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Enabling NAICS to mitigate inter-cell interference in evolving wireless networks

In this paper, we introduce a new approach to weaken the effect of inter-cell codeword interference caused by two adjacent cells, which leads to a reduction in the communication performance at the cell-edge users. The objective is to create a new transmission procedure exchanged by the proximate base stations to ensure an efficient communication in the context of multiple-input-multiple-output (MIMO) transmissions. One of the most important issues is how the cell-edge users detect and decode the outcomes observed from the individual multi-streams of multi-codewords transmitted by two neighboring base stations. To address this problem, this paper presents a novel framework based on the network assisted interference cancellation and suppression (NAICS) technique. Its application to the scenario with two adjacent base stations and cellular-edge users is guided by two schemes. First, two base stations explicitly define and exchange the transmission policy for each codeword regarding the modulation index, the number of streams, and the order of the transmitted codewords. Second, a new taxonomy scheme is proposed, where the Bayesian decision classifier theory is used for the cell-edge users to extract the judgment rules from the exchanged transmission policy and enable symbols estimation and recognition in terms of posterior probability statements, hence offering a decision for each codeword. We introduce an integer optimization model subject to several practical constraints, such as the channel conditions of the users, the number of subcarriers, and the required throughput. The numerical tests demonstrate how to satisfy these requirements while validating the benefits over conventional approaches.