Multi-user Cognitive Radio Networks Research Articles

User fairness-enhanced beamforming for IRS-assisted cognitive radio networks based on prior judgment mechanism

This research proposes a novel approach, namely the user fairness-enhanced beamforming (UFEB) scheme, to improve the user fairness in intelligent reflecting surface (IRS) assisted multiuser cognitive radio networks (CRNs). The proposed scheme takes into account the bounded channel state information (CSI) error of the primary user (PU) related channel and establishes a sum α-fair utility maximization problem, where α denotes the fairness adjustment index. This problem optimizes the system for fairness and rate, subject to constraints, such as the quality-of-service requirement of secondary users (SUs), limited interference on PUs, maximum transmit power of cognitive base station (CBS), and unit modulus of the IRS. Firstly, we design a prior judgment mechanism that makes preliminary assessments of the optimized requirements of the system in terms of fairness and rate, whose rationality can be proved in the simulation results. Then, with the prior selection of α, by exploiting the successive convex approximation and Taylor series expansion, we present an alternating iterative algorithm to solve the α-fair utility maximization problem. Simulation results show that the prior selection of α can accurately reflect the difference in the optimization requirements for sum rate and fairness of the system after the beamforming of CBS and the phase of IRS are determined, and validate the effectiveness and superiority of our proposed UFEB scheme in improving the fairness among SUs.

Downlink resource allocation for NOMA-based hybrid spectrum access in cognitive network

To solve the contradiction between limited spectrum resources and increasing communication demand, this paper proposes a wireless resource allocation scheme based on the Deep Q Network (DQN) to allocate radio resources in a downlink multi-user cognitive radio (CR) network with slicing. Secondary users (SUs) are multiplexed using non-orthogonal multiple access (NOMA). The SUs use the hybrid spectrum access mode to improve the spectral efficiency (SE). Considering the demand for multiple services, the enhanced mobile broadband (eMBB) slice and ultrareliable low-latency communication (URLLC) slice were established. The proposed scheme can maximize the SE while ensuring Quality of Service (QoS) for the users. This study established a mapping relationship between resource allocation and the DQN algorithm in the CR-NOMA network. According to the signal-to-interference-plus-noise ratio (SINR) of the primary users (PUs), the proposed scheme can output the optimal channel selection and power allocation. The simulation results reveal that the proposed scheme can converge faster and obtain higher rewards compared with the Q-Learning scheme. Additionally, the proposed scheme has better SE than both the overlay and underlay only modes.

Performance Analysis of Full-Duplex Multirelaying Energy Harvesting Scheme in Presence of Multiuser Cognitive Radio Network

This paper investigates the outage performance of secondary receiver with an energy harvesting cognitive radio network employing multiple full-duplex (FD) relaying protocol in presence of multi-user spectrum sharing environment. Under this energy harvesting aided FD relaying network scenario, adaptive transmit power policies for the secondary user (SU) source, secondary FD relays and exact outage probability of the secondary user are derived analytically in presence of multiple primary users and multiple secondary destinations. Impact of self interference at the receive antenna of each FD relay in addition to all the transmitting interference at all receive nodes is being addressed. A comparative study considering all the interference together in an FD relaying protocol with that of conventional half-duplex (HD) relaying protocol is carried out. With the same diversity order, a trade-off between FD multi-relay and multi-destination is shown. Closed form expressions of secondary outage probability for FD energy harvested relay equipped CR network has also been evaluated. Maintaining quality of service (QoS) of multiple primary users (PUs), power allocation strategies at each secondary transmitting node is taken care of. Finally, all the analytical closed form expressions have been validated through Monte-Carlo simulations.

Multiuser NOMA With Multiple Reconfigurable Intelligent Surfaces for Backscatter Communication in a Symbiotic Cognitive Radio Network

In this paper, we develop an optimization framework for the symbiotic operation of a multiuser cognitive radio network (CRN) consisting of a non-orthogonal multiple access (NOMA)-based primary network (PN) and a reconfigurable intelligent surface (RIS)-based secondary network (SN) sharing the same spectrum. In the symbiotic relationship formed in this system model, the RIS devices' PN signal backscattering not only provides multiple primary receivers with a spatial diversity gain but also supports the transmission of the RIS devices' own data to their designated receiver. First, through investigation of a simple network configuration, we obtain insights into the impacts of various factors on the system performance, and thus, into the importance of radio resource allocation for CRN symbiotic operation. Then, we develop a resource allocation framework that maximizes the sum rate of the PN and SN by jointly optimizing the system resources for cases of devices with hardware imperfections. To reduce the complexity of solving this problem, we convert it into a convex weighted minimum mean square error problem. An iterative algorithm for finding the optimal NOMA power allocation for the PN and the reflection coefficients of the RISs is developed, and its overhead is analyzed. Simulation results demonstrate that the proposed algorithm outperforms various benchmarks.

3D beamforming in Intelligent Reflecting Surface (IRS)-assisted multi-user cognitive radio networks

In this paper, we consider a multi-user cognitive radio network (CRN) equipped with an intelligent reflecting surface (IRS). We examine the network performance by evaluating the fairness of the secondary system, which is satisfying the minimum required signal to interference and noise ratio (SINR) for each secondary user (SU). The minimum SINR of the SUs is maximized by joint optimization of the beamforming vector and three-dimensional beamforming (3DBF) angles at the secondary base station (SBS) and also the phase shifts of the IRS elements. This optimization problem is highly non-convex. To solve this problem, we utilize Dinkelbach’s algorithm along with an alternating optimization (AO) approach to achieve some sub-problems. Accordingly, by further applying a semi-definite relaxation method, we convert these sub-problems to equivalent convex forms and find a solution. Furthermore, analytically we propose an algorithm for optimizing 3DBF angles at the SBS. Through numerical results, the improvement of the sum SINR of the secondary system using the proposed method is illustrated. Moreover, it is shown that as the number of reflecting elements of IRS increases, the sum SINR significantly augments while satisfying fairness. Also, the convergence of the proposed algorithm is verified utilizing numerical results.

Channel-Hopping Sequence and Rendezvous MAC for Cognitive Radio Networks.

In cognitive radio networks (CRNs), two secondary users (SUs) need to meet on a channel among multiple channels within a finite time to establish a link, which is called rendezvous. For blind rendezvous, researchers have devised ample well-grounded channel hopping (CH) sequences that guarantee smaller time-to-rendezvous. However, the best part of these works lacks the impact of network factors, particularly channel availability and collision during rendezvous. In this study, a new CH scheme is investigated by jointly considering the medium access control (MAC) protocol for single-hop multi-user CRNs. The analysis of our new variable hopping sequence (V-HS) guarantees rendezvous for the asymmetric channel model within a finite time. Although this mathematical concept guarantees rendezvous between two SUs, opportunities can be overthrown because of the unsuccessful exchange of control packets on that channel. A successful rendezvous also requires the exchange of messages reliably while two users visit the same channel. We propose a MAC protocol, namely ReMAC, that can work with V-HS and CH schemes. This design allows multiple rendezvous opportunities when a certain user visits the channel and modifies the conventional back-off strategy to maintain the channel list. Both simulation and analytical results exhibited improved performance over the previous approaches.

A golden eagle optimized hybrid multilayer perceptron convolutional neural network architecture‐based three‐stage mechanism for multiuser cognitive radio network

SummaryCognitive radio (CR) was developed to solve the issue of spectrum scarcity because CR users are unlicensed and can use the underutilized licensed spectrum without interfering with licensed users. During handoff, however, this CR creates a target channel sequence (TCS) to transfer the channel to the multiusers. It must also deal with issues such as equally assigning TCS to different secondary users (SUs), channel access collisions, and channel obsolesces during TCS development. TCS is issued to SUs in the same and different primary user (PU) areas. In this paper, the hybrid multilayer perceptron (MLP)–convolutional neural network (CNN) (MLP‐CNN) technique is specifically proposed to provide services to the SU even under proactive TCS constraints, as well as to address the aforementioned challenges. The MLP can increase cell quality by resolving the transmission time gap problem that occurs during the proactive stage. The MLP determines the characteristics of each SU in a specific area with high reliability for channel allocation, while the CNN allocates the channel based on the characteristics of the entire SU present in a specific area. This hybrid MLP‐CNN approach, on the other hand, is unable to achieve the optimal degree of optimized TCS generation accuracy due to the need for parameter fine‐tuning, which is accomplished using the golden eagle optimization technique. Different parameters, such as single‐user scenario, multiuser scenario, multichannel scenario, and network access overhead, are used to measure the performance of the proposed model. The handoff delay, average interval per target channel, and the overhead that occurs during the transmission of data by our proposed method are 0.56%, 0.17%–0.03%, and 50%–10% respectively. Thus, our proposed method outperformance all the existing approaches.

HSA-SPC: Hybrid Spectrum Access with Spectrum Prediction and Cooperation for Performance Enhancement of Multiuser Cognitive Radio Network

The spectrum sensing (SS) is employed to identify the status of licensed channel in the cognitive radio/user (CR/CU). Arbitrarily choosing the licensed channel for sensing in high traffic intensity network results into reduced throughput of cognitive user due to wait state problem in interweave mode. However, the wait state problem is overcome with hybrid spectrum access, resulting into improvement in throughput of CU. In addition, the sensing decisions without cooperation increases the sensing error and affect the sensing performance of CU resulting into reduction in throughput of CU. In this paper, we have proposed new approach that is Hybrid Spectrum Access with Spectrum Prediction and Cooperation (HSA-SPC) of cognitive users in which we have collectively employed spectrum prediction, cooperation and hybrid spectrum access to improve the sensing performance and throughput of CU. Further, for the proposed approach we have considered a new frame structure in multiuser environment and derived the closed form expression for the throughput. In addition to this, we have also shown the computation of energy efficiency for our proposed approach. Further, with the help of simulation results, we have shown the improvement in CU throughput with our proposed approach over reported literature. Comparison of various approaches with the proposed method HSA-SPC is also illustrated.

Improvement in the performance of mobile/hidden secondary users in multi‐user CRN

In a multi-user cognitive radio network (CRN), mobile or hidden secondary users (SUs) are present. Conventionally, such SUs utilize rigid threshold value (λ) for spectrum sensing (SS). When these SUs perform SS, they receive poor quality signals from primary users. This provokes erratic SS decisions, reduces the involvement of SUs in SS, diminishes the probability of detection (P d) and raises miss detection (P m) or false alarm of SUs. As a consequence of this, SUs are not able to participate in SS and allocation in emergency circumstances. To alleviate these problems, an adaptive λ can be utilized for mobile/hidden SUs during SS. Thus, a two-level λ optimization technique for SS has been proposed using a coalition formation approach to maintain co-operation among SUs. The proposed technique has been validated on Universal Software Radio Peripheral test-bed. Estimation of the probability of error (P e), P d, and P m for mobile/hidden SUs with energy detection SS (EDSS) performed using a fixed λ and the two-level optimized λ. From these estimations, it has been observed that compared to the fixed λ EDSS, P e and P m is reduced remarkably, and P d is enhanced for mobile/hidden SUs using the two-level optimized λ EDSS.

Queueing Delay Performance of Schedulers in Centralized Sensing Cognitive Radio Networks

Wireless communication has become cost-effective and it can be established very rapidly in order to bring broadband access to under-served rural as well as urban area users. The underutilized spectrum has enormous, untapped, public capacity to provide high-speed and pervasive broadband connectivity. The wireless Radio Frequency (RF) spectrum is highly congested due to the continuous increase in the demand for high data rate services. Cognitive Radio (CR) allows secondary/unlicensed users to use a part/slice of a licensed spectrum, referred to as a channel, whenever the primary/licensed user is not transmitting in that channel during a period. In order to find the primary–free channels in the RF spectrum, CR performs spectrum sensing before transmitting its signal. When there are multiple secondary users trying to sense, find and access the available free channels it becomes very challenging. Scheduling resolves the problem by the use of queues. The behaviour of queues in terms of Queuing delay performance of Schedulers in Multiuser Cognitive Radio Networks when there is a Centralized Coordinator performs sensing and scheduling, has been studied. Three different scheduling policies are considered, namely Maximum Gain, Maximum Weight and Proportionally Fair scheduling. When the network is heavily loaded, each policy presents varied Queuing Delay Performance. The cases when average Signal-to-Noise Ratio of users is considered to be unequal have been analysed with varying normalized sensing duration. Queuing delay for various arrival rates of users is plotted and the performances have been compared and discussed. From the results, it can be concluded that the Maximum Weight Scheduler performs better than others do.

A novel three-phase target channel allocation scheme for multi-user Cognitive Radio Networks

Generation and allocation of Target Channel Sequence (TCS) in relation to spectrum handoff introduces several challenges in a multi-user Cognitive Radio Network. The primary challenge is to ensure fair and unique TCS allocation to multiple Secondary Users (SUs) in order to prevent channel access conflict during concurrent handoff operations. Another challenge is to perform dynamic TCS generation to guard against channel obsolescence effects under time-varying user activities. Inclusion of real-time SUs further mandates the use of time-bound TCS generation in order to prevent call drops due to time-outs during handoff. To address these issues of fairness, uniqueness, dynamic and time-bounded TCS generation, this paper proposes a novel machine-learning enabled three-phase TCS generation and allocation scheme for multiple real-time SUs involved in Voice over IP (VoIP) communication. While proactive and periodic phases compute unique TCS in O (n log n) time, the reactive phase performs on-demand TCS generation. Generation of TCS is formulated as a fractional knapsack problem and greedy technique is applied therein to optimally select channels based on their existing conditions. A novel HMM-BiLSTM (Hidden Markov Model-Bidirectional Long Short Term Memory) framework is further devised to safeguard VoIP interests through periodic channel eligibility prediction. Additionally, an analytical framework is developed which records negligible probability of channel access conflict among multiple SUs. Simulation results confirm fair and dynamic channel allocation with reduced handoff delays and call-drops and intelligent learning based decision-making operations with high accuracy in comparison to existing approaches. Finally, test-bed implementation validates practical applicability of the proposed methodology.

Resource Allocation in Multi-User Cognitive Radio Network With Stackelberg Game

Resource allocation with sensing-based interference price is considered for multi-users cognitive radio (CR) network, in which the primary base station (PBS) controls the secondary users' (SUs) transmission by pricing the SUs' interference power. SUs firstly initiates data transmission based on the sensing decision and then PBS sets the interference price according to each SU's interference power. Stackelberg game is formulated to jointly obtain the maximum revenue for PBS and optimize the resource allocation to maximize the transmission gain for SUs. Two practical CR network models are investigated: the sensing based spectrum sharing(SBSS) and the opportunistic spectrum access(OSA). For each scenario, the resource allocation strategy is investigated under the two pricing schemes, namely uniform interference pricing and non-uniform interference pricing. Especially, the stackelberg equilibriums for the proposed games is characterized, and the distributed sensing based interference price bargaining algorithm is proposed according to different channel state information (CSI) for the non-uniform interference pricing case. Numerical examples are carried out to demonstrate the effectiveness of the proposed game algorithm under different pricing scheme.

Distributed Energy Efficiency Optimization for Multi-User Cognitive Radio Networks Over MIMO Interference Channels: A Non-Cooperative Game Approach

Energy efficiency (EE) optimization is investigated for a multi-user cognitive radio network (CRN) over multiple-input-multiple-output (MIMO) interference channels (ICs). To reduce the system overhead due to information exchange among the secondary CR uses (SUs), the EE optimization problem is formulated as a non-cooperative game, where each SU transmitter competes against the other SU pairs by optimizing its transmit covariance matrix. Specifically, each multi-antenna SU maximizes locally its energy efficiency in terms of the number of bits transmitted per unit energy consumption, subject to the per-SU transmit power constraint and the primary user (PU) perceived total interference constraint. It is proved that the formulated non-cooperative game admits at least one Nash equilibrium (NE), and the sufficient condition for a unique NE is derived subsequently. Primal decomposition is employed in the local EE optimization problem to relax the coupling PU perceived interference constraint such that fully distributed operation is allowed. A distributed iterative EE optimization algorithm (DIEEOA) is then proposed to obtain the unique NE, which is shown to converge to the global optimum. Linear precoding techniques are employed to mitigate the impacts of multi-user interference and imperfect channel state information (CSI). Through numerical simulations, effectiveness of the proposed scheme is validated and the system setting parameters’ impacts on the performance are studied.

A Reliable Co-operative Decision-Making Technique for the Improvement in the Performance of Multi-User CRN Using Coalition Game Theory

In the past few years, there has been a proliferation in commercial wireless services such as mobile, Wi-Fi, TV, etc. This has raised a demand for radio spectrum. As per the reports of spectrum occupancy and spectrum policy task force, most of the allocated spectrums are underutilized. Cognitive radio is an emerging technology, which can improve the spectrum utilization efficiency. Reliable spectrum sensing decision-making is a strenuous task in multi-user cognitive radio network (CRN). For enhancing spectrum utilization of such a network and obtaining a faster collaborative decision from honest secondary users, this paper proposes a reliable intra-coalition and inter-coalition decision-making technique (DMT). In this technique, the investigation of Pareto optimal coalitions is performed. Spectrum/channels have been allocated based on Pareto optimality of coalitions. Further, the performance of multi-user CRN has been evaluated using the proposed and traditional DMTs (i.e. OR/AND/maximum voting/half voting rules). It has been noticed that the proposed DMT outperforms DMTs. The performance parameters such as fusion decision processing time and the end-to-end communication delay to obtain a reliable collaborative decision have been reduced by approximately 50% using the proposed DMT compared to the traditional DMTs.

Optimal Spectrum Sensor Assignment in Multi-channel Multi-user Cognitive Radio Networks

Accurate detection of spectrum holes is the most important and critical task in any cognitive radio (CR) communication system. When a single spectrum sensor is assigned to detect a specific primary channel, then the detection may be unreliable because of noise, random multipath fading and shadowing. Also, even when the primary channel is invisible at the CR transmitter, it may be visible at the CR receiver (the hidden primary channel problem). With a single sensor per channel, a high and consistently uniform level of sensitivity is required for reliable detection. These problems are solved by deploying multiple heterogeneous sensors at distributed locations. The proposed spectrum hole detection method uses cooperative sensing, where the challenge is to properly assign sensors to different primary channels in order to achieve the best reliability, a minimum error rate and high efficiency. Existing methods use particle swarm optimization, the ant colony system, the binary firefly algorithm, genetic algorithms and non-linear mixed integer programming. These methods are complex and require substantial pre-processing. The aim of this paper is to provide a simpler solution by using simpler binary integer programming for optimal assignment. Optimal assignment minimizes the probability of interference which is a non-linear function of decision variables. We present an approach used to linearize the objective function. Since multiple spectrum sensors are used, the optimal constrained assignment minimizes the maximum of interferences. While performing the optimization, the proposed method also takes care of the topological layout concerned with channel accessibility. The proposed algorithm is easily scalable and flexible enough to adapt to different practical scenarios.

An efficient time-switching relaying protocol for multiuser cognitive radio relay networks with SWIPT

In this paper, we propose a new time-switching relaying (TSR) protocol for multi-destination dual-hop underlay cognitive radio relay networks with simultaneous wireless information and power transfer (SWIPT) technique. The proposed protocol maintains the same receiver complexity of the conventional TSR protocol with the ability for implementing both of them using the same hardware. We study the performance of the proposed TSR protocol and compare it with the conventional TSR and power-splitting relaying (PSR) protocols. Unified analytical expression is derived for the outage probability, in addition to studying the performance at high signal-to-noise ratio (SNR) values where unified approximate and simple expressions for the outage probability are provided. The results show that the proposed TSR protocol outperforms the conventional TSR protocol in terms of outage probability. Also, findings illustrate that applying the SWIPT technique in multi-destination underlay cognitive relay networks results in a unity diversity order. In contrast to conventional relaying networks, results show that the best location for the relay node in SWIPT relay networks is either near the source or the destinations, but not in between.

When NOMA Meets Multiuser Cognitive Radio: Opportunistic Cooperation and User Scheduling

This correspondence paper investigates a novel non-orthogonal multiple access (NOMA) assisted overlay spectrum sharing framework for multiuser cognitive radio networks toward an enhanced spectrum utilization. In particular, one secondary user is scheduled to help forward the primary signal and convey its own signals as well by applying the NOMA principle. A reliability-oriented secondary user scheduling (R-SUS) scheme is first proposed with a target at minimal primary and secondary outage probabilities. Then, a fairness-oriented secondary user scheduling (F-SUS) scheme is proposed, such that all the candidate secondary users have an equal opportunity to be scheduled for the cooperation. Expressions of primary and secondary outage probabilities are derived in closed form to evaluate the resultant network reliability performance. The results reveal that: 1) the proposed R-SUS and F-SUS schemes can achieve a full diversity order for the primary and secondary transmissions, and 2) although the F-SUS scheme enhances user fairness, it suffers a higher secondary outage probability compared with the R-SUS scheme.

A maximum throughput channel allocation protocol in multi-channel multi-user cognitive radio network

Channel allocation protocol plays a crucial role in ensuring that cognitive radio technology achieves more efficient radio resource utilization. To evaluate and compare the performance of different channel allocation protocols accurately, we need to model the cognitive radio system using various channel allocation protocols under one identical framework. Building such a framework is challenging. In this paper, we propose a method named distribution probability matrix (DPM) to describe the allocation results under different allocation protocols and a queueing analysis framework that uses DPM method. A framework that is capable of analyzing every individual secondary user (SU) in the system is developed. We also propose a maximum throughput (MT) channel allocation protocol aimed at achieving optimal throughput. Then we compare the MT protocol and existing protocols with the same objective using our proposed framework. The numerical results show that the MT protocol outperforms the existing protocols. Performance comparisons of different SUs are carried out to compare the protocols comprehensively. The convenience using our analytical framework of modeling different allocation protocols and evaluating the overall performances of the protocols is revealed throughout the analysis.

QoS‐driven power and time allocation scheme for spectrum leasing in overlay cognitive radio networks

The coexistence of a primary user (PU) with a secondary user (SU) is studied in an overlay cognitive radio network (CRN) which is proposed as an efficient technique to treat with the problem of spectrum underutilisation. A cooperative spectrum leasing process under the decode and forward protocol is considered among the PU and SU. Assuming users' specific Quality of Service requirements, a joint power and time allocation scheme for maximising the PU's cooperative effective capacity subject to minimum SU's and PU's effective capacities and maximum SU's instantaneous power transmission constraints is proposed. Its performance is close to the optimal one, that is computed using the exhaustive search scheme. A two-step solution is applied considering the fundamentals of convex optimisation theory and closed-form power and time expressions are extracted. As simulation results prove, the proposed scheme outperforms a less sophisticated scheme considering power allocation under constant time. Finally, the proposed algorithm is incorporated in a multi-user CRN scenario where efficient matchings among the PUs and the SUs are investigated targeting to the increase of PUs' cooperative performance applying the matching theory.

Distributed QoS Constraint based Resource Adaptation Strategy for Cognitive Radio Networks

This paper primarily investigates and addresses the optimal power adaptation strategy problem for multi-user cognitive radio network. The need to determine the optimal power transmission for the secondary user (SU) in a distributed network environment is challenging and important. This requires an efficient adaptive strategy with high convergence capability in order to meet up with the multi-users quality of service (QoS) requirements in a cognitive network and ultimately ensure more efficient resource utilization amongst users. In this paper, a distributed QoS contraint scheme which considered both the transmission power and outage probability has been proposed to maximize the network performance and to control the SUs transmission power. Firstly, the QoS constraint optimization problem for distributed secondary users is formulated and solved in order to adapt with the network dynamics of the cognitive networks. Subsequently, the solution has been used to dynamically adjust the radio power of the SUs to conform with the stringent network constraint and ensures coexistence amongst the users. More importantly, the simulation result shows that the scheme increases the overall network utility when compared to the scheme without adaptation.