Resource Allocation in Heterogeneous Cognitive Radio Network With Non-Orthogonal Multiple Access

Abstract

In this paper, we study resource allocation problems for a two-tier cognitive heterogeneous network in interweave spectrum sharing mode. Secondary users (SUs) in small cells (SCs) opportunistically access the licensed spectrum resources. Non-orthogonal multiple access (NOMA) is used to boost the number of accessible SUs sharing the limited and dynamic licensed spectrum holes. Practically, there exists a tradeoff: an SC can increase its instantaneous sum throughput by accessing more idle bandwidth, which creates higher liability due to the dynamics of licensed spectrum and contention among the multiple SCs. Aiming to maximize the sum throughput of second-tier SCs network, we formulate a mixed integer non-linear programming problem with the constraints of the available idle bandwidth, the successive interference cancellation complexity, the transmission power budget, and the minimum data requirements. To efficiently solve this problem, we decompose the original optimization problem into bandwidth resource allocation subproblem, SUs clustering subproblem, and power allocation subproblem. Based on the scale of SCs network and the activities of licensed spectrum, we introduce an optimal bandwidth configuration to maximize the average sum throughput of SCs. By analyzing the derivation of the achievable rate expression of a NOMA-enabled SU, we develop a novel SUs clustering algorithm which can improve the throughput of a cluster by grouping SUs with more distinctive channel conditions. With the results of SUs clustering, we propose power allocation within a NOMA cluster by using Karush-Kuhn-Tucker optimality conditions. Furthermore, we perform power allocation across NOMA clusters by using the difference of convex programming. The simulation results validate the performance of the proposed resource allocation algorithms.INDEX TERMS Cognitive heterogeneous network, interweave spectrum sharing mode, kmeans clustering, non-orthogonal multiple access (NOMA), resource allocation.Recently, the demands of the forthcoming fifth-generation (5G) communication system are expected to achieve 1,000 times the system capacity and 10 times the spectral efficiency of fourth-generation (4G) networks [1]. It is easy to understand that the conventional mobile network architecture is not able to support the increasing requirements of the communication system. To keep pace with the explosive growth of mobile data, one of the promising solutions is to deploy traffic offloading with the contemporary mobileThe associate editor coordinating the review of this manuscript and approving it for publication was Fuhui Zhou.

Fair Energy-Efficient Resource Allocation for Downlink NOMA Heterogeneous Networks

“…On the other hand, to address the resource allocation problems in multi-cell scenarios, recent studies have been introduced for the sum-rate [35]- [41] and energy efficiency maximization [15]- [18]. The reviews from [35]- [41] have analyzed the effect of power allocation and user scheduling problems on maximizing sum-rate in NOMA-HetNets. The authors in [35] investigated the power allocation problem using a Stackelberg game approach by adopting a distributed power allocation algorithm to achieve the Stackelberg equilibrium.…”

“…The collaborative communication method was introduced in [40] to improve system throughput and reduce inter-cell interference. The sorting based user-pairing approach was introduced in [41] for cognitive radio in NOMA-HetNets system. The KKT condition and difference of convex (DC) algorithm were used to obtain the optimal power within and across the subchannel, respectively.…”

Non-orthogonal Multiple Access: An Enabler for Massive Connectivity

“…The CR-NOMA networks are envisioned to be spectrally efficient with high system capacity. Some of the existing literature have investigated the performance of NOMA integrated with CR networks as in [53], [54].…”

Joint User Association and Power Allocation in Heterogeneous NOMA Networks With Imperfect CSI

“…But only one SC is considered. For a downlink NOMA-based heterogeneous cognitive network with one macrocell and multiple SCs [19], the sum throughput of multiple SCs was maximized by jointly optimizing bandwidth allocation, power allocation, and user clustering. In [20], a compressive sensing based spectrum allocation and power control algorithm was proposed to achieve the overall sum-rate maximization while considering both co-tier and cross-tier interferences.…”

“…Based on Dinkelbach's method [38], we can transform the objective function in problem (19) into the equivalent form…”