Standard blind interference alignment (sBIA) suffers from noise accumulation which severely deteriorates received signal-to-noise ratio (SNR) and significantly reduces transmission rate. A noise accumulation factor is proposed to describe the loss between the user’s received SNR, and the final post processing SNR which determines the performance of the decoding of the encoded data streams (EDSs). A heterogeneous semi-BIA (H-SBIA) framework where users with different noise accumulation factors can be flexibly allocated effective EDSs (E-EDSs) is constructed. Relying on the H-SBIA framework, a heuristic H-SBIA algorithm is designed for maximizing the overall E-EDSs considering both fairness and coherence time constraints. Extensive simulations demonstrate that H-SBIA produces great fairness performance improvement at a limited cost in the achievable sum rate. The Jain’s fairness index is about 2.2 times greater than that for SNR-SBIA proposed in previous work, at the cost of sacrificing 10% of the achievable sum rate.

中文翻译：

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具有公平性考虑的有限信噪比网络中的异构半盲干扰对齐

标准盲干扰对齐 (sBIA) 会受到噪声累积的影响，这会严重降低接收信噪比 (SNR) 并显着降低传输速率。提出了一个噪声累积因子来描述用户接收到的 SNR 与决定编码数据流 (EDS) 解码性能的最终后处理 SNR 之间的损失。构建了一个异构半BIA（H-SBIA）框架，其中具有不同噪声累积因子的用户可以灵活地分配有效的EDS（E-EDS）。依赖于 H-SBIA 框架，设计了一种启发式 H-SBIA 算法，用于在考虑公平性和相干时间约束的情况下最大化整体 E-EDS。广泛的模拟表明，H-SBIA 在可实现的总和率中以有限的成本产生了极大的公平性能改进。Jain 的公平性指数大约是之前工作中提出的 SNR-SBIA 的 2.2 倍，代价是牺牲了 10% 的可实现的总和率。