In this paper, we analyze a shared access network with a fixed primary node and randomly distributed secondary nodes whose distribution follows a Poisson point process (PPP). The secondaries use a random access protocol allowing them to access the channel with probabilities that depend on the queue size of the primary. Assuming a system with multipacket reception (MPR) receivers having bursty packet arrivals at the primary and saturation at the secondaries, our protocol can be tuned to alleviate congestion at the primary. We study the throughput of the secondary network and the primary average delay, as well as the impact of the secondary node access probability and transmit power. We formulate an optimization problem to maximize the throughput of the secondary network under delay constraints for the primary node, which in the case that no congestion control is performed has a closed form expression providing the optimal access probability. Our numerical results illustrate the impact of network operating parameters on the performance of the proposed priority-based shared access protocol.
@cite_26 , we analyzed the throughput of the secondary network when MPR capability is enabled in a cognitive network with congestion control on the primary user. Using the collision channel scenario, throughput optimization with deadline constraints on a single secondary user accessing a multi-channel system is studied in @cite_14 . The optimal stopping rule and power control strategy are provided in terms of closed-form expressions. In @cite_20 the joint scheduling and power control is considered in order to minimize the sum average secondary delay subject to interference constraints at the primary user. However, prior work has not studied the random access protocol design which takes into account both the throughput of the secondary network and the delay of the primary one.
@cite_14 @cite_26 @cite_20
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In a cognitive radio scenario, we consider a single secondary user (SU) accessing a multichannel system. The SU senses the channels sequentially to detect if a primary user (PU) is occupying the channels and stops its search to access a channel if it offers a significantly high throughput. The optimal stopping rule and power control problem is considered. The problem is formulated as an SU's throughput-maximization problem under power, interference, and packet delay constraints. We first show the effect of the optimal stopping rule on packet delay and then solve this optimization problem for both the overlay system, where the SU transmits only at the spectrum holes, and the underlay system, where tolerable interference (or tolerable collision probability) is allowed. We provide closed-form expressions for the optimal stopping rule and show that the optimal power control strategy for this multichannel problem is a modified waterfilling approach. We extend the work to a multi-SU scenario and show that when the number of SUs is large, the complexity of the solution becomes smaller than that of the single-SU case. We discuss the application of this problem in typical networks where packets simultaneously arrive and have the same departure deadline. We further propose an online adaptation policy to the optimal stopping rule that meets the packets' hard-deadline constraint and, at the same time, gives higher throughput than the offline policy. We explore the performance tradeoff between opportunistic and regulated access inherent in the design of multiuser cognitive radio networks. We consider a multichannel cognitive radio system with sensing limits at the secondary users and interference tolerance limits at the primary and secondary users. Our objective is to determine the optimal amount of spectrum sharing, i.e., the number of secondary users that maximizes the total deliverable throughput in the network.We begin with the case of perfect primary user detection and zero interference tolerance at each of the primary and secondary nodes. With identical primary and secondary traffic statistics, we find that the optimal fraction of licensed users lies between the two extremes of fully opportunistic and fully licensed operation and is equal to the traffic duty cycle. When the secondary users can vary their transmission probabilities based on the number of active primary users, we find that the optimal number of opportunistic users is equal to the average number of unoccupied channels. We then consider the more involved case of imperfect sensing and non-zero interference tolerance constraints. We provide numerical simulation results to study the tradeoff between licensing and autonomy and the impact of primary user sensing and interference tolerance on the deliverable throughput for two different subchannel selection strategies at the secondary users. In this paper, we study resource allocation for a multicarrier-based cognitive radio (CR) network. More specifically, we investigate the problem of secondary users’ energy-efficiency (EE) maximization problem under secondary total power and primary interference constraints. First, assuming cooperation among the secondary base stations (BSs), a centralized approach is considered to solve the EE optimization problem for the CR network where the primary and secondary users are using either orthogonal frequency-division multiplexing (OFDM) or filter bank based multicarrier (FBMC) modulations. We propose an alternating-based approach to solve the joint power-subcarrier allocation problem. More importantly, in the first place, subcarriers are allocated using a heuristic method for a given feasible power allocation. Then, we conservatively approximate the nonconvex power control problem and propose a joint successive convex approximation-Dinkelbach algorithm (SCADA) to efficiently obtain a solution to the nonconvex power control problem. The proposed algorithm is shown to converge to a solution that coincides with the stationary point of the original nonconvex power control subproblem. Moreover, we propose a dual decomposition-based decentralized version of the SCADA. Second, under the assumption of no cooperation among the secondary BSs, we propose a fully distributed power control algorithm from the perspective of game theory. The proposed algorithm is shown to converge to a Nash-equilibrium (NE) point. Moreover, we propose a sufficient condition that guarantees the uniqueness of the achieved NE. Extensive simulation analyses are further provided to highlight the advantages and demonstrate the efficiency of our proposed schemes. The performance of secondary user in underlay cognitive networks is analyzed by considering opportunistic selection of two-way full-duplex (FD)-based amplify-and-forward (AF) relays. The closed-form expressions for the distribution of end-to-end signal-to-noise ratio (SNR) are first derived, with independent and identically distributed (i.i.d.) Rayleigh fading channels assumed in each wireless link. After that, the theoretical closed-form expressions for various performance metrics, including the outage probability of each link, lower bounds of both the derived outage probability and the symbol error probability (SEP), and upper bound of the average outage channel capacity, are derived. Numerical results show that the performance of the secondary users can be substantially influenced by parameters such as the number of relays, the average SNR, the primary user’s tolerable interference threshold @math , and the power of residual self-interference in the FD relays after performance self-interference cancellation, etc. Furthermore, in the presence of multiple candidate relays, an optimum relay selection in terms of the outage probability, the SEP, the average channel capacity, and or the outage capacity can always be attained as long as the average SNR of each relaying link approaches @math .