Utility-Optimal Multi-Pattern Reuse in Multi-Cell Networks
ABSTRACT Achieving sufficient spatial capacity gain through the use of small cells requires careful consideration of inter-cell interference (ICI) management via BS power coordination coupled with user scheduling inside cells. Optimal algorithms are known to be difficult to implement due to high computation and signaling overhead. This study proposes joint pattern-based ICI management and user scheduling algorithms that are practically implementable. The key idea is to decompose the original problem into two sub-problems in which ICI management is run at a slower time scale than user scheduling. We empirically show that even with such a slow tracking of system dynamics at the ICI management part, the decomposed approach achieves a considerable performance increase compared to conventional universal reuse schemes.
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ABSTRACT: Small cells such as pico or femto cells are promising as a solution to cope with higher traffic explosion and the large number of users. However, the users within small cells are likely to suffer severe inter-cell interference (ICI) from neighboring base stations (BSs). To tackle this, several papers suggest BS transmit power on/off control algorithms which increase edge user throughput. However, these algorithms require centralized coordinator and have high computational complexity. This paper makes a contribution towards presenting fully distributed and low complex joint BS on/off control and user scheduling algorithm (FDA) by selecting on/off pattern of BSs. Throughput the extensive simulations, we verify the performance of our algorithm as follows: (i) Our FDA provides better throughput performance of cell edge users by 170% than the algorithm without the ICI management. (ii) Our FDA catches up with the performance of optimal algorithm by 88-96% in geometric average throughput and sufficiently small gap in edge user throughput.12/2013; 38A(12). DOI:10.7840/kics.2013.38A.12.1125
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ABSTRACT: Fractional frequency reuse (FFR) is an inter-cell interference coordination (ICIC) scheme wherein the whole bandwidth is divided into regions using different frequency reuse factors in orthogonal frequency division multiple access (OFDMA) networks. However, it is hard to achieve the balance of cell-edge and cell-center user throughput in practice as all base stations (BS) are configured with static transmission power for each sub-band in traditional FFR. To solve this problem, a gradient projection based self-optimizing (GPB-SO) algorithm for ICIC is proposed to adjust transmission power of each sub-bands set by exchanging the gradient direction information, aiming to maximize the overall network utility. Furthermore, a closed-form of gradient direction is attained to update the transmission power of each BS in a distributed manner. With the simulation results, it is demonstrated that the proposed algorithm not only provides a better tradeoff between spectrum efficiency and cell-edge user throughput, but also increases the system energy efficiency due to interference reduction.Communications and Networking in China (CHINACOM), 2012 7th International ICST Conference on; 01/2012
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ABSTRACT: The successful deployment of LTE heterogeneous networks (HetNets) depends crucially on the inter-cell interference (ICI) management. Among ICI coordination schemes, fractional frequency reuse (FFR) is considered as an efficient technique well-suited to OFDMA-based HetNets. Two coupled questions in this context are: 1) how to associate users to appropriate base-stations considering the long list of available candidate cells, and 2) how to allocate frequency resources among multiple cells. In this paper, we treat the multi-cell frequency allocation as frequency partitioning among multiple reuse patterns, and develop a novel algorithm to solve these two coupled questions in a joint manner. We also provide practical criterion to select the set of essential candidate patterns from all possible patterns. Results show that the proposed joint strategy improves both the cell-edge user and overall network throughput.