We propose a four-stage hierarchical resource allocation scheme for the downlink of a large-scale small-cell network in the context of orthogonal frequency-division multiple access (OFDMA). Since interference limits the capabilities of such networks, resource allocation and interference management are crucial. However, obtaining the globally optimum resource allocation is exponentially complex and mathematically intractable. Here, we develop a partially decentralized algorithm to obtain an effective solution. The three major advantages of our work are as follows: 1) as opposed to a fixed resource allocation, we consider load demand at each access point (AP) when allocating spectrum; 2) to prevent overloaded APs, our scheme is dynamic in the sense that as the users move from one AP to the other, so do the allocated resources, if necessary, and such considerations generally result in huge computational complexity, which brings us to the third advantage: 3) we tackle complexity by introducing a hierarchical scheme comprising four phases: user association, load estimation, interference management via graph coloring, and scheduling. We provide mathematical analysis for the first three steps modeling the user and AP locations as Poisson point processes. Finally, we provide the results of numerical simulations to illustrate the efficacy of our scheme.
An ambitious goal in dense networks is to achieve optimal but decentralized resource allocation. The problem of decentralized power allocation was first addressed by @cite_20 . They showed that there exists a fully distributed algorithm which requires only local information if there exists a common, known, SINR at which the system performance is globally optimum, and there exists a feasible but unknown power vector that achieves this SINR. Unfortunately, these assumptions are hard to satisfy in practice @cite_24 . The proposed distributed algorithms in @cite_5 @cite_25 maximize the total system capacity ignoring user rate requirements and fairness among the users both within and among cells while @cite_1 aims for proportional fairness ignoring individual user rate requirements. To obtain a distributed solution, the authors in @cite_5 @cite_25 simplify the network model to an interference-ideal" network where the total interference is constant and independent of user location in the cell.
@cite_1 @cite_24 @cite_5 @cite_25 @cite_20
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In small cell networks (SCNs) co-channel interference is an important issue, and necessitates the use of interference mitigation strategies that allocate resources efficiently. This work discusses a distributed utility-based algorithm for downlink resource allocation (i.e., power and scheduling weights per carrier) in multicarrier SCNs. The proposed distributed downlink resource allocation (DDRA) algorithm aims to maximize the sum utility of the whole system. To achieve this goal, each base station (BS) selects the resource allocation strategy to maximize a surplus function comprising both, own cell utility and interference prices (that reflect the interference that is caused to neighboring cells). Two different utility functions are considered: max-rate and proportional fair-rate. For performance evaluation, a SCN deployed in a single story WINNER office building is considered. Simulation results show that the proposed algorithm is effective in enhancing not only the sum data rate of a SCN, but also the degree of fairness in resource sharing among users. We give an asynchronous adaptive algorithm for power control in cellular radio systems, which relaxes the demands of coordination and synchrony between the various mobiles and base stations. It relaxes the need for strict clock synchronization and also allows different links to update their power at different rates; unpredictable, bounded propagation delays are taken into account. The algorithm uses only local measurements and incorporates receiver noise. The overall objective is to minimize transmitters’ powers in a Pareto sense while giving each link a Carrier-to-Interference ratio which is not below a prefixed target. The condition for the existence and uniqueness of such a power distribution is obtained. Conditions are obtained for the asynchronous adaptation to converge to the optimal solution at a geometric rate. These conditions are surprisingly not burdensome. We analyze the sum capacity of multicell wireless networks with full resource reuse and channel-driven opportunistic scheduling in each cell. We address the problem of finding the co-channel (throughout the network) user assignment that results in the optimal joint multicell capacity, under a resource-fair constraint and a standard power control strategy. This problem in principle requires processing the complete co-channel gain information, and thus, has so far been justly considered unpractical due to complexity and channel gain signaling overhead. However, we expose here the following key result: The multicell optimal user scheduling problem admits a remarkably simple and fully distributed solution for large networks. This result is proved analytically for an idealized network. From this constructive proof, we propose a practical algorithm that is shown to achieve near maximum capacity for realistic cases of simulated networks of even small sizes. Joint optimization of transmit power and scheduling in wireless data networks promises significant system-wide capacity gains. However, this problem is known to be NP-hard and thus difficult to tackle in practice. We analyze this problem for the downlink of a multicell full reuse network with the goal of maximizing the overall network capacity. We propose a distributed power allocation and scheduling algorithm which provides significant capacity gain for any finite number of users. This distributed cell coordination scheme, in effect, achieves a form of dynamic spectral reuse, whereby the amount of reuse varies as a function of the underlying channel conditions and only limited inter-cell signaling is required. For wireless cellular communication systems, one seeks a simple effective means of power control of signals associated with randomly dispersed users that are reusing a single channel in different cells. By effecting the lowest interference environment, in meeting a required minimum signal-to-interference ratio of rho per user, channel reuse is maximized. Distributed procedures for doing this are of special interest, since the centrally administered alternative requires added infrastructure, latency, and network vulnerability. Successful distributed powering entails guiding the evolution of the transmitted power level of each of the signals, using only focal measurements, so that eventually all users meet the rho requirement. The local per channel power measurements include that of the intended signal as well as the undesired interference from other users (plus receiver noise). For a certain simple distributed type of algorithm, whenever power settings exist for which all users meet the rho requirement, the authors demonstrate exponentially fast convergence to these settings. >