special case of the proposed framework. When the adversary has perfect spectrum usage information, this framework reduces to a previously demonstrated optimal sensing disruption. When the adversary has imperfect information on the spectral status, the proposed framework is significantly more robust than conventional sensing attacks. Further, when the adversary’s power budget increases, it asymptotically approaches the sensing disruption performance upper bound. [ABSTRACT FROM AUTHOR]

The eight papers in this special issue focus on cognitive inspired networked applications. Cognition is emerging as a new and promising methodology with the development of cognitive-inspired computing, ubiquitous networks, and systems, which has the potential to enable a large class of applications. However, recent advances, such as interference alignment, collaborative communications, and cognitive computational theory, make us lack a clear understanding of analytical cognitive-inspired computing, networks, and systems, as well as best practices, to design resource-efficient, utility, pervasive, scalable, and autonomy oriented cognitive systems and applications. The objective of this special Issue is to bring together state-of-the-art research contributions that address these key aspects of cognitive inspired network systems and applications. [ABSTRACT FROM PUBLISHER]

Network densification over space and spectrum is expected to be key to enabling the requirements of next generation mobile systems. The pitfall is that radio resource allocation becomes substantially more complex. In this paper, we propose LaSR, a practical multi-connectivity scheduler for OFDMA-based multi-RAT systems. LaSR makes optimal discrete control actions by solving a sequence of simple optimization problems that do not require prior information of traffic patterns. In marked contrast to previous work, the flexibility of our approach allows us to construct scheduling policies that achieve a good balance between system cost and utility satisfaction, while jointly operate across heterogeneous RATs, accommodate real-system requirements, and guarantee system stability. Examples of system requirements considered in this paper include (but are not limited to): constraints on how scheduling data can be encoded onto signaling protocols (e.g., LTE's DCI), delays when turning on/off radio units, or on/off cycles when using unlicensed spectrum. We evaluate our scheduler via a thorough simulation campaign in a variety of scenarios with e.g., mobile users, RATs using unlicensed spectrum (using a duty cycle access mechanism), imperfect queue state information, and constrained signaling protocol. [ABSTRACT FROM AUTHOR]

In deterministic deployment of wireless sensor networks (WSNs), nodes are carefully placed at desired locations, with careful planning for separation distances, heights, and node orientations. This deployment strategy ensures good radio communication and sensing coverage. However, deterministic deployments are impractical for harsh large tree vegetation environments that span hundreds of kilometers. In these cases, stochastic deployments may enable effective deployment strategies. When relying on stochastic deployments, nodes are likely to be positioned at un-desired locations with unknown node orientations, which can lead to suboptimal radio connectivity and network performance. This paper examines the effects of factors affecting path loss under conditions resembling stochastic aerial deployments of WSN. Unlike previous work in the existing literature, this paper takes into account important deployment factors that affect network performance, such as node orientation and positioning, energy-to-bit-rate ratio, packet loss rate, and the composition of environment. Based on experimentation, a set of path loss models are proposed to model radio signal propagation in jungle-like environments under side-effects encountered in aerial deployments. The proposed models are compared with theoretical models and with other proposed models in the literature. Results show a significant difference with improved network performance that exhibits enhanced energy-to-bit-rate ratio, reduced packet loss rate, and increased node lifetime. [ABSTRACT FROM AUTHOR]

Rendezvous is a fundamental building block in distributed cognitive-radio networks (CRNs), in which pairs or groups of users must find a jointly available channel. Research on the rendezvous problem has focused so far on minimizing the time to rendezvous (to find a suitable channel) or on maximizing the rendezvous degree (percentage of channels on which rendezvous can take place). In this paper, we model the rendezvous problem in a more realistic way that acknowledges the fact that available channels may suffer from interference which varies from location to location as well as over time. In other words, channels are influenced by heterogeneous interference. In this setting, CRNs benefit from rendezvous methods that find a quiet channel, which supports high symbol rates and does not suffer much from dropped packets. In this paper, we propose three important rendezvous design disciplines to achieve bounded rendezvous time, full rendezvous degree, and to rendezvous on quiet channels that suffer little interference. We first present the Disjoint Relaxed Difference Set (DRDS) based rendezvous algorithm as a cornerstone, which ensures rendezvous on every channel (full rendezvous degree) in bounded time. When channels suffer from heterogeneous interference, we propose the Interference based DRDS (I-DRDS) algorithm which ensures rendezvous on channels with less interference, incorporating the interference normalization and interference mapping methods. We conduct extensive simulations to evaluate the proposed algorithms; compared with the state-of-the-art algorithms, the results show that I-DRDS has the best rendezvous performance on less interfered channels, with slightly larger rendezvous time. [ABSTRACT FROM AUTHOR]

In this paper, we quantify the impact of uncertainty in predicted mobility information on the sensing cost of the mobile sensing problem (i.e. sensing of target areas) in vehicular sensor networks. We focus on vehicular sensor networks in which the communication channel is the bottleneck of the system, and sensing the environment is meant to be provided with a minimum load on the communication channel. First, we formulate an opportunistic scheduler, which does not utilize predicted mobility information in the sensing procedure. Then, we formulate a strategic scheduler, which utilizes predicted mobility information in the sensing procedure. After that, we propose two types of noise models that capture the uncertainty in knowing mobility information. We quantify the impact of uncertainty in knowing mobility information on the sensing cost of the strategic scheduler using an independent coverage model. We find that the strategic scheduler outperforms the noise-free opportunistic scheduler even when the strategic scheduler utilizes noisy mobility information up to a certain threshold of noise. This threshold is determined in the paper, and is referred to as the breaking point of the strategic scheduler. Simulation results of the breaking point closely matches our analysis, which demonstrates the correctness of the used analysis methodology. Finally, we use an estimation model for the mobility information between two points on the road, and we propose an algorithm to keep the scheduling scheme below the breaking point. [ABSTRACT FROM AUTHOR]

Cognitive radio networks (CRNs) have been proposed to solve the spectrum scarcity problem. One of their fundamental procedures is to construct a communication link on a common channel for the users, which is referred to as rendezvous. In reality, the capability to sense the spectrum may vary from user to user. We study distributed rendezvous for heterogeneous sensing capabilities in this paper. The licensed spectrum is divided into $n$ n channels, $U = \lbrace 1,2,\ldots,n\rbrace$ U = { 1 , 2 ,... , n } . We denote the sensing capability of user $i$ i as $C_i \subseteq U$ C i ⊆ U and the set of available channels (i.e., the channels not occupied by paying users) as $V_i \subseteq C_i$ V i ⊆ C i . Due to hardware differences, the users may have different sensing capabilities: $C_i \ne C_j$ C i ≠ C j , and this is called heterogeneous sensing capability. In this paper, we propose efficient algorithms for two scenarios: the fully available scenario where $V_i = C_i$ V i = C i and the partially available scenario where $V_i \subseteq C_i$ V i ⊆ C i . Our idea is to utilize two ‘pointers’ to traverse the sensing capability set, which sets our algorithms apart from the extant rendezvous algorithms. Considering any two neighboring users $a, b$ a , b , we propose the Traversing Pointer (TP) algorithm that guarantees rendezvous in $O(\max \lbrace |C_a|,|C_b|\rbrace \log \log n)$ O (max { | C a | , | C b | } log log n) time slots for the fully available scenario. This result is only $O(\log \log n)$ O (log log n) larger than the theoretical lower bound. Moreover, it removes an $O(\min \lbrace |C_a|,|C_b|\rbrace)$ O (min { | C a | , | C b | }) factor when compared to the state-of-the-art result ($O(|C_a||C_b|)$ O (| C a | | C b |) in S.-H. Wu et al. For the partially available scenario, we propose the Moving Traversing Pointers (MTP) and Prime based Moving Traversing Pointers (P-MTP) algorithms that can guarantee rendezvous within $O((\max \lbrace |V_a|,|V_b|\rbrace)^2\log \log n)$ O ((max { | V a | , | V b | }) 2 log log n) and $O(|V_a||V_b|\log \log n)$ O (| V a | | V b | log log n) time slots respectively, where the latter one combines the pointers and a common technique of plugging in a prime number. The proposed algorithms work more efficiently than the previous best result ($O(|C_a||C_b|)$ O (| C a | | C b |) in C.-C. Wu et al. under various circumstances. We also conduct extensive simulations and the results corroborate our analyses. [ABSTRACT FROM AUTHOR]

Describes the above-named upcoming conference event. May include topics to be covered or calls for papers. [ABSTRACT FROM AUTHOR]

This paper considers a green cognitive radio network wherein each secondary user (SU) is solely powered by an energy harvester which extracts energy from RF signals of a primary user transmitter. Each SU operates in a harvesting–transmitting fashion periodically. In each period, all SUs first harvest energy for a fixed duration and then transmit data using the harvested energy to one access point for the rest of the period in the code-division multiple access method. Considering the intrinsic harvesting-throughput tradeoff, we jointly optimize the harvesting time and transmit powers of SUs to maximize the sum throughput of the network, subject to the primary interference constraint and the energy causality constraint. Despite of the nonconvex nature of the formulated optimization problem, we find an equivalent but convex substitution to the original problem. Then, we propose a dual-decomposition-based solution method to solve the problem. Simulations finally demonstrate the efficiency of this paper. [ABSTRACT FROM AUTHOR]

In this paper, for an overlay cognitive radio system in which the primary users’ information exchange is assisted by a secondary multiple-input multiple-output two-way relay network, we propose two spectrum sharing schemes that achieve the full diversity gain in both the primary and secondary networks. In the spectrum sharing problem that we formulate, there are two degrees of freedom to be optimized: relay selection and spectrum allocation. We jointly optimize these two degrees of freedom such that the achievable rate in the secondary network is maximized under the constraint that a minimum quality of service requirement in terms of the achievable rate is satisfied for the primary network. We analyze the performance of the proposed schemes in terms of the outage probability (that reflects the rate of unsuccessful spectrum sharing), the asymptotic behavior and the diversity order over Rayleigh fading channel. The analysis of the paper shows that in both of the proposed schemes, the full diversity of order $N M^2$ is achieved by the primary users, where $N$ is the number of the secondary users and $M$ is the number of antennas per terminal. Moreover, in both of the schemes, the full selection diversity of order $N M^2$ is guaranteed for the secondary network. We also provide extensive simulation results to confirm the theoretical analysis of this paper. [ABSTRACT FROM AUTHOR]

This correspondence paper studies the impact of nonidentically distributed channel estimation error on performance of a scheduled space-time block coding with mean-value power allocation in cognitive-radio multiple-input multiple-output system. Using the derived probability density function, the exact closed-form expressions of the proposed system for outage probability, ergodic capacity, and symbol-error rate (SER) with both $\rm M$ -ary quadrature amplitude modulation and phase shift keying modulation are derived. Using asymptotic analysis, this paper quantifies the diversity order for both outage probability and SER in the presence of nonidentically distributed error. From the analysis results, this paper shows that the diversity order is enhanced by the multiuser diversity as the number of user terminals increases regardless of outage probability and SER, while the spatial diversity is eliminated. [ABSTRACT FROM AUTHOR]

This paper applies machine learning to optimize the transmission policies of cognitive radio inspired non-orthogonal multiple access (CR-NOMA) networks, where time-division multiple access (TDMA) is used to serve multiple primary users and an energy-constrained secondary user is admitted to the primary users’ time slots via NOMA. During each time slot, the secondary user performs the two tasks: data transmission and energy harvesting based on the signals received from the primary users. The goal of the paper is to maximize the secondary user’s long-term throughput, by optimizing its transmit power and the time-sharing coefficient for its two tasks. The long-term throughput maximization problem is challenging due to the need for making decisions that yield long-term gains but might result in short-term losses. For example, when in a given time slot, a primary user with large channel gains transmits, intuition suggests that the secondary user should not carry out data transmission due to the strong interference from the primary user but perform energy harvesting only, which results in zero data rate for this time slot but yields potential long-term benefits. In this paper, a deep reinforcement learning (DRL) approach is applied to emulate this intuition, where the deep deterministic policy gradient (DDPG) algorithm is employed together with convex optimization. Our simulation results demonstrate that the proposed DRL assisted NOMA transmission scheme can yield significant performance gains over two benchmark schemes. [ABSTRACT FROM AUTHOR]

The Third Generation Partnership Project has standardized cellular vehicle-to-everything (C-V2X) sidelink Mode 4 to support direct communication between vehicles. In Mode 4, the sensing-based semipersistent scheduling (SPS) scheme enables vehicles to autonomously reserve and select radio resources. In particular, SPS has three processes to realize the resource scheduling, including continuously sensing resources, probabilistically reselecting resources, and periodically reserving resources. However, vehicles randomly select resources from the available resource lists in the resource reselection process, resulting in frequent packet collisions especially when radio resources are insufficient. Unlike the traditional SPS, this paper proposes a multiagent deep reinforcement learning-based SPS (RL-SPS) algorithm to help vehicles select appropriate radio resources with the aim of reducing packet collisions. Furthermore, a multi-head attention mechanism is adopted to improve the training efficiency by helping vehicles selectively pay attention to the observations and actions of neighbouring vehicles. It is worth noting that the RL-SPS algorithm fits the characteristics of Mode 4, which selects resources without requiring any global information. Simulation results show that RL-SPS outperforms other decentralized approaches and demonstrate the scalability and robustness of RL-SPS in a dynamic vehicular network. [ABSTRACT FROM AUTHOR]

Licensed Shared Access (LSA) is a new concept proposed by the radio spectrum policy group in order to optimize spectrum usage: a Mobile Network Operator (MNO) can access temporarily to other incumbent's spectrum after obtaining a license. The licensing process is made via an auction mechanism. The mechanisms proposed in the literature for the LSA context are one-shot auction mechanisms which allocate all the available spectrum as a unique block. In this paper, we first show how to increase the performance of those auctions (in terms of revenue, efficiency and fairness of the allocation) while preserving truthful bidding, by splitting spectrum and converting single block auctions into multi-block auctions. Simulation results illustrate how appropriately choosing the number of blocks allows to increase the aforementioned metrics. Second, we show how to convert one-shot mechanisms to equivalent ascending mechanisms (in terms of allocations and payments) so that we add transparency and privacy to the auction. [ABSTRACT FROM AUTHOR]

This paper addresses a brand new way of thinking that distinguishes itself by integrating two entities, namely, dynamic spectrum management on the one hand and cognitive dynamic system (CDS) on the other. To be more precise, the CDS performs the role of a cognitive engine centered at the heart of wireless communications, wherein licensed bands rooted in network providers responsible for primary users and unlicensed bands rooted in other networks responsible for secondary users. Thus, these two worlds embody primary users and secondary users, which collectively function within a single complex wireless communication network empowered by the CDS for the first time ever. [ABSTRACT FROM PUBLISHER]

It is well known that the data transport capacity of a wireless network can be increased by leveraging the spatial and frequency diversity of the wireless transmission medium. This has motivated the recent surge of research in cooperative and dynamic-spectrum-access (which we also refer to as cognitive spectrum access) networks. Still, as of today, a key open research challenge is to design distributed control strategies to dynamically jointly assign: 1) portions of the spectrum and 2) cooperative relays to different traffic sessions to maximize the resulting network-wide data rate. In this paper, we make a significant contribution in this direction. First, we mathematically formulate the problem of joint spectrum management and relay selection for a set of sessions concurrently utilizing an interference-limited infrastructure-less wireless network. We then study distributed solutions to this (nonlinear and nonconvex) problem. The overall problem is separated into two subproblems: 1) spectrum management through power allocation with given relay selection strategy; and 2) relay selection for a given spectral profile. Distributed solutions for each of the two subproblems are proposed, which are then analyzed based on notions from variational inequality (VI) theory. The distributed algorithms can be proven to converge, under certain conditions, to VI solutions, which are also Nash equilibrium (NE) solutions of the equivalent NE problems. A distributed algorithm based on iterative solution of the two subproblems is then designed. Performance and price of anarchy of the distributed algorithm are then studied by comparing it to the globally optimal solution obtained with a newly designed centralized algorithm. Simulation results show that the proposed distributed algorithm achieves performance that is within a few percentage points of the optimal solution. [ABSTRACT FROM PUBLISHER]

A call for papers on topics including active antennas, cognitive radio and channel modeling is presented.

This paper presents hardware-friendly algorithm for Gini-index (GI) cooperative-spectrum-sensing (CSS) algorithm for the data-fusion based cooperative cognitive-radio network. It simplifies the complex computations of sample-covariance-matrix (SCM) elements and test-statistics value of the conventional GI-based CSS algorithm. It delivers excellent detection performance under the realistic scenario of non-uniform dynamical noise and signal-power. Based on GI-based CSS algorithm, three different VLSI architectures are proposed for the cooperative spectrum sensor (CSR): CSR-VLAR1, CSR-VLAR2, and CSR-VLAR3. Here, CSR-VLAR1 is the first-time reported CSR-architecture for the conventional GI-based CSS algorithm. Subsequently, CSR-VLAR2 represents hardware-architecture of the proposed hardware-friendly GI-based CSS algorithm. Eventually, additional architectural optimization has been applied to CSR-VLAR2 that is transformed into the most hardware-efficient VLSI-architecture of CSR, referred as CSR-VLAR3, which is ASIC chip-fabricated in UMC 130 nm-CMOS technology node. Furthermore, both CSR-VLAR1 and CSR-VLAR2 are synthesized and post-layout simulated in the same technology node. Our ASIC-chip of CSR-VLAR3 occupies 0.35 mm2 of core-area and consumes 8.31 mW of total power at 88.8 MHz of maximum clock frequency, when the supply voltage is 1.2 V. Our CSR ASIC-chip has been functionally verified with the aid of real-world signals, using USRPs and FPGAs based test-setup of cooperative cognitive-radio network. Measured results of our design are compared with reported implementations where the proposed CSR is $4.52\times $ hardware-efficient and $2.8\times $ power-efficient than the state-of-the-art CSR-implementations. Thus, our work addresses the key challenge of designing hardware-efficient CSR that delivers excellent detection performance in the real-world scenario. [ABSTRACT FROM AUTHOR]

This paper presents a non-orthogonal multiple access (NOMA)-assisted full-duplex (FD) cooperative spectrum sharing protocol to achieve full-rate at a cell-edge primary user, where the secondary receiver helps the primary network relay the information to the cell-edge primary user. Unlike other FD cooperative schemes, the FD relay in the proposed system is not transmitting while receiving symbols from the primary transmitter. Hence, the proposed scheme can achieve full-rate at a primary user without degrading its outage performance. Further, it guarantees every alternate time-slots to the secondary network for accessing the licensed spectrum by employing a secondary receiver as a cooperative relay. In this paper, we derive the analytical expression for the outage probability and ergodic capacity. Moreover, we validate the derived expression through simulation and asymptotic results. The numerical and simulation results substantiate that the proposed scheme significantly outperforms other existing protocols. [ABSTRACT FROM AUTHOR]

In this corresponence paper, we address the spectrum sensing problem under the non-Gaussian noise scenario characterized by the symmetric alpha stable ($\text{S}\alpha \text{S}$) model. In this paper, a novel spectrum sensing method is proposed using the maximum generalized correntropy, aimed at improving the spectrum sensing performance in low generalized signal-to-noise ratio conditions. To further enhance the robustness of the proposed method, multiple receive antennas are applied to carry out cooperative spectrum sensing. Besides, a modified conjugate gradient algorithm is used to optimize the sparse vector for cooperative spectrum sensing. Finally, simulation results are presented to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]