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首页> 外文期刊>IEEE Transactions on Information Theory >Base Station Cooperation With Feedback Optimization: A Large System Analysis
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Base Station Cooperation With Feedback Optimization: A Large System Analysis

机译：基站配合反馈优化：大型系统分析

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In this paper, we study feedback optimization problems that maximize the users' signal to interference plus noise ratio (SINR) in a two-cell multiple-input multiple-output broadcast channel. Assuming the users learn their direct and interfering channels perfectly, they can feed back this information to the base stations (BSs) over the uplink channels. The BSs then use the channel information to design their transmission scheme. Two types of feedback are considered: 1) analog and 2) digital. In the analog feedback case, the users send their unquantized and uncoded channel state information (CSI) over the uplink channels. In this context, given a user's fixed transmit power, we investigate how he/she should optimally allocate it to feed back the direct and interfering (or cross) CSI for two types of BS cooperation schemes, namely, multicell processing (MCP) and coordinated beamforming. In the digital feedback case, the direct and cross link channel vectors of each user are quantized separately, each using the random vector quantization scheme, with different size codebooks. The users then send the index of the quantization vector in the corresponding codebook to the BSs. Similar to the feedback optimization problem for the analog feedback, we investigate the optimal bit partitioning for the direct and interfering link for both types of cooperation. We focus on regularized channel inversion precoding structures and perform our analysis in the large system limit in which the number of users per cell $(K)$ and the number of antennas per BS $(N)$ tend to infinity with their ratio $beta=({K}/{N})$ held fixed. We show that for both types of cooperation, for some values of interfering channel gain, usually at low values, no cooperation between the BSs is preferr- d. This is because, for these values of cross channel gain, the channel estimates for the cross link are not accurate enough for their knowledge to contribute to improving the SINR and there is no benefit in doing BS cooperation under that condition. We also show that for the MCP scheme, unlike in the perfect CSI case, the SINR improves only when the interfering channel gain is above a certain threshold.
机译：在本文中，我们研究了反馈优化问题，该问题使用户在两单元多输入多输出广播信道中的信噪比（SINR）最大化。假设用户完美地学习了他们的直接信道和干扰信道，他们可以通过上行链路信道将该信息反馈给基站（BS）。然后，BS使用信道信息来设计其传输方案。考虑两种类型的反馈：1）模拟和2）数字。在模拟反馈的情况下，用户通过上行链路信道发送其未量化和未编码的信道状态信息（CSI）。在这种情况下，给定用户的固定发射功率，我们研究他/她应如何针对两种类型的BS合作方案（即多小区处理（MCP）和协作式）优化分配它以反馈直接和干扰（或交叉）CSI波束成形。在数字反馈情况下，每个用户的直接和交叉链路信道矢量分别使用随机矢量量化方案和不同大小的码本分别进行量化。用户然后将相应码本中的量化向量的索引发送给BS。与模拟反馈的反馈优化问题类似，我们针对两种协作类型研究了直接链路和干扰链路的最佳位分配。我们关注正规化的信道反转预编码结构，并在较大的系统限制下进行分析，在该限制下，每个小区的用户数量$（K）$和每个BS $（N）$的天线数量趋于无穷大，其比率为$ beta =（{K} / {N}）$固定。我们表明，对于两种类型的合作，对于某些干扰信道增益值（通常为低值），BS之间均不希望合作。这是因为，对于这些交叉信道增益值，交叉链路的信道估计不够准确，以至于其知识不足以帮助改善SINR，并且在这种情况下进行BS合作没有任何好处。我们还表明，对于MCP方案，与在完美CSI情况下不同，仅当干扰信道增益高于某个阈值时，SINR才会提高。

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