This paper studies the joint user association and resource allocation in heterogeneous networks (HetNets) from a novel perspective, motivated by and generalizing the idea of fractional frequency reuse. By treating the multi-cell multi-user resource allocation as resource partitioning among multiple reuse patterns, we propose a unified framework to analyze and compare a wide range of user association and resource allocation strategies for HetNets, and provide an optimal benchmark for network performance. The enabling mechanisms are a novel formulation to consider all possible interference patterns or any pre-defined subset of patterns, and efficient sparsity-pursuit algorithms to find the solution. A notable feature of this formulation is that the patterns remain fixed during the resource optimization process. This creates a favorable opportunity for convex formulations while still considering interference coupling. More importantly, in view of the fact that multi-cell resource allocation is very computational demanding, our framework provides a systematic way to trade off performance for the reduction of computational complexity by restricting the candidate patterns to a small number of feature patterns. Relying on the sparsity-pursuit capability of the proposed algorithms, we develop a practical guideline to identify the feature patterns. Numerical results show that the identified feature patterns can significantly improve the existing strategies, and jointly optimizing the user association and resource allocation indeed brings considerable gain.
Uncertainty in the spatial signature of interfering signals is a major source of performance degradation in the downlink of a MIMO network. In general, the exact transmit covariance matrix of the other transmitters can not be predicted correctly in advance as the optimal strategies mutually depend on each other. In this work, we investigate two approaches for optimizing downlink transmission that are robust to interference, while operating in absence of cross channel information at the transmitter. Although they may seem unrelated, the two approaches share an interesting symmetry and are both derived from a more general minimax duality.
Inter-cell interference diminishes the performance of wireless cellular networks, hence interference management by cooperation of basestations should be employed to combat interference and increase spectral efficiency. Contrary to full cooperation, which renders the network into a super-cell with distributed antennas, we investigate a form of weak cooperation: transmission strategies among the basestations are coordinated, which requires a minor overhead, while the users treat interference of other cells as noise. Although our results are not restricted to reuse partitioning, we assume a set of strategies, each corresponding to a different reuse factor, and assign orthogonal resources to each strategy. Basestation cooperation is realized by dynamically adjusting the resource allocation, socalled fractional reuse partitioning, while the capacity achieving single-cell strategies are employed in each cell in order to optimally manage intra-cell interference exploiting all degrees of freedom offered by multiple antennas at the transmitter and receiver. Efficient operation of a cellular communications network requires interference management in order to achieve high data rates including rate assignment matched to the user demands, which we formulate as network utility maximization problem. We put special emphasis on the popular utilities sum-rate and proportional fairness, either with or without additional quality of service constraints. Finally, we illustrate the performance gain of our method by providing system level simulation results for a three sectorized cellular network with nineteen sites.
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