Cognitive Radio Relay Networks Research Articles

Radio Resource Allocation Fairness in Cooperative Cognitive Radio Relay Networks

Radio Resource Allocation Fairness in Cooperative Cognitive Radio Relay Networks

Total Throughput Capacity Maximization in Cooperative Cognitive Radio Relay Networks

Total Throughput Capacity Maximization in Cooperative Cognitive Radio Relay Networks

Power Minimization in Dual-Hop Underlay Cooperative Cognitive Radio Relay Networks for Optimal Resource Allocation

In this paper, we investigate a multi-user one-way dual-hop multi-relay cognitive radio underlay spectrum sharing network. .The joint optimal resource allocation of the bandwidth and power allocation in underlay Cooperative Cognitive Radio Relay Network (CCRRN) was studied and a convex optimization analytical framework is presented. A combined optimal power and bandwidth allocation (COPBA) scheme for the minimization of the total network power and blocking probability of the call admission control stage of the CCRRN is investigated. Our goal is to jointly optimize the bandwidth and power allocation such that the total transmission power in the CCRRN is minimized without compromising the Quality of Service (QoS) demands of the secondary users (SUs) and the interference constraint thresholds of the primary users (PUs) in the primary networks. The mathematical model and convex optimization problem with the aim of minimizing the total transmission power of the CCRRN was formulated. The resulting optimization problem was solved in the standard computer based numerical optimization solver package CONOPT in MATLAB/TOMLAB environment. Simulation results obtained is compared the equal-bandwidth optimal power allocation scheme. Keywords—Radio resource allocations; Cognitive radio Networks; Convex optimization; Cooperative relay networks, Transmit power minimization

Secrecy performance of transmit antenna selection for underlay MIMO cognitive radio relay networks with energy harvesting

In this paper, the secrecy performance in a MIMO cognitive radio (CR) relay network with energy harvesting (EH) and transmit antenna selection/maximal ratio combining (TAS/MRC) is invstigated, where the DF relaying protocol and multiple colluding passive eavesdroppers are considered. To improve the security of wireless transmission, two antenna selection schemes are proposed, namely, the optimal transmit antenna selection (OTAS) scheme and suboptimal transmit antenna selection (STAS) scheme. For the purpose of comparison, the space-time transmission (STT) scheme is introduced as a baseline. The exact and asymptotic closed-form secrecy outage probability (SOP) expressions for OTAS, STAS and STT schemes are derived over Rayleigh fading channels. An extension of the TAS framework to an artificial noise (AN) aided MIMO network is further presented and an AN aided transmit antenna selection (AN-TAS) scheme is proposed, in which the unselected antennas at R are used to emit AN for interfering with Es. Numerical results show that the OTAS and STAS schemes perform better than STT scheme in terms of SOP. Meanwhile, the SOP of AN-TAS scheme is much smaller than that of OTAS, STAS and STT schemes in the high SNR region, indicating the benefit of applying AN in MIMO network.

Optimal precoders and power splitting factors in multiuser multiple‐input multiple‐output cognitive decode‐and‐forward relay systems with wireless energy harvesting

SummaryThis paper considers an underlay cognitive radio (CR) wireless network with the assistance of a two‐hop decode‐and‐forward (DF) relay node. Different from the conventional CR relay networks, in this paper, the relay node using power‐splitting (PS) techniques harvests wireless energy transmitted from the secondary base station (SBS) and, then, uses its harvested energy to forward the received signals to multiple secondary receive users (SRUs). Due to the highly mutual dependence of the transmission strategies at nodes on the system performance, the optimal design of signal processing at transceivers to efficiently handle the interference and energy constraints is crucial. This paper studies the jointly optimal design of the precoders, PS factors to maximize the minimum user rate under the transmit power, harvested energy, and interference power constraints. Owing to the nonlinearly and mutually coupled design matrix variables in both objective function and constraints, the joint design of the precoders and PS factors for the concerned problem is mathematically challenging. To tackle these difficulties, we devise the appropriate surrogate functions to recast the nonconvex optimization problem into the convex one. By using the minorization–maximization framework, an efficient iterative algorithm with provable convergence is developed by solving the convex optimization problem in each iteration. Numerical results are provided to evaluate the achievable user rate performance of the considered system and investigate the effects of the interference power thresholds at the primary receive users and the number of antennas at the SBS and the relay on the minimum achievable user rate.

Joint Buffer-Aided Hybrid-Duplex Relay Selection and Power Allocation for Secure Cognitive Networks With Double Deep Q-Network

This paper applies the reinforcement learning in the joint relay selection and power allocation in the secure cognitive radio (CR) relay network, where the data buffers and full-duplex jamming are applied at the relay nodes. Two cases are considered: maximizing the throughput with the delay and secrecy constraints, and maximizing the secrecy rate with the delay constraint, respectively. In both cases, the optimization relies on the buffer states, the interference to/from the primary user, and the constraints on the delay and/or secrecy. This makes it mathematically intractable to apply the traditional optimization methods. In this paper, the double deep Q-network (DDQN) is used to solve the above two optimization problems. We also apply the a-priori information in the CR network to improve the DDQN learning convergence. Simulation results show that the proposed scheme outperforms the traditional algorithm significantly.

Power Adaptation for Enhancing Spectral Efficiency and Energy Efficiency in Multi-Hop Full Duplex Cognitive Wireless Relay Networks

This work investigates the energy efficiency (EE) and the spectral efficiency (SE) performance of multi-hop full duplex cognitive relay networks (MH-FDCRNs) operating in the spectrum sharing mode, under the influence of interference from the primary source. We formulate three distinct optimization problems for finding the optimal power allocation (OPA) for the secondary nodes in MH-FDCRN: (i) EE optimization with minimum SE requirement; (ii) SE optimization with minimum EE requirement; and (iii) EE-SE trade-off optimization. The impact of residual self-interference (RSI) arising due to full duplex operation at the relay nodes and the inter relay interference (IRI) arising due to frequency re-use are considered. For the EE/SE optimization problems, we transform the original non-convex optimization problems to their convex forms by expressing the numerator of the objective function as the difference of concave functions, and by using parametric transformation. For the EE-SE trade-off optimization problem, we first transform the original multi objective optimization problem into a single objective form; and then into its convex form by introducing an auxiliary variable. Computationally efficient algorithms are proposed to solve the considered problems. The convergence properties of the proposed algorithms are established through mathematical analysis as well as through computer simulation studies. We prove that the points of convergence of the proposed iterative algorithms are the Karush-Kuhn-Tucker (KKT) points of the initial non-convex problems. With the help of detailed numerical results, the best trade-off among EE and SE can be achieved by proper selection of priority factor, compared to the individual optimization approaches.

Energy efficient resource allocation based on relay selection and subcarrier pairing with channel uncertainty in cognitive radio network

More and more mobile devices join in the wireless networks, it has become a challenging problem how to mitigating the interferences and improving the energy efficiency in dynamic wireless communication environment. In this paper, a robust resource allocation algorithm is proposed to maximize the energy efficiency (EE) in a multi-carrier decode-and-forward (DF) cognitive radio(CR) relay network, which subjects to multiple constraints in aspects of relay transmit power, signal to noise ratio threshold, subcarrier pairing, relay selection and the primary user’s minimum transmit rate requirement. The original optimization problem is non-convex with consideration of the channel uncertainty, and it is the mixed binary integer programming problem. Subsequently, we use the worst-case method to deal with channel uncertainty and convert the original non-convex problem into a quasi-concave problem by the Dinkelbach method. We solve the NP-hard problem in two steps: fixing the subcarriers to get the optimal relay selection and allocating the subcarriers according to the optimal relay selection. By using the method of dual decomposition, we get the approximate optimal solution of the problem. Simulation results show that the proposed joint resource allocation scheme guarantees the desired service quality requirements, and the energy-efficiency obtained is improved comparing with some existing works. In addition, the proposed resource allocation algorithm shows better convergence performance under different topologies, which validates the resource allocation scheme has good scalability.

Effective Capacity in Wireless Networks: A Comprehensive Survey

Low latency applications, such as multimedia communications, autonomous vehicles, and Tactile Internet are the emerging applications for next-generation wireless networks, such as 5th generation (5G) mobile networks. Existing physical-layer channel models, however, do not explicitly consider quality-of-service (QoS) aware related parameters under specific delay constraints. To investigate the performance of low-latency applications in future networks, a new mathematical framework is needed. Effective capacity (EC), which is a link-layer channel model with QoS-awareness, can be used to investigate the performance of wireless networks under certain statistical delay constraints. In this paper, we provide a comprehensive survey on existing works, that use the EC model in various wireless networks. We summarize the work related to EC for different networks, such as cognitive radio networks (CRNs), cellular networks, relay networks, adhoc networks, and mesh networks. We explore five case studies encompassing EC operation with different design and architectural requirements. We survey various delay-sensitive applications, such as voice and video with their EC analysis under certain delay constraints. We finally present the future research directions with open issues covering EC maximization.

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.

An enhanced sum rate in the cluster based cognitive radio relay network using the sequential approach for the future Internet of Things

The cognitive radio relay plays a vital role in cognitive radio networking (CRN), as it can improve the cognitive sum rate, extend the coverage, and improve the spectral efficiency. However, cognitive relay aided CRNs cannot obtain a maximal sum rate, when the existing sensing approach is applied to a CRN. In this paper, we present an enhanced sum rate in the cluster based cognitive radio relay network utilizing a reporting framework in the sequential approach. In this approach a secondary user (SU) extends its sensing time until right before the beginning of its reporting time slot by utilizing the reporting framework. Secondly all the individual measurement results from each relay aided SU are passed on to the corresponding cluster head (CH) through a noisy reporting channel, while the CH with a soft-fusion report is forwarded to the fusion center that provides the final decision using the n-out-of-k-rule. With such extended sensing intervals and amplified reporting, a better sensing performance can be obtained than with a conventional non-sequential approach, therefore making it applicable for the future Internet of Things. In addition, the sum rate of the primary network and CCRRN are also investigated for the utilization reporting framework in the sequential approach with a relay using the n-out-of-k rule. By simulation, we show that the proposed sequential approach with a relay (Lemma 2) provides a significant sum rate gain compared to the conventional non-sequential approach with no relay (Lemma 1) under any condition.

Cognitive Bidirectional Buffer-Aided DF Relay Networks: Protocol design and Power Allocation

In this paper, we consider bidirectional decode-and-forward buffer-aided relay selection and transmission power allocation schemes for underlay cognitive radio relay networks. First, a low complexity delay-constrained bidirectional relaying protocol is proposed. The proposed protocol maximizes the single-hop normalized sum of the primary network (PN) and secondary network (SN) rates and controls the maximum packet delay caused by physical layer buffering at relays. Second, optimal transmission power expressions that maximize the single-hop normalized sum rate are derived for each possible transmission mode. Simulation results are provided to evaluate the performance of the proposed relaying protocol and transmission power allocation scheme and compare their performance with that of the optimal scenario. Additionally, the impacts of several system parameters including maximum buffer size, interference threshold, maximum packet delay and number of relays on the network performance are also investigated. The results reveal that the proposed bidirectional relaying protocol and antenna transmission power allocation schemes introduce a satisfactory performance with much lower complexity compared to the optimal relay selection and power allocation schemes and provide an application dependent delay-controlling mechanism. It is also found that the network performance degrades as the delay constraint is more restricted until it matches the performance of conventional unbuffered relaying with delay constraints of three. Additionally, findings show that using buffer-aided relaying significantly enhances the SN performance while slightly weakens the performance of the PN.

MIMO Cooperative Cognitive Radio Relay Networks With Uniquely Factorable Signal Transmission

In this paper, we consider a cooperative cognitive radio relay network (CCRRN), in which there is a pair of primary transmitter (PT) and primary receiver (PR) with each having a single antenna, a secondary transmitter (ST) equipped with two antennas and a secondary receiver (SR) having a single antenna. We assume that ST operates in a half-duplex mode with an amplify-and-forward cooperative transmission scheme adopted. For such a system, a novel transmission scheme for CCRRN based on uniquely-factorable constellation pair is proposed, which enables ST to transmit its own information to SR without knowing its channel links to PR and SR while assisting PT transmission. It is shown that one of the significant advantages of our proposed new scheme is that, in a noise-free case, PR and SR are able to decode their own signals uniquely and that, in a Gaussian noise case, they are still able to reliably decode their own signals with full diversity. In addition, a tight upper bound of average pairwise error probability for the maximum-likelihood detector is derived, showing that full diversity is achieved. With this, an optimal power allocation strategy is proposed that minimizes the worst-case upper bound.

A Unified Power-Allocation Framework for Bidirectional Cognitive Radio Communication

In this paper, we consider the power-allocation problem in orthogonal frequency-division multiplexing (OFDM)-based bidirectional cognitive radio (CR) relay networks. A joint optimization under the amplify-and-forward (AF) relaying protocol is formulated to maximize the throughput of secondary users (SUs) via power loading over different subcarriers at the three (two transceivers and a relay) nodes. For compatibility with practical systems, separate power constraints are considered at each transmitting node. Furthermore, to provide high-level protection to primary users (PUs) in underlay CR transmission, per-subcarrier-based interference constraints are assumed. For tractability of the solution, we adopt a decomposition framework where the joint power optimization (JPO) is split into two subproblems. Later, each subproblem is solved by exploiting the convex optimization techniques. Moreover, a low-complexity suboptimal solution of the joint power allocation at all nodes is also proposed. Simulation results are presented to validate the effectiveness of the proposed schemes.

Erratum: Efficient signal detection for cognitive radio relay networks under imperfect channel estimation

Erratum: Efficient signal detection for cognitive radio relay networks under imperfect channel estimation

Efficient signal detection for cognitive radio relay networks under imperfect channel estimation

AbstractAssuming imperfect channel knowledge at the destination, we propose an improved detector for an underlay cognitive (secondary) amplify‐and‐forward wireless relay network coexisting with a primary transmission system. At first, we adjust the amplify‐and‐forward relay amplification factors by taking into account the interference temperature constraint at the primary receiver. In a second step, for the case of coherent distributed space‐time coding at the relays and maximum a posteriori signal detection at the destination, we propose a modified detector formulation. The proposed scheme reduces the impact of channel uncertainty on the receiver performance by appropriately exploiting the statistics of the channel estimation errors. Furthermore, it takes into account the interference level limitation imposed by the primary transmission system on the relays. Through some numerical results, we show that the proposed detector reduces significantly the required power level to achieve a target performance at the destination, which is of special interest for underlay cognitive radio systems. This way, the proposed receiver allows a reduction of the required protection area around the primary transmitter, leading to the increased of flexibility of the cognitive radio network design. Copyright © 2016 John Wiley & Sons, Ltd.

MICOR: A Market for Incentive-Compatible Cooperative Relay in Cognitive Radio Networks

We study a type of cooperative relay, which is among secondary users. In this type of cooperative relay, some secondary users may have more spectra than needed; therefore, they can use their spare spectrum to relay traffic for other secondary users. While this type of cooperative relay has been studied extensively, a crucial problem in this type of cooperative relay lies in the (lack of) incentives for the potential relay nodes. To address this problem, we propose that relay nodes should receive payments for their relay services so that they have incentives to provide such services. Specifically, we design a Market for Incentive-compatible COoperative Relay (MICOR). MICOR allows each potential relay node to submit a valuation function, which describes how much payment its relay service is worth. MICOR guarantees that, under a precondition called No Monopoly, it is a Dominant Strategy Equilibrium for all potential relay nodes to submit their true valuation functions. To establish this precondition, we also design an algorithm for channel assignment that achieves the optimal monopoly reduction with a very high probability. Results of extensive evaluations demonstrate that MICOR provides strong incentives to potential relay nodes, effectively reduces monopoly, and has good efficiency.

Throughput Maximization for Cognitive Radio Networks Using Active Cooperation and Superposition Coding

We propose a three-message superposition coding scheme in a cognitive radio relay network exploiting active cooperation between primary and secondary users. The primary user is motivated to cooperate by substantial benefits it can reap from this access scenario. Specifically, the time resource is split into three transmission phases. The first two phases are dedicated to primary communication, while the third phase is for the secondary's transmission. We formulate two throughput maximization problems for the secondary network subject to primary user rate constraints and per-node power constraints with respect to the time durations of primary transmission and the transmit power of the primary and the secondary users. The first throughput maximization problem assumes a partial power constraint such that the secondary power dedicated to primary cooperation, i.e. for the first two communication phases, is fixed apriori. In the second throughput maximization problem, a total power constraint is assumed over the three phases of communication. The two problems are difficult to solve analytically when the relaying channel gains are strictly greater than each other and strictly greater than the direct link channel gain. However, mathematically tractable lowerbound and upperbound solutions can be attained for the two problems. For both problems, by only using the lowerbound solution, we demonstrate significant throughput gains for both the primary and the secondary users through this active cooperation scheme. We find that most of the throughput gains come from minimizing the second phase transmission time since the secondary nodes assist the primary communication during this phase. Finally, we demonstrate the superiority of our proposed scheme compared to a number of reference schemes that include best relay selection, dual-hop routing, and an interference channel model.

Energy-Efficient Relay Selection Scheme for Physical Layer Security in Cognitive Radio Networks

Security is a critical issue in cognitive radio (CR) relay networks. Most previous work concentrates on maximizing secrecy capacity (SC) as a criterion to guarantee the security requirements in CR relay networks. However, under the requirement of “green” radio communication, the energy consumption is largely ignored. This paper proposes a relay selection scheme which jointly considers the best relay selection and dynamic power allocation in order to maximize SC and to minimize energy consumption. Moreover, we consider finite-state Markov channels and residual relay energy in the relay selection and power allocation process. Specifically, the formulation of the proposed relay selection and power allocation scheme is based on the restless bandit problem, which is solved by the primal-dual index heuristic algorithm. Additionally, the obtained optimal relay selection policy has an indexability property that dramatically reduces the computational complexity. Numerical results are presented to show that our proposed scheme has the maximum SC and minimum energy consumption compared to the existing ones.

Partial Relay Selection in Decode and Forward Cooperative Cognitive Radio Networks over Rayleigh Fading Channels

The performance of an partial relay selection on the decode-and-forward (DF) mode cognitive radio (CR) relay networks is studied, with some important factors, including the outage probability, the bit error ratio (BER), and the average channel capacity being analyzed. Different from the conventional relay selection schemes, the impact of spectrum sensing process as well as the spectrum utilization efficiency of primary users on the performance of DF-based CR relaying networks has been taken into consideration. In particular, the exact closed-form expressions for the figures of merit such as outage probability, BER, and average channel capacity over independent and identically distributed (i.i.d.) Rayleigh fading channels, have been derived in this paper. The validity of the proposed analysis is proven by simulation, which showed that the numerical results are consistent with the theoretical analysis in terms of the outage probability, the BER and the average channel capacity. It is also shown that the full spatial diversity order can always be obtained at the signal-to-noise ratio (SNR) range of [0dB, 15dB] in the presence of multiple potential relays.