Abstract

Cognitive radio networks (CRNs) are expected to improve spectrum utilization significantly by allowing secondary users (SUs) to opportunistically access the licensed spectrum of primary users (PUs). In an integrated network consisting of multiple heterogeneous CRNs, SUs with multiple interfaces may have to conduct inter-system or intra-system spectrum handoff due to the arrival of PUs or performance degradation on serving spectrum. In this case, designing an optimal spectrum handoff scheme which offers quality of service (QoS) guarantee and performance enhancement of the SUs is of particular importance. On the other hand, resource allocation strategy on target channel also plays an important role in affecting the transmission performance of handoff SUs. In this paper, we jointly design spectrum handoff and resource allocation strategy for handoff SUs in heterogeneously integrated CRNs. To achieve joint resource management among various CRNs, we propose a joint radio resource management architecture, based on which the proposed spectrum handoff and resource allocation scheme can be conducted. Jointly considering the transmission performance of the handoff SUs, we formulate the total energy efficiency of the SUs and design an optimization problem which maximizes the energy efficiency subject to spectrum handoff, QoS, and power constraints of the SUs. An iterative algorithm is proposed to solve the formulated nonlinear fractional optimization problem. Within each iteration, the optimization problem is transformed equivalently into two subproblems, i.e., power allocation subproblem of each SU-spectrum pair and spectrum handoff subproblem for all the SUs. The two subproblems are solved, respectively, through applying Lagrange dual method and the Kuhn-Munkres (K-M) algorithm. Numerical results demonstrate the efficiency of the proposed algorithm.

Highlights

  • Envisaged as a revolutionary technology to improve spectrum utilization, cognitive radio networks (CRNs) [1] have received considerable attentions from both academia and industry in recent years

  • To reduce the computation complexity of the proposed spectrum handoff and resource allocation scheme, we present a candidate spectrum selection scheme which selects the qualified spectrum among all the available spectrum based on the quality of service (QoS) requirements of interrupted secondary users (SUs); the proposed joint spectrum handoff and resource allocation scheme only applies to the candidate spectrum of the SUs

  • 7 Proposed joint optimization scheme: multiple SUs’ case. It can be seen from the previous section that in the case that the transmission of one SU is interrupted, the optimal spectrum handoff and power allocation problem can be solved through designing the optimal transmit power strategy and selecting the optimal subchannel, which offers the maximal energy efficiency

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Summary

Introduction

Envisaged as a revolutionary technology to improve spectrum utilization, cognitive radio networks (CRNs) [1] have received considerable attentions from both academia and industry in recent years. We jointly consider spectrum handoff and resource allocation problem of SUs in heterogeneous CRNs and design a joint optimal strategy for all the SUs in terms of handoff target channel selection and transmit power allocation. The authors in [12] study joint power and channel allocation problem in CRNs offering heterogeneous services and propose an optimal resource allocation scheme which maximizes the capacity of all the SUs. To meet the requirement on increasingly higher transmission rate, high transmit power is required, resulting in higher energy consumption at both user devices and base stations (BSs), which is highly undesired especially for energy-sensitive devices.

System model and proposed joint resource management architecture
Channel queuing model and interruption time analysis
Interruption delay constraint
Proposed joint optimization scheme: single SU case
Proposed joint optimization scheme: multiple SUs’ case
Iterative algorithm-based energy efficiency maximization
Locally optimal spectrum handoff and power allocation algorithm
Spectrum handoff subproblem
Simulation results and discussions
10 Conclusions
Full Text
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