Multiuser Cognitive Networks Research Articles

A generalized order user selection scheme for cognitive networks with imperfect channel estimation and multiple primary users

In this paper, we study the performance of multiuser cognitive networks with multiple primary users and imperfect channel estimation. Two scenarios are considered: primary users with the same spectrum band and primary users with orthogonal spectrum bands. The utilized generalized order user selection scheme is efficient in situations where a user other than the best user (user of best channel) is erroneously selected by the scheduling unit for conducting its communication as an imperfect channel estimation or an outdated channel information condition. In this scheme, the secondary user with the second or even the Nth best signal-to-noise ratio (SNR) is assigned the system resources by a scheduler. In our paper, closed-form expressions are derived for the outage probability, average symbol error probability (ASEP), and ergodic channel capacity of the secondary system assuming imperfectly estimated Rayleigh fading channels. Furthermore, closed-form expressions are derived for the outage probability, considering the interference from a primary transmitter. Also, to get more insights about the system performance, the system is studied at the high SNR regime where the diversity order and coding gain are derived and analyzed. The achieved analytical and asymptotic results are verified by Monte-Carlo simulations. The main results illustrate that the number of primary users affects the performance of the secondary system through affecting only the coding gain and not the diversity order. Results show that in the case where the primary users use the same spectrum band, the system performance enhances as the number of primary users decreases and degrades as the number of primary users increases. In contrast, when the primary users use orthogonal spectrum bands, findings illustrate that the system performance degrades as the number of primary users decreases and enhances as the number of primary users increases. Also, results show that a zero diversity gain is achieved by the system and a noise floor appears in the results when the secondary users channels are imperfectly estimated.

Parallel Cooperative Spectrum Sensing Based on Genetic Algorithm for Heterogeneous Multi-channel Cognitive Network

To sense the spectrum efficiently in heterogeneous multi-channel and multi-user cognitive network, a parallel cooperative spectrum sensing strategy based on genetic algorithm is proposed. It defines the expression of system utility considering the transmission gain and sensing overhead, and protects the primary user network by introducing the penalty function. To obtain the largest system utility, it uses genetic algorithm to obtain the optimal assignment between multiple channels and multiple secondary users. The simulation results show that the proposed strategy can obtain higher system utility and require less cooperative secondary users than improved iterative Hungarian algorithm based strategy, greedy based strategy and random cooperative sensing strategy.

Multiuser Cognitive Networks with Nth-Best User Selection and Imperfect Channel Estimation

The Nth-best user selection scheme is efficient in outdated channel information conditions where the user that was the best at the selection time instant could not be the best at the transmission time instant. Also, this scheme is useful when the scheduling unit fails in error in selecting the best user among all the available users. Furthermore, the Nth-best user selection scheme is efficient in situations where, while the best user is waiting to be scheduled by a certain base station or a scheduling unit, it gets scheduled by other unit. In the proposed scheme, the secondary user with the Nth-best end-to-end signal-to-noise ratio (SNR) is scheduled the system resources. In our paper, closed-form expressions are derived for the outage probability, average symbol error probability, and ergodic channel capacity assuming imperfect estimation of channel state information and Rayleigh fading channels. Furthermore, to further analyze the system performance, the system is studied at the high SNR regime. The derived analytical and asymptotic expressions are verified by Monte-Carlo simulations. Main results illustrate that the diversity order of the studied multiuser cognitive Nth-best user selection network is the same as its non-cognitive counterpart. Also, findings show that with perfect channel estimation of secondary users, the diversity order of the system linearly increases with decreasing the order of the scheduled user, and vice versa, whereas a zero diversity gain is achieved by the system and a noise floor appears in the results when the channels of secondary users are imperfectly estimated assuming constant estimation error variance. Finally, results illustrate that the imperfect estimation of the secondary cell-primary cell channel affects only the coding gain of the system without affecting the diversity order.

Joint optimization of spectrum sensing and accessing in multiuser multiple‐input single‐output cognitive networks

AbstractIn this paper, a joint spectrum sensing and accessing optimization framework for a multiuser cognitive network is proposed to significantly improve spectrum efficiency. For such a cognitive network, there are two important and limited resources that should be distributed in a comprehensive manner, namely feedback bits and time duration. First, regarding the feedback bits, there are two components: sensing component (used to convey various users' sensing results) and accessing component (used to feedback channel state information). A large sensing component can support more users to perform cooperative sensing, which results in high sensing precision. However, a large accessing component is preferred as well, as it has a direct impact on the performance in the multiuser cognitive network when multi‐antenna technique, such as zero‐forcing beamforming, is utilized. Second, the tradeoff of sensing and accessing duration in a transmission interval needs to be determined, so that the sum transmission rate is optimized while satisfying the interference constraint. In addition, the aforementioned two resources are interrelated and inversive under some conditions. Specifically, sensing time can be saved by utilizing more sensing feedback bits for a given performance objective. Hence, the resources should be allocation in a jointly manner. Based on the joint optimization framework and the intrinsic relationship between the two resources, we propose two joint resource allocation schemes by maximizing the average sum transmission rate in a multiuser multi‐antenna cognitive network. Simulation results show that, by adopting the joint resource allocation schemes, obvious performance gain can be obtained over the traditional fixed strategies.Copyright © 2014 John Wiley & Sons, Ltd.

Coalitional Power Control and Time Allocation for Multiuser Cognitive Networks

Coalitional Power Control and Time Allocation for Multiuser Cognitive Networks

Cross-Layer Minimum-Delay Scheduling and Maximum-Throughput Resource Allocation for Multiuser Cognitive Networks

A cognitive network is considered that consists of a base station (BS) communicating with multiple primary and secondary users. Each secondary user can access only one of the orthogonal primary channels. A model is considered in which the primary users can tolerate a certain average delay. A special case is also considered in which the primary users do not suffer from any delay. A novel cross-layer scheme is proposed in which the BS performs successive interference cancellation and thus a secondary user can coexist with an active primary user without adversely affecting its transmission. A scheduling algorithm is proposed that minimizes the average packet delay of the secondary user under constraints on the average power transmitted by the secondary user and the average packet delay of the primary user. A resource allocation algorithm is also proposed to assign the secondary users' channels such that the total throughput of the network is maximized. Our results indicate that the network throughput increases significantly by increasing the number of transmitted packets of the secondary users and/or by allowing a small delay for the primary user packets.

Security Spectrum Auction Framework for Cognitive Radio Networks

Spectrum auction is an effective way to improve spectrum efficiency in cognitive networks. All traditional auction framework such as VCG price auction framework and second price auction framework face security risks. Collusion is the most important risk in multi-user cognitive networks. In this paper, we propose a security mechanism for the multi-user cognitive spectrum auction networks. This security auction framework is based on position information. We use this security spectrum auction framework to increase the total system revenue and prevent collusion. The simulation results show that the security spectrum auction framework can greatly improve spectrum efficiency. Besides, the auction process will be safer and fairer. In particular, when users are uniformly distributed, the proposed auction framework will obtain more seller revenue.

Rate Maximization and Beamforming Design for Relay-Aided Multiuser Cognitive Networks

This paper studies the rate maximization problem and designs the beamforming strategy for relay-aided multiuser multiantenna cognitive radio (CR) network that coexists with a primary radio (PR) system via opportunistic spectrum sharing (OSS). The beamforming is characterized by two layers: 1) Cognitive beamforming (CB) is employed by CR base station (BS), CR relay station (RS), and secondary users (SUs) to mitigate the interference to and from the PR. 2) Joint CR-BS and CR-RS beamforming is used to maximize the weighted sum rate of the SUs. Since the optimization turns out to be nonconvex, we apply the classic Blahut-Arimoto algorithm and solve the original problem through an iterative approach. Interestingly, each iterative step involves only convex optimizations whose closed forms can be found. Moreover, we derive an upper bound for the sum rate to assist system analysis. Numerical results show that the proposed approach achieves higher performance, compared with the widely studied diagonally structured beamformer design.

Maximum Throughput Region of Multiuser Cognitive Access of Continuous Time Markovian Channels

The problem of cognitive access of multiple primary channels by multiple cognitive users is considered. The primary transmission on each channel is modeled by a continuous time Markov on-off process. Cognitive access of the primary channels is realized via channel sensing. Each cognitive user adopts a slotted transmission structure, senses one channel in each slot and makes the transmission decision based on the sensing outcome. The cognitive transmissions in each channel are subject to collision constraints that limit their interference to the primary users. The maximum throughput region of this multiuser cognitive network is characterized by establishing inner and outer bounds. Under tight collision constraints, the inner bound is obtained by a simple orthogonalized periodic sensing with memoryless access policy and its generalizations. The outer bound, on the other hand, is obtained by relating the sum throughput with the interference limits. It is shown that when collision constraints are tight, the outer and inner bounds match. This maximum throughput region result is further extended by a generalized periodic sensing scheme with a mechanism of timing sharing. Under general collision constraints, another outer bound is obtained via Whittle's relaxation and another inner bound obtained via Whittle's index sensing policy with memoryless access. Packet level simulations are used to validate the analytical performance prediction.