Interference cancellation based transmission strategy using primary ARQ for cooperative CRNs

We investigate opportunistic cooperation between secondary (unlicensed) users and primary (licensed) users in cognitive radio networks. We consider two models for such cooperation. In the first model, called the Cooperative Relay Model, a secondary user cannot transmit its own data concurrently with a primary user. However, it can employ cooperative relaying in order to improve the effective transmission rate of the primary user. In the second model, called the Interference Model, a secondary user is allowed to transmit its data concurrently with a primary user. However, the secondary user can “cooperate” by deferring its transmissions when the primary user is busy. In both models, the secondary users must make intelligent cooperation decisions as they seek to maximize their own throughput subject to average power constraints. The decision options are different during idle and busy periods of the primary user, and the decisions in turn influence the durations of these periods according to a controllable infinite state Markov chain. Such problems can be formulated as constrained Markov decision problems, and conventional solution techniques require either extensive knowledge of the system dynamics or learning based approaches that suffer from large convergence times. However, using a generalized Lyapunov optimization technique, we design a novel greedy and online control algorithm that overcomes these challenges. Remarkably, this algorithm does not require any knowledge of the network arrival rates and is provably optimal.

In this paper, we propose a cooperative spectrum sharing protocol for cognitive radio networks (CRNs) which operate in underlay mode with an automatic repeat-request (ARQ)-based primary user (PU) including primary transmitter (PT) and primary receiver (PR) and a bidirectionally communicating secondary user (SU) which consists of the nodes S1 and S2. In ARQ-based PU, ACKnowledgement/Negative-ACKnowledgement (ACK/NACK) messages are sent from PR to PT and listened by S1 and S2 to control the PU's packet success. Si (i=1 or 2) alerts both PT and PR if its reception from PT is successed or failed and PR considers the link from Si or PT, respectively. PU transmits its packet by accessing the spectrum alone or by cooperating with SU or under an interference caused by SU. If PT transmits alone its packet successfully, in the next time slot it transmits a new packet in underlay mode during which SU access the spectrum simultaneously with a tolerable interference level to improves the PU's performance. Conversely, if PT fails to transmit alone its packet and at least one of the nodes S1 and S2 success to decode it, then S1 or S2 cooperates with PT attending as a relay to enhance the PU's throughput in the next time slot during which PT stays silent. If both of S1 and S2 fail to decode PT's packet, PT retransmits its packet in underlay mode. When the first transmission of PT's packet is failed in underlay mode, PT retransmits this packet alone. We derive analytical expressions for the throughputs of PU and SU and show that the proposed protocol significantly improves the PU's throughput compared to non-CR scheme while stabilizing the SU's throughput at high SNR values.

The cooperation among the secondary users or among the secondary users and the primary users have proven to bring many benefits to both primary and secondary users in cognitive radio networks. In this paper, cooperative relaying in cognitive radio networks is studied for rateless coding error-control mechanism. For a pair of primary transmitter and receiver, we find the region in which any secondary or cognitive user can cooperate with the primary user in a constructive way. A queueing theoretical model is employed to describe the underlying system parameters and to create a mathematical framework for the analysis. The throughput performance of the secondary users is maximized based on the input traffic of the primary users. Using the unique properties of rateless codes, a low-complexity relay selection mechanism is also introduced. Through the experimental analysis, we show that the proposed rateless-coding-based cooperative mechanism can perform similar to the more complicated and more costly selective-repeat automatic repeat request algorithm. The numerical analysis using the Rapid Analysis of Queueing Systems (RAQS) software package is provided to support the findings.

Motivated by the low utilization of the licensed spectrum across many frequency bands, sensing-based oppor- tunistic spectrum access has recently emerged as an alternative to the outdated exclusive spectrum access policy. Under this new paradigm, a secondary (unlicensed) user monitors a primary (licensed) frequency band for a given and opportunistically transmits if it does not detect any ongoing licensed operations. Evidently, selection of the parameters involves balanc- ing a tradeoff between the speed and the quality with which the secondary user senses the licensed band. With the average throughput as the performance criterion, we obtain the parameters so as to optimize the performance of the secondary user while providing the primary user with its desired level of interference protection. I. INTRODUCTION As evidenced by recent measurements, many frequency bands across the licensed spectrum are significantly under- utilized (1), (2). This finding suggests that the spectrum scarcity, as perceived today, is largely due to the inefficient fixed frequency allocations rather than the physical shortage of the spectrum and has led the regulatory bodies to consider the opportunistic access to the temporally/spatially unused licensed bands (a.k.a. the white spaces) as a means to improve the efficiency of spectrum usage. In the absence of cooperation or signalling between the primary licensee and the secondary users, spectrum availability for the opportunistic access may be determined by direct spectrum where the secondary user monitors a licensed band for a given sensing time and opportunistically transmits if it does not detect any ongoing licensed operations. This approach is particularly appealing due to its low deployment cost and its compatibility with legacy primary users and is being considered for inclusion in the upcoming IEEE 802.22 standard for opportunistic access to the TV spectrum (3). Due to their ability to autonomously detect and to react to the changes in the spectrum usage, secondary users equipped with the spectrum capability may be considered as a primitive form of the cognitive radio (4). Design of any scheme involves balancing a tradeoff between the quality and the speed of through an appropriate selection of the time. As we shall illustrate, in the context of spectrum sensing, may be fine- tuned to enhance the secondary users' perceived quality-of- service (QoS) as long as the regulatory constraint for the protection of the primary users against harmful interference is satisfied. In particular, we will obtain the optimum times at different stages of the spectrum to maximize the average throughput of the secondary user. In this paper, simple energy detection (a.k.a. radiometry) (5) is chosen as the underlying detection scheme. In general, when some information about the structure of the primary signal is available, ad hoc feature-detectors offer a better performance (6). We note, however, that the methodology employed in this paper may be applied to optimize different spectrum sensors once the quality is characterized in terms of the time. The remainder of this paper is organized as follows. The regulatory constraints on spectrum are described in the following section. Section 3 provides an overview of the energy-based spectrum sensing. The optimum times for different stages of the spectrum are derived in Section 4. Finally, this paper is concluded in Section 5.

In this paper, we address the performance improvement of cooperative cognitive radio networks (CCRNs) with secondary users exploiting multiple antennas in the multiple input multiple output (MIMO) scenario. In order to expand the region of achievable rate by enabling full-duplex communication between secondary users, we adopt simultaneous transmitting and receiving antennas for the secondary users. The link capacities of the proposed framework are analyzed under the assumption of full channel side information. It is shown through numerical evaluation that the proposed MIMO CCRN framework can provide a considerable performance gain over the conventional CCRN and conventional MIMO CCRN frameworks.

In this paper, we consider the resource allocation prob­ lem of cooperative spectrum sharing among multi­ ple primary users (PUs) and multiple secondary users (SUs), where the PUs lease their spectrum to the SUs in a time-division manner while the SUs in return help the data transmission of the PUs by relaying. The PU's strategy is to decide a proper fraction of spectrum leas­ ing time to SUs for maximizing its payoff, while the SU's strategy is to decide the proper power for relaying PU s' traffic, and for transmitting its own data so as to satisfy its payoff. We formulate the problem as a coali­ tional game with transferable utility (TU), and propose an algorithm to show the game converges to the solu­ tion concept (the core). We also show that the grand coalition formed by grouping all PUs and SUs is stable. Finally, we demonstrate in simulations the benefits of our proposed approach comparing to other approaches of power control.

This paper studies a cooperative cognitive radio network (CRN), where the primary system leases the licensed band to the secondary system for a fraction of time in exchange for the secondary user (SU) acting as a decode-and-forward relay. Our aim is to determine the cooperative resource allocation strategy among the primary user (PU) and SU so as to minimize both the total power consumption and the leased time. Considering the delay-sensitive applications in CRN, the proposed policy ensures delay-related quality-of-service (QoS) requirements of both PU and SU. To this end, a multi-objective optimization problem of power and time allocation is formulated based on the effective capacity. Via employing weighting method the optimization problem is transformed to a convex one and solved based on the lagrangian dual method. The proposed policy has been shown to be deterministic functions of delay QoS exponent and channel fading states. In particular, the power allocation is achieved by multi-level QoS based water-filling policy. Moreover, numerical results are provided to demonstrate the performance of the proposed resource allocation policy.

Turning foes to allies in cognitive radio networks

Spectrum sensing ranks amongst cognitive radio's most critical and demanding activities. For radio resource networks to use idle licensed bandwidth, secondary users need to identify the primary user's signal precisely. Cognitive radio networks (CRNs) have been proposed to improve the efficiency of spectrum use to meet the increasing demand for wireless bandwidth; they allow channel communication between secondary (unlicensed) and primary (licensed) users without interruption. A secondary user in a CRN must at all times track the presence of a primary user's signal to prevent the primary user (PU) from attempting to intervene whether the statistical variance of the PU data traffic would boost the prediction outcome provided by multiple current teaching algorithms in estimating the PU's average OFF time and the need for the best adaptive learning to predict accurate PU activities. The objective of the work has been modeled to identify the PUs signal. First, the data has been created via quadrature phase shift keying modulation and quadrature amplitude modulation by convolutional neural network (CNN). A dataset with different orthogonal frequency‐division (OFDM) multiple access signals is generated. Then cyclo‐stationary functions are extracted using the accumulation process fast Fourier Transform. This research aims to use glow worm swarm optimization (GWSO) to automatically check for the optimal design of CNNs without any manual work being required. Also, k‐Nearest Neighbors (kNN) replaces Softmax, as it outperforms conventional CNN architectures with noisy signals. A comparison of the proposed classifier with another machine learning classifier, namely support vector machine, Decision tree (DT), clearly shows that SVM and DT are outperformed by the improved convolution neural network‐glow worm swarm optimization (ICNN‐GWSO) classification. Results of simulation show that the improved scheme outperforms conventional spectrum sensing systems, both in fading and non‐fading settings, where efficiency is measured using measures such as chance, the total likelihood of error, and the ability to optimize data transfer possibilities.

In this paper, we propose a dynamic spectrum sharing protocol where a pair of secondary users and a pair of primary users bidirectionally communicate. A secondary relay equipped with multiple antennas is deployed to assist the secondary transmissions and improve the secondary access to the spectrum. We employ a new time division access so that no interference may exist between primary and secondary users. The proposed scheme is then compared to the axiomatic and simple scheme where the secondary users communicate with each other with the assistance of the relay in underlay mode. We study and compare the performances of the two schemes in terms of outage probability. An upper bound for the secondary outage probability of the proposed scheme is derived. Our simulation results prove that the proposed scheme significantly outperforms the underlay spectrum sharing while in both schemes, the primary outage probability is kept identical to the case where secondary users are absent.

Integrating simultaneous wireless information and power transfer (SWIPT) into cooperative cognitive radio networks (CCRN) can increase both energy and spectral efficiencies. In contrast to most of existing works that only consider relay transmission, this paper studies opportunistic relaying along with dynamic energy harvesting in SWIPT-based CCRN. We first propose a new framework that can unify the direct transmission, relay transmission and energy harvesting. Under the proposed framework, we then formulate the optimization problem of maximizing the ergodic capacity for secondary user (SU) while meeting the ergodic capacity for the primary user (PU) and average harvested energy for SU, by joint transmission mode selection and transmit power splitting ratio selection. The problem is nonconvex and we propose the optimal policy by using the dual Lagrangian method. Simulations validate the effectiveness of the proposed scheme.

In this paper, we exploit a novel setting for Cognitive Radio (CR) networks to enable multiple operators to involve secondary users (SUs) as cooperative relays for their primary users. In return, SUs get an opportunity to access spare channels for their own data transmission. Initially, we assume that the CR network supports payment transfer. Then, we formulate the system as a transferable utility coalitional game. We show that there is an operating point that maximizes the sum utility over all operators and SUs while providing each player a share such that no subset of operators and SUs has an incentive to break away from the grand coalition. Such operating points exist when the solution set of the game, the core, is nonempty. Subsequently, we examine an interesting scenario where there is no payment mechanism in the network. This scenario can be investigated by using a nontransferable utility coalitional game model. We show that there exists a joint action to make the core nonempty. A general method with exponential computational complexity to get such a joint action is discussed. Then, we relate the core of this game to a competitive equilibrium of an exchange economy setting under special situations. As a result, several available efficient centralized or distributed algorithms in economics can be employed to compute a member in the core. In a nutshell, this paper constitutes the design of new coalition based dynamics that could be used in future CR networks.

We investigate the feasibility of employing non-orthogonal multiple-access (NOMA) in cell-free massive multiple-input multiple-output (MIMO) operating with underlay spectrum-sharing. In our proposed system model, multiple clusters of NOMA-enabled secondary users (SUs) are concurrently served by geographically distributed secondary access-points (S-APs) via conjugate beamforming. The uplink channels are estimated locally at each S-AP via pilots sent by SUs. A set of orthogonal pilots is shared among the secondary and primary clusters to strike a balance between the throughput and training overhead, while enabling massive connectivity in primary and secondary systems. We derive the achievable rates of the secondary system by capturing the adverse effects of inter/intra-cluster interference, primary/secondary pilot contamination, imperfect successive interference cancellation (SIC) and partial channel state information (CSI). We propose a transmit power allocation policy for the secondary system to mitigate the detrimental impact of near-far effects by virtue of max-min fairness criterion. Through an achievable rate analysis, we reveal that although the number of concurrently served users can be substantially boosted by employing the proposed system model, the achievable rates are adversely affected due to detection uncertainties with imperfect SIC and statistical CSI at NOMA-enabled SUs.

In this paper an interference alignment and cancellation (IAC) based coexistent transmission and user scheduling strategy is proposed for infrastructured cognitive radio (CR) multiple-input multiple-output (MIMO) system with multiple secondary users (SUs). By exploiting cooperative capability at primary base station (PBS) and secondary base station (SBS), spatial signal processing procedure is designed. With the scheme, interference alignment (IA) is firstly employed to adjust multiple cognitive transmissions to lie in the subspace orthogonal to the one spanned by the primary signal. Then primary information is recovered at PBS without interference and fed to SBS via inter-BS collaboration. Finally, SBS subtracts the primary part from the received mixed signal and extracts the secondary information separately. With regard to SU scheduling transmission difference of various channel realizations is exploited to achieve multiuser diversity gain. According to the IAC based mechanism the priority of primary is guaranteed, and interference-free coexistence of both primary and secondary transmission is achieved. Moreover, effective utilization of spatial channel resource is realized compared with traditional IA based scheme. Theoretical analysis and simulation results show that the achievable data rate of secondary transmission is significantly enhanced.

In underlay cognitive radio (CR), a secondary user (SU) can transmit concurrently with a primary user (PU) provided that it does not cause excessive interference at the primary receiver (PRx). The interference constraint fundamentally changes how the SU transmits, and makes link adaptation in underlay CR systems different from that in conventional wireless systems. In this paper, we develop a novel, symbol error probability (SEP)-optimal transmit power adaptation policy for an underlay CR system that is subject to two practically motivated constraints, namely, a peak transmit power constraint and an interference outage probability constraint. For the optimal policy, we derive its SEP and a tight upper bound for MPSK and MQAM constellations when the links from the secondary transmitter (STx) to its receiver and to the PRx follow the versatile Nakagami-m fading model. We also characterize the impact of imperfectly estimating the STx-PRx link on the SEP and the interference. Extensive simulation results are presented to validate the analysis and evaluate the impact of the constraints, fading parameters, and imperfect estimates.