Effective Scheduling in Infrastructure-Based Cognitive Radio Networks

Abstract

In this paper, we investigate a joint scheduling and power control for an infrastructure-based cognitive radio network (CRN) in coexistence with a cellular primary radio network (PRN). The PRN uses a set of licensed nonoverlapping orthogonal frequency channels for transmission. This set of channels is also accessed in an opportunistic manner by a set of cognitive radio base stations (CR-BSs) to support secondary users (SUs). The problem is formulated to maximize the spectrum utilization of SUs without causing excessive interference to active primary users (PUs) of the PRN. In addition, all the serviced SUs must meet a certain Quality of Service (QoS), such as satisfying a predefined signal to interference noise ratio (SINR). A centralized solution for joint scheduling and power control is derived to make the global accessing decision for all unserved SUs. With the assumption that the knowledge of all subscribers is available, a coordinator of the CRN can use the joint scheduling and power control algorithm to maximize the spectrum utilization of serviced SUs by solving a mixed-integer linear programming (MILP) with an NP-hard complexity. To avoid the NP-hard complexity, we propose a suboptimal heuristic greedy algorithm that can be obtained at a much lower complexity based on the coloring interference graph among unserved SUs effected by serviced SUs and active PUs. Its superior performance over the existing algorithms is demonstrated through simulations.

Traffic-Aware Channel Sensing Order in Dynamic Spectrum Access Networks

“…Then, we formally introduce our adoption of an ML PU state transition probability estimator and derive its CR bound 2 . Lastly, we show how the estimator and its bounds can be used to derive the sensing order confidence metric.…”

“…Similarly,s = 0 if (i) s = 1 and a mis-detection error occurred, or (ii) s = 0 and no false alarm error occurred. 2 For further discussions and derivations of traffic estimation in terms of sensing errors, please refer to [3]. 1) Throughput Loss Derivation: As discussed earlier, when PU traffic statistics are not perfectly known, the sensing order algorithm will not always produce the best sequence, and, as a result, incur DSA throughput loss.…”

“…The simulation results are shown under two different traffic parameter sets named 'Test 1' and 'Test 2'. We define the probability of PU presence vector as u = {u (1) , u (2) , · · · , u (M ) }. From the simulation results, we can first observe that the average sensing order confidence increases when the number of samples increases.…”

“…the process of ordering PU channels to sense to optimize a predefined objective (throughput, delay, etc.) of a DSA network [2]. For instance, in a centralized DSA network, PU channel selection and sensing order scheduling can be done by the base station (or a coordinator).…”

Joint optimization of detection threshold and resource allocation in infrastructure-based multi-band cognitive radio networks

“…We envisage that the PRN is underutilized and more revenue can be obtained by deploying CRNs, which opportunistically utilize the temporarily underutilized frequency sub-carriers available at the PRN and provide service to SUs. In this article, we focus on spectrum overlay and consider an infrastructurebased CRN, in which there exists a central entity or CR base station (CR-BS) that controls and coordinates the spectrum allocation and access of SUs [1,5].…”