Efficient resource allocation in cooperative cognitive radio networks: a coalitional game approach

In this paper, an innovative framework labeled as cooperative cognitive Cyber Physical Systems (CCCPSs) on the sea is developed to provide opportunistic channel access. Specifically, a two-phase frame structure is applied to let Secondary users (SUs) entirely utilize the transmission opportunities for a portion of time as the reward by cooperation with Primary users (PUs). Amplify-and-forward (AF) relaying mode is exploited in SU nodes, and Backward induction method based Stackelberg game is employed to achieve optimal determination of SU, power consumption and time portion for cooperation. Close-form solutions for the best access time portion β <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> to SU <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> as well as the power P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> for SU <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> are derived to realize the Nash Equilibrium. Simulation results validate the effectiveness of our proposed strategy.

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.

Aiming at efficient utilizing the licensed spectrum, in this paper, we develop an interference cancellation based transmission strategy (ICTS) for the cooperative cognitive radio networks (CRN) which will take advantage of the opportunities that arise during the primary automatic repeat requests (ARQ) and cancel the primary interference in both the primary initial transmission and retransmission slots. Simultaneously, the secondary system will cooperate with the primary system by supporting its outage quality-of-service (QoS) requirement to speedup the primary transmission and acquire more interference-free spectrum opportunities. Specifically, in the primary initial transmission slot, the secondary users (SU) will transmit in the underlay mode. In addition, SUs try to decode the primary signal to cancel the primary interference and overhear the primary ARQ signal. In the retransmission slot, SUs can cancel the primary interference directly when the primary signal is successfully decoded in the initial slot or utilize the primary information redundancy to cancel the interference. With statistical channel state information (CSI), SUs can calculate their interference cancellation probabilities in both slots and derive their average transmission rate. To maximize SUs' average transmission rate while meeting PUs outage QoS requirement, we formulate the optimization problem and efficiently allocate SUs transmit power. The closed-form expressions of the average transmission rates for both PUs and SUs are derived. Furthermore, SUs' upper bound symbol error probability (SEP) and PUs' SEP are derived. Simulation results demonstrate the performance superiority of our developed ICTS over conventional ARQ-based transmission strategies in terms of SUs' average throughput and PUs' outage probability and SEP.

SummaryIn cognitive radio networks (CRNs), resources available for use are usually very limited. This is generally because of the tight constraints by which the CRN operate. Of all the constraints, the most critical one is the level of permissible interference to the primary users. Attempts to mitigate the limiting effects of this constraint, thus achieving higher productivity, are a current research focus, and in this work cooperative diversity is investigated as a promising solution. Cooperative diversity has the capability to achieve diversity gain for wireless networks. In the work, therefore, the possibility of and mechanism for achieving greater utility for the CRN when cooperative diversity is incorporated are studied. To accomplish this, a resource allocation model is developed and analyzed for the heterogeneous, cooperative CRN. In the model, during cooperation, a best relay is selected to assist the secondary users that have poor channel conditions. Overall, the cooperation makes it feasible for virtually all the secondary users to improve their transmission rates while still causing minimal harm to the primary users. The results show a marked improvement in the resource allocation performance of the CRN when cooperation is used in contrast to when the CRN operates only by direct communication.

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.

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.

Resource allocation of cognitive relay networks under continuous transmission mechanism

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.

With the rapid development of wireless services and applications, the currently radio spectrum is becoming more crowded. How to accommodate more wireless services and applications within the limited radio spectrum becomes a big challenge faced by modern society. Cognitive radio (CR) is proposed as a promising technology to tackle this challenge by introducing secondary users (SUs) to opportunistically or concurrently access the spectrum allocated to primary users (PUs). Currently, there are two prevalent CR models: the spectrum sharing model and the opportunistic spectrum access model. In the spectrum sharing model, the SUs are allowed to coexist with the PUs as long as the interferences from SUs do not degrade the quality of service (QoS) of PUs to an unacceptable level. In the opportunistic spectrum access model, SUs are allowed to access the spectrum only if the PUs are detected to be inactive. These two models known as underlay and overlay schemes, respectively. This thesis studies a number of topics in CR networks under the framework of these two schemes. First, studied cognitive radio transmissions under QoS delay constraints. Initially, we focused on the concept: effective capacity for cognitive radio channels in order to identify the performance in the presence of QoS constraints. Both underlay and overlay schemes are studied taking into consideration the activity of primary users, and assuming the general case of channel fading as Gamma distribution. For this setting, we further proposed a selection criterion by which the cognitive radio network can choose the adequate mode of operation. Then, we studied the cognitive radio transmissions focusing on Rayleigh fading channel and assumed that no prior channel knowledge is available at the transmitter and the receiver. We investigated the performance of pilot-assisted transmission strategies. In particular, we analyzed the channel estimation using minimum mean-square-error (MMSE) estimation, and analyzed efficient resource allocation strategies. In both cases, power allocations and effective capacity optimization were obtained. Effective capacity and interference constraint were analyzed in both single-band and multi-band spectrum sensing settings. Finally, we studied optimal access probabilities for cognitive radio network using Markov model to achieve maximum throughput for both CR schemes.

This paper investigates the utility-based resource allocation for cooperative spectral sharing in orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) system. We study the multicarrier CR system with a primary user (PU) and multiple secondary users (SUs). By applying a cooperative spectrum leasing protocol, SUs can serve as amplify-and-forward (AF) relays to assist PU's transmission. Taking both the fairness and the throughput of SUs into account, we aim to maximize the total utility functions of SUs and formulate a joint optimization problem under constraints of the target rate of the primary system and the total power of the secondary system. The resource allocation involves rate arrangement, power control, subcarrier-relay selection, subcarrier allocation and subcarrier pairing. The proposed resource allocation policy is obtained by using the Lagrange dual approach. Simulation results demonstrate that the proposed scheme can make balance between throughput and fairness among SUs.

This letter proposes an efficient joint resource allocation scheme for OFDM-based cooperative cognitive radio networks (CRNs) under various practical limitations. Compared with those traditional approaches, which guarantee the transmission rates of cognitive users, the proposed scheme ensures that cognitive users are maintained in proportion to the predefined target rates. Numerical simulation shows that the proposed scheme can achieve a reasonable tradeoff between performance and computational complexity.

In cluster-based cognitive radio networks, secondary system uses available channels that are not used temporally by primary systems. In multi-channel operational environments, each available channel may have different wireless channel gain and primary activity so that achievable data rate to secondary users (SUs) and required sensing parameter value are channel dependent. SUs also have different energy saving requirements and data traffic demands. Therefore, based on measured channel conditions and user constraints, cluster head needs to decide which channel should be allocated to which SUs and to configure the optimum MAC frame structure for satisfying each SU’s service demands. Furthermore, optimization to provide proportional fairness among the cognitive secondary users in resource allocation in terms of energy consumption and data rate is very important. In this paper, a dynamic MAC frame configuration and optimal resource allocation scheme for multi-channel ad-hoc cognitive radio network is proposed. We formulate our dynamic resource allocation model as a constrained optimization problem with multi-objective functions using particle swarm optimization (PSO) algorithm. The proposed PSO scheme guarantees that the allocation captures the individual traffic and energy saving demands and maximizes the objectives functions simultaneously. Simulation results show the proposed scheme can successfully maximize the intended utility function and provide proportional fairness between SUs.

In this study, the problem of robust adaptive power control (PC) in an underlay cognitive radio network with multiple secondary users (SUs) and primary users (PUs) is considered. Due to the effects of uncertainties (i.e. estimation errors, delays), the optimal PC (resource allocation) cannot guarantee the quality of service of SUs and PUs under imperfect channel state information and interference power of PUs. A robust resource allocation problem is formulated to maximise sum throughput of SUs under individual power constraints and signal-to-interference-and-noise ratio constraints of SUs as well as interference temperature constraints of PUs, whereas channel uncertainties and interference uncertainties induced into the secondary system are modelled by multiplicative uncertainties. Under the worst-case approach, the problem is transformed into a geometric programming problem solved by Lagrange dual methods. The performance of the different algorithms and the impact of uncertainties are discussed according to several simulation results.

In this paper we consider a two level sensing in hybrid overlay/underlay non cooperative cognitive radio networks. For initial phase sensing energy detection is used to identify channel state (idle or busy) and then transmit power is set accordingly (overlay/underlay). During the transmission phase of secondary user (SU) a two level sensing is carried out. The first level detects interference and second level identifies source of interference using Direction of Arrival (DOA) detection schemes and underlay transmission is carried out if signal is found to be from Primary User(PU). This approach isolates the interference due to PU from the interference due to miss detection from another SU or from malicious user. Further due to two level detection the probability of detection is considerably increased. Based on the detection outcome channel occupancy is decided. The proposed multilevel sensing and power allocation algorithm is analyzed theoretically. The performance of proposed scheme is verified by simulation results.

We propose an adaptive resource allocation algorithm for a multiple-input multiple-output (MIMO) orthogonal frequency division multiple access (OFDMA) based downlink cognitive radio network (CRN). The CRN has multiple secondary users (SUs) coexisting with multiple primary users (PUs). The aim is to maximize the total data throughput of SUs while ensuring interference leakage to PUs is below a threshold and quality of services (QoS) for SUs such as data rates and bit error rate (BER) are met. The algorithm optimally allocates radio resources such as subcarrier, spatial beams, powers and bits to each user using an integer linear programming (ILP) framework. The performance of the algorithm has been evaluated using simulation results.