For the realization of green communications in cognitive radio ad hoc networks(CRAHNs), selfadaptive and efficient power allocation for secondary users(SUs) is essential. With the distributed and timevarying network topology, it needs to consider how to optimize the throughput and power consuming, avoid the interference to primary users(PUs) and other SUs, and pay attention to the convergence and fairness of the algorithm. In this study, this problem is modeled as a constraint optimization problem. Each SU would adjust its power and corresponding strategy with the goal of maximizing its throughput. By studying the interactions between SUs in power allocation and strategy selection, we introduce best-response dynamics game theory and prove the existence of Nash equilibrium(NE) point for performance analysis. We further design a fully distributed algorithm to make the SUs formulate their strategy based on their utility functions, the strategy and number of neighbors in local area. Compared with the water-filling(WF) algorithm, the proposed scheme can significantly increase convergent speed and average throughput, and decrease the power consuming of SUs.
This paper focuses on the energy efficient relay selection problem in a cooperative multi-relay network,aims to find the most energy efficient relay node for the source node while ensuring its minimum data rate requirement.The interaction between the source node and the relay nodes is modeled as a Vickrey auction game,when the source node broadcasts a cooperation request,the relay nodes compete for the cooperation,and the one with the minimum bid will be chosen which denotes the cost of the source node during the cooperation process,but it only needs to provide the minimum bid provided by the other relay nodes,which can encourage all the relay nodes to give the true bid.Besides,the minimum rate requirement of the source node will be ensured and the relay node taking part in the cooperation will gain some reward and the reward can be maximized by reinforcement learning(RL).
In this paper, the joint resource allocation （RA） problem with quality of service （QoS） provisioning in downlink heterogeneous cellular networks （HCN） is studied. To fully exploit the network capacity, the HCN is modeled as a K-tier cellular network where each tier＇s base stations （BSs） have different properties. However, deploying numbers of low power nodes （LPNs） which share the same frequency band with macrocell generates severe inter-cell interference. Enhancement of system capacity is restricted for inter-cell interference. Therefore, a feasible RA scheme has to be developed to fully exploit the resource efficiency. Under the constraint of inter-cell interference, we formulate the RA problem as a mixed integer programming problem. To solve the optimization problem we develop a two-stage solution. An integer subchannel assignment algorithm and Lagrangian-based power allocation algorithm are designed. In addition, the biasing factor is also considered and the caused influence on system capacity is evaluated. Simulation results show that the proposed algorithms achieve a good tradeoff between network capacity and interference. Moreover, the average network efficiency is highly improved and the outage probability is also decreased.
Various cognitive network technologies are developed rapidly. In the article, the power and spectrum allocation in multi-hop cognitive radio network （CRN） with linear topology is investigated. The overall goal is to minimize outage probability and promote spectrum utility, including total reward and fairness, while meeting the limits of total transmit power and interference threshold to primary user simultaneously. The problem is solved with convex optimization and artificial bee colony （ABC） algorithm jointly. Simulation shows that the proposed scheme not only minimizes outage probability, but also realizes a better use of spectrum.
Although the medium access control （MAC） signaling has been well-defined in the 3rd generation partnership project （3GPP） long term evolution （LTE） specifications, the scheduling algorithm crucial to guarantee QoS performance, still remains as open issues. In this article, a traffic-based queue-aware scheduling （TQS） algorithm is proposed for evolved nodeB＇s （eNB＇s） MAC scheduler in 3GPP LTE broadband wireless networks. The proposed TQS is divided into three sub-algorithms： firstly, the authors propose a traffic model construction （TMC） algorithm which can construct a discrete-time Markov-modulated Poisson process （dMMPP） to represent each flow. Secondly, a newly traffic state estimation （TSE） algorithm is designed to obtain the queue＇s analytical statistics. Thirdly, based on the derived results of TSE and the channel states, a scheduling action decision （SAD） algorithm is presented that can adaptively allocate bandwidth to flows by considering both queue states and spectrum efficiency. Simulation shows that the TMC and TSE algorithm can capture the fluctuation of traffic and queue accurately. Moreover, compared with a widely accepted traffic-based scheduling algorithm, the proposed TQS has better average queue length and overflow probability performance.
