Fairness-Based Distributed Resource Allocation in Two-Tier Heterogeneous Networks

Abstract

Booming demand for mobile intelligent terminal equipment results in the exponential growth of the data flow in the fifth-generation mobile communication network. Small-cell networks have been considered as one of the possible solutions, where interference management and effective resource allocation are outstanding issues due to numerous small base stations. In this paper, the concept of cognitive smallcell networks is introduced, which combines technologies from cognitive radio with small cells. We aim to maximize the total throughput of the cognitive small-cell networks by jointly considering interference management, fairness-based resource allocation, average outage probability, and channel reuse radius. In order to make the optimization problem tractable, we decompose the original problem into three subproblems. First, we derive the average outage probability function of the network with respect to the spectrum sensing threshold. With a given outage probability threshold, the associated range of the channel reuse radius is obtained. In addition, to maximize the total throughput, a fairness-based distributed resource allocation (FDRA) algorithm is proposed to guarantee the fairness among cognitive small-cell base stations, which defines the satisfaction degree and the traffic requirement indicators to dynamically adjust the resource allocation process. Finally, considering the time-varying traffic load and different geographical environments, an improved FDRA (IFDRA) algorithm is proposed to further improve the throughput of the hot area. The simulation results demonstrate that the proposed FDRA algorithm and IFDRA algorithm could achieve a considerable performance improvement compared with schemes in the literature while providing better fairness among cognitive small-cell base stations. INDEX TERMS Cognitive small cell, resource allocation, channel reuse radius, fairness.

A Survey on Resource Allocation for 5G Heterogeneous Networks: Current Research, Future Trends, and Challenges

“…4, the key issues of RA in traditional HetNets (e.g., TDMA) are optimal power allocation (i.e., power control, RA), user association (i.e., the user uses which BS to communicate) and bandwidth allocation. The structure of this typical network is the simplest [59] Fairness [49], [63] Mean rate [51] Distributed algorithm [63] Water-filling [51], Match theory [56] Macro-small [53], [58], [63] allocate power [48]- [63] Secrecy rate [54] Centralized algorithm (others) Iteration algorithm [55], [58], [63] Macro-femto [56], [60]- [62] bandwidth [48], [58] EE [52], [55], [57], [58], [60]- [62] Imperfect CSI [59]- [61] Convex optimization [48], [52]- [54], [59]- [62] Macro-pico [50]- [52], [54], [55], [57] User association [49], [51], [53], [55] Throughput, rate [48]- ...…”

“…In [62], a security-aware EE RA was modeled as a FP problem for HetNets where the average packet delay, the average packet dropping probability, and the total available power consumption were considered. The authors in [63] studied the distributed RA to maximize the total throughput of the cognitive small cell networks by jointly considering interference management, fairness-based RA, average outage probability and channel reuse radius.…”

A Survey on Resource Allocation for 5G Heterogeneous Networks: Current Research, Future Trends and Challenges

“…4, the key issues of RA in traditional HetNets (e.g., TDMA) are optimal power allocation (i.e., power control, RA), user association (i.e., the user uses which BS to communicate) and bandwidth allocation. [59] Fairness [49], [63] Mean rate [51] Distributed algorithm [63] Water-filling [51], Match theory [56] Macro-small [53], [58], [63] allocate power [48]- [63] Secrecy rate [54] Centralized algorithm (others) Iteration algorithm [55], [58], [63] Macro-femto [56], [60]- [62] bandwidth [48], [58] EE [52], [55], [57], [58], [60]- [62] Imperfect CSI [59]- [61] Convex optimization [48], [52]- [54], [59]- [62] Macro-pico [50]- [52], [54], [55], [57] User association [49], [51], [53], [55] Throughput, rate [48]- [50],…”

Achieving Arbitrary Throughput–Fairness Trade-offs in the Inter-cell Interference Coordination with Fixed Transmit Power Problem

“…The authors of [28] proposed a distributed optimization scheme for joint user association and ICIC with the proportional fairness criterion in small cell deployments. A fair distributed resource allocation algorithm to achieve a high total throughput in heterogeneous networks was proposed in [29]. To ensure fairness, users in BSs that have low satisfaction degrees (ratio of number of channels currently allocated and number of required channels) and high traffic requirement levels are preferentially allocated channels.…”

“…Also, in [23] (respectively, [25]), an allocation algorithm was proposed to provide proportional fairness (respectively, max-min fairness), which corresponds to the case α = 1 (respectively, α = ∞), of the scheme proposed in our work. In contrast to [23], [25], [26], [27], [28] and [29], a modification of the α−fairness criterion is considered in this paper. Note that we consider all the values of α in [0, ∞), which correspond to different trade-offs between the total throughput and fairness.…”