Joint distributed access point selection and power allocation in cognitive radio networks

Abstract

Spectrum management has been identified as a crucial step towards enabling the technology of the cognitive radio network (CRN). Most of the current works dealing with spectrum management in the CRN focus on a single task of the problem, e.g., spectrum sensing, spectrum decision, spectrum sharing or spectrum mobility. In this work, we argue that for certain network configurations, jointly performing several tasks of the spectrum management improves the spectrum efficiency. Specifically, we study the uplink resource management problem in a CRN where there exist multiple cognitive users (CUs) and access points (APs), with each AP operates on a set of nonoverlapping channels. The CUs, in order to maximize their uplink transmission rates, have to associate to a suitable AP (spectrum decision), and to share the channels belong to this AP with other CUs (spectrum sharing). These tasks are clearly interdependent, and the problem of how they should be carried out efficiently and distributedly is still open in the literature.In this work we formulate this joint spectrum decision and spectrum sharing problem into a non-cooperative game, in which the feasible strategy of a player contains a discrete variable and a continuous vector. The structure of the game is hence very different from most non-cooperative spectrum management game proposed in the literature. We provide characterization of the Nash Equilibrium (NE) of this game, and present a set of novel algorithms that allow the CUs to distributively and efficiently select the suitable AP and share the channels with other CUs. Finally, we study the properties of the proposed algorithms as well as their performance via extensive simulations.

Opportunistic distributed channel access for a dense wireless small‐cell zone

“…The threshold-strategy definition (5) states that a player transmits on channel k if the channel power gain h k i is the largest among all the channels and h k i is greater than a threshold h ith . As a consequence of the special form of threshold strategies, we can denote the strategy profile of the players by simply specifying their threshold vector s th := (h ith , i ∈ N ).…”

Joint base station selection and beamforming in uplink SIMO networks with per-user power constraints

“…Thus,wm can be interpreted as the downlink transmit beamformer of the virtual BS to user m, which contains the power information. Similarly, θm is the noise power at user m. Moreover, the downlink transmit power is constrained by (6), and the noise power by (5).…”

“…The popular strategy is to formulate the problem from the perspective of sparse beamforming, i.e., introducing some sparse constraints to the linear beamformers while optimizing the network performance in, e.g., spectrum efficiency [4][5][6], user fairness [7], and power efficiency [5,8,9], etc. However, most current works focus on the downlink transmission.…”

Optimal collaborative access point association in wireless networks

“…The decision is generally made by the client locally and selfishly, which has been analyzed through game theory in different network settings, e.g., wireless access networks [24] [7] [19], cognitive radio networks [11], and cellular networks with linear topologies (linear cellular networks) [15]. It has been shown that the selfish behavior does not necessarily converge to the optimal equilibrium [23].…”

“…Optimized decentralized association algorithms [14] [23] [11] try to achieve global optimum with local client measurements. Yet certain simplifications have been made in these pioneering studies, e.g., uniform throughput among all users or linear topology, which can hardly be extended to general network settings.…”