Maximizing System Throughput by Cooperative Sensing in Cognitive Radio Networks

Abstract

Cognitive Radio Networks allow unlicensed users to opportunistically access the licensed spectrum without causing disruptive interference to the primary users (PUs). One of the main challenges in CRNs is the ability to detect PU transmissions. Recent works have suggested the use of secondary user (SU) cooperation over individual sensing to improve sensing accuracy. In this paper, we consider a CRN consisting of a single PU and multiple SUs to study the problem of maximizing the total expected system throughput. We propose a Bayesian decision rule based algorithm to solve the problem optimally with a constant time complexity. To prioritize PU transmissions, we re-formulate the throughput maximization problem by adding a constraint on the PU throughput. The constrained optimization problem is shown to be NP-hard and solved via a greedy algorithm with pseudo-polynomial time complexity that achieves strictly greater than 1/2 of the optimal solution. We also investigate the case for which a constraint is put on the sensing time overhead, which limits the number of SUs that can participate in cooperative sensing. We reveal that the system throughput is monotonic over the number of SUs chosen for sensing. We illustrate the efficacy of the performance of our algorithms via a numerical investigation.

Energy Efficient Resource Allocation in EH-Enabled CR Networks for IoT

“…Each device does local spectrum sensing by a low complex Energy Detector (ED) concerning the presence of PUs. We assume that the spectrum sensing results of users are independent [23] and each user is permitted to sense any number of sub-channels. The local spectrum sensing results are sent to the FC located in the AP.…”

“…(2)). The length of the control slot is called the sensing overhead and is constant for all devices [23]. The fixed control slot period is devoted for CSS and reporting the optimization results from the AP in each slot.…”

“…In fact, among the users with unsatisfied minimum data rate, a channel is allocated to the user that has the maximum rate on that channel. Then, for the remaining channels, we search over all the users to find a favorite user to allocate each channel (lines [15][16][17][18][19][20][21][22][23]. As shown in the algorithm, the favorite userk for channel n is one that achieves the maximum increase in the objective function of (10).…”

DISCERN: Cooperative whitespace scanning in practical environments

“…The problem of selecting a subset of SUs for sensing is challenging because of multiple factors: (i) Multiple PUs such as femtocells, mobile cellular sites, and microphones can be present in the area that are operating on the same channel; (ii) Statistical models do not hold well in practical environments due to obstacles and multipath effects leading to different SUs having different sensing accuracies that are difficult to estimate [3]; and, (iii) Different SUs have different sensing accuracy. However, all the previous works related to the problem of selecting the best SUs for cooperative sensing either implicitly assume that there is only a single PU in the network [10], [11], [12], [6], [13], [14], [15] or they neglect presence of obstacles by assuming that statistical models of signal transmission hold [14] or they simplistically assume that every SU has identical accuracy [10], [11] or they assume that the probability of detection and probability of false positive of SUs is known beforehand [15].…”

Queues management of secondary users in a Cognitive Radio Network

“…In a cellular network, the scheduler is installed on the Base Station (BS), which acts as an interface between the SUs and network services [15,6]. While in the distributed case, such a scheduler is implemented on each SU, and to have an overview, the SU cooperate with each other to exchange network settings [7].…”