Relay-assisted Cognitive Radio Networks Research Articles

Joint Optimization for Over-the-Air Computation in AF Relay-Assisted Cognitive Radio Networks

Joint Optimization for Over-the-Air Computation in AF Relay-Assisted Cognitive Radio Networks

Throughput maximization for downlink DF MIMO relay based SWIPT cognitive radio networks

We address the throughput maximization problem for downlink transmission in decode-and-forward (DF) relay-assisted cognitive radio networks (CRNs) based on simultaneous wireless information and power transfer (SWIPT) capability. In this assumed network, the multiple-input multiple-output (MIMO) relay and secondary user (SU) equipment are designed to operate with the energy harvest from radio frequency (RF) signals as well as SWIPT capability. Also, the cognitive base station (CBS) communicates with SU just through a MIMO relay. Based on the considered network model, multiple combinatorial constraints in the main problem complicate the solution. So, this paper applies heuristic policies in the convex optimization framework to tackle this complexity. First, the throughput maximization problem is considered for the two sides of the relay separately. Second, we progress towards solving each side’s complicated problem optimally by recruiting subproblems-solving strategy. Finally, these optimum solutions are integrated by proposing a heuristic iterative power assignment algorithm in which combinatorial constraints are satisfied with low convergence time. The proposed algorithm performance is evaluated in comparison with benchmark algorithms via numerical outcomes in respect of optimality, convergence time, and compliance with constraints.

Multiple Description Coding for Enhancing Outage and Video Performance Over Relay-Assisted Cognitive Radio Networks

Multimedia content delivery, such as video transmission over wireless networks, imposes significant challenges include spectrum capacity and packet losses. The cognitive radio (CR) technology is developed to solve the spectrum issue, while multiple description coding (MDC) is one of the promising source coding techniques to alleviate packet loss problems and exploit the benefit of path diversity. The source information was split into several descriptions in MDC, then transmitted over a network with multiple paths. The quality of the received data increases with the number of descriptions received at the receiver. In this paper, the proposed system comprises of relay-assisted cognitive radio network using the MDC technique for video transmission. In the simulations, the outage performance of the MDC scheme over two networks, which were relay-assisted network and non-relay network, were compared. Then, the outage probability was used to estimate the video quality, peak signal to noise ratio (PSNR) of the received video. The results obtained show the benefits of the relay assisted networks by 9% improvement on average outage performance over the non-relay network. Furthermore, the video performance improved by an average of 9% in PSNR compared to the non-relay system.

Joint 3D trajectory and resource optimization for a UAV relay-assisted cognitive radio network

In this paper, we consider a new spectrum sharing scenario for a cognitive relay network, where a secondary unmanned aerial vehicle (UAV) relay receives information from the ground secondary base station (SBS) and transmits information to the ground secondary user (SU), coexisting with the primary users (PUs) at the same wireless frequency band. We investigate the optimization of the UAV relay's three-dimensional (3D) trajectory to improve the communication throughput performance of the secondary network subject to the interference constraints of the PUs. The information throughput maximization problem is studied by jointly optimizing the UAV relay's 3D trajectory and the transmit power of the SBS and the UAV, subject to the constraints on the velocity and elevation of the UAV relay, the maximum and average transmit power, and the information causality, as well as a set of interference temperature (IT) constraints. An efficient algorithm is proposed to solve the admittedly challenging non-convex problem by using the path discretization technique, the successive convex approximation technique and the alternating optimization method. Finally, simulation results are provided to show that our proposed design outperforms other benchmark schemes in terms of the throughput.

Censoring-Based Relay Transmission for Achieving Energy Efficiency in Cognitive Radio Networks

Energy efficiency (EE) is critical to achieve cooperative sensing and transmission in relay-assisted cognitive radio networks (CRNs) with limited battery capacity. This paper proposes an energy-efficient cooperative transmission strategy with combined censoring report and spatial diversity in cooperative process, namely, censoring-based relay transmission (CRT). Specifically, secondary relays (SRs) take part in cooperative sensing with differential censoring to reduce energy consumption, and the best SR assists secondary transmission to enhance communication quality in transmission stage and thus to improve secondary transmission EE. First, we derived generalized-form expressions for detection probability, reporting probability, sensing energy, and expected throughput for CRT. Second, we investigate a mean EE-oriented maximization nonconvex problem by joint optimizing sensing duration and power allocation for secondary users under secondary outage probability and sensing performance constraints. With the aid of Jensen’s inequality, an efficient cross-iteration algorithm with low complexity is proposed to obtain the suboptimal solutions, which is developed by golden segmentation search method. Finally, extensive simulations are conducted to evaluate the performance of CRT. The results show significant improvements of SUs’ EE compared with traditional noncooperation single cognitive transmission schemes, which demonstrate the benefits of our proposed cooperative strategy in conserving energy for secondary transmission.

On Residual Energy Maximization in Cognitive Relay Networks With Eavesdropping

This paper addresses a residual energy maximization problem on a two-hop decode-and-forward relay-assisted cognitive radio network operating in a time-slotted mode in the presence of an eavesdropper. In the first time slot, spectrum sensing (SS) and energy harvesting (EH) from the received signal of the primary user (PU) are done simultaneously by the secondary transmitter through power splitting mode. On the next time slot, the secondary transmitter either harvests energy or transmits its own data to the relay node based on the SS decision result. The relay node subsequently either forwards this data or harvests energy based on the activity followed by the secondary transmitter node. Considering both linear and EH models, an optimization nonlinear problem of residual energy maximization is explored under the constraints of sensing reliability of the PU, secondary outage probability, and secrecy outage probability. Closed-form expressions of the sensing time and secondary and relay transmit power are derived both in the presence and absence of an eavesdropper. The impact of eavesdropping on detection probability, false alarm probability, secondary outage probability, and residual energy are analyzed. Simulation results show that about $\sim$ 21.19% and $\sim$ 42.84% gain in total residual energy values are achieved compared to the existing works.

CSI Based Multiple Relay Selection and Transmit Power Saving Scheme for Underlay CRNs Using FRBS and Swarm Intelligence

In this article, a multiple relay selection (MRS) scheme for signal-to-noise ratio (SNR) enhancement is proposed for underlay relay-assisted cognitive radio networks (RCRNs). A secondary source-destination pair experiencing deep fading on direct path is assisted by amplify-and-forward (AF) relays in an underlay mode. In this energy-constrained scenario, the aim is to maximize the secondary network's end-to-end SNR through an intelligent power-saving method incorporated with MRS. In contrast to the prior relay selection (RS) schemes, the relay-selection factor is the difference of SNR of the source-relay link and corresponding relay-destination link for each relay along with its corresponding interference channel coefficient. The difference factor aims to achieve the SNR upper bound while performing minimum power amplification, eventually resulting in interference mitigation as well. The proposed algorithm has been implemented using Fuzzy Rule Based System (FRBS), Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) and their performance has been compared through simulations.

Performance analysis of relay subset selection schemes for underlay relay‐assisted CRNs

In underlay cognitive radio networks (CRNs), secondary users (SUs) are allowed to co-exist with the primary users at the cost of reduced transmit power in order to satisfy the stringent interference constraint. This obligation reduces the coverage area of the SUs and they have to involve relaying services for assistance. However, selecting the best combination of relays from the available options is more challenging in an energy-constrained environment, since it is affected not only by the interference constraint, but also the selected subset of relays must guarantee some quality-of-service (QoS) at the secondary destination also. This study is focused on an amplify-and-forward-based relay-assisted CRN operating in an underlay scenario and studies three relay subset selection or multiple relay selection schemes taking into consideration outgoing secondary and primary link qualities of each candidate relay. For each scheme, the closed-form expression for probability density function of signal-to-noise ratio at the secondary destination is derived followed by detailed analysis of two QoS metrics, i.e. outage probability and bit error rate. Finally, simulations have been performed to validate the analytical results.

Outage-Optimal Energy Harvesting Schemes in Relay-Assisted Cognitive Radio Networks

Outage-Optimal Energy Harvesting Schemes in Relay-Assisted Cognitive Radio Networks

Robust energy efficiency power allocation for relay-assisted uplink cognitive radio networks

In this paper, we investigate a worst-case robust power allocation scheme to improve energy efficiency (EE) for an amply-and-forward relaying uplink underlay OFDM cognitive radio system with imperfect channel situation information about the channel between primary user (PU) and secondary user (SU) and the channel between SU and corresponding relay. Specifically, a max–min problem is formulated to transform the original optimization problem into maximum EE on minimum throughout channel, and an epigraph problem is introduced to obtain analytical expressions of objective power allocation. Simulation results show that the proposed EE power allocation scheme is valid and effective in EE and robustness.

Transmit Power Minimization using Fuzzy Rule based System in Relay Assisted Cognitive Radio Networks

power minimization is one of the major research challenges in Relay-Assisted Cognitive Radio Networks. In this process, the transmit power of each individual relay is adjusted in such a way that the overall transmit power consumption at the relay network is minimized while satisfying the minimum Quality-of-Service (QoS) requirements of primary and secondary networks. In this paper, a similar constrained optimization problem is focused in which a secondary source-destination pair is assisted by a potential relay network having Cognitive Radio capabilities. A Fuzzy Rule Based System (FRBS) is proposed for intelligent relay selection such that total transmit power at the relay network is minimized while achieving the desired signal-to-noise ratio (SNR) at the destination and keeping the primary communication undisturbed. The effectiveness of the proposed scheme is highlighted through simulation results. KeywordsRadio Network, Underlay Spectrum Sharing, Cooperative Communication, Amplify-and-Forward, Fuzzy Rule Based System

Estimation of Distribution Algorithm for Resource Allocation in Green Cooperative Cognitive Radio Sensor Networks

Due to the rapid increase in the usage and demand of wireless sensor networks (WSN), the limited frequency spectrum available for WSN applications will be extremely crowded in the near future. More sensor devices also mean more recharging/replacement of batteries, which will cause significant impact on the global carbon footprint. In this paper, we propose a relay-assisted cognitive radio sensor network (CRSN) that allocates communication resources in an environmentally friendly manner. We use shared band amplify and forward relaying for cooperative communication in the proposed CRSN. We present a multi-objective optimization architecture for resource allocation in a green cooperative cognitive radio sensor network (GC-CRSN). The proposed multi-objective framework jointly performs relay assignment and power allocation in GC-CRSN, while optimizing two conflicting objectives. The first objective is to maximize the total throughput, and the second objective is to minimize the total transmission power of CRSN. The proposed relay assignment and power allocation problem is a non-convex mixed-integer non-linear optimization problem (NC-MINLP), which is generally non-deterministic polynomial-time (NP)-hard. We introduce a hybrid heuristic algorithm for this problem. The hybrid heuristic includes an estimation-of-distribution algorithm (EDA) for performing power allocation and iterative greedy schemes for constraint satisfaction and relay assignment. We analyze the throughput and power consumption tradeoff in GC-CRSN. A detailed analysis of the performance of the proposed algorithm is presented with the simulation results.

SNR Maximization through Relay Selection in Cognitive Radio Networks

In this study, we apply Artificial Bee Colony (ABC) optimization to the problem of relay selection and transmit power allocation to selected relays in non-regenerative Relay Assisted Cognitive Radio Networks (RCRNs). The objective of our proposed scheme is to maximize the Signal-to-Noise Ratio (SNR) at the secondary destination while keeping the interference offered to the primary user below a certain threshold level. Simulation results verify the effectiveness of the proposed algorithm.

Joint Resource Scheduling for Relay-Assisted Broadband Cognitive Radio Networks

In this paper, we study resource scheduling in a relay-assisted cognitive radio network with the orthogonal frequency division multiple access (OFDMA) scheme adopted to cope with the multi-user configuration. We discuss the optimization over the relay assignment, subcarrier allocation, per-node power control, and heterogenous quality-of-service (QoS) provisioning. The discrete characters of relay assignment and subcarrier allocation lead to a mixed integer nonlinear program (MINLP) whose computational complexity grows exponentially with the number of subcarriers. In an attempt to treat this complication, an asymptotically optimal solution based on the dual-analysis framework is proposed, for which we investigate the optimality of the dual method in the two scenarios: with and without subcarrier pairing. For both cases, it is shown that zero-duality-gap is achievable and the joint scheduling problem can be solved through a series of subproblems whose closed-form solutions are found. Moreover, for the scenario without subcarrier pairing we prove that the relaxation of allocation variables does not affect global optimality, i.e., even if the allocation variables are relaxed into continuous ones, binary variables are always retrievable as optimal solutions. Numerical examples are provided to corroborate the efficiency of the proposed approach.

A distributed method for relay selection in cooperative cognitive radio networks

We analyze the performance of a fully decentralized relay-selection method for the relay-assisted Cognitive Radio (CR) systems. The method requires no explicit communication among relay, assumes no prior knowledge of geometry. In particular, we assume that the frequencies chosen by the relay-assisted CR network overlapped with an active primary link. The proposed relay-selection scheme decreases the outage probability by considering both the channel scenario and the interference to the primary link compared with the non-cooperative transmission. Benefits of cooperative diversity are increased with the increased number of relays.

Joint power and channel allocation for energy-efficient performance of relay-assisted cognitive radio networks based on energy pricing

In a cooperative relay scenario with multiple spectrum bands, modelling the nodes of a network as energy sellers, a joint power and channel allocation scheme based on energy pricing is proposed for parallel-transmission to extend the lifetime of the cooperative network by considering variant transmit power and spectrum availability. The optimal power allocation solutions between the source node and the relay node are obtained by a dual-based optimal power schedule algorithm, and the end-to-end throughput of parallel-transmission is fulfilled with minimum system cost (MSC) criteria. The superiority of the MSC scheme over other schemes has been verified by quantitative results.