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Active Learning and CSI Acquisition for mmWave Initial Alignment

机译:主动学习和CSI采集以实现mmWave初始对准

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Millimeter wave (mmWave) communication with large antenna arrays is a promising technique to enable extremely high data rates due to large available bandwidth in mmWave frequency bands. In addition, given the knowledge of an optimal directional beamforming vector, large antenna arrays have been shown to overcome both the severe signal attenuation in mmWave as well as the interference problem. However, fundamental limits on achievable learning rate of an optimal beamforming vector remain. This paper considers the problem of adaptive and sequential optimization of the beamforming vectors during the initial access phase of communication. With a single-path channel model, the problem is reduced to actively learning the Angle-of-Arrival (AoA) of the signal sent from the user to the Base Station (BS). Drawing on the recent results in the design of a hierarchical beamforming codebook, sequential measurement dependent noisy search strategies, and active learning from an imperfect labeler, an adaptive and sequential alignment algorithm is proposed. For any given resolution and error probability of the estimated AoA, an upper bound on the expected search time of the proposed algorithm is derived via Extrinsic Jensen-Shannon Divergence. The upper bound demonstrates that the search time of the proposed algorithm asymptotically matches the performance of the noiseless bisection search up to a constant factor, in effect, characterizing the AoA acquisition rate. Furthermore, the upper bound shows that the acquired AoA error probability decays exponentially fast with the search time with an exponent that is a decreasing function of the acquisition rate. Numerically, the proposed algorithm is compared with prior work where a significant improvement of the system communication rate is observed. Most notably, in the relevant regime of low (-10 dB to +5 dB) raw SNR, this establishes the first practically viable solution for initial access and, hence, the first demonstration of stand-alone mmWave communication.
机译:与大型天线阵列的毫米波(mmWave)通信是一种有前途的技术,由于mmWave频带中的可用带宽很大,因此可以实现极高的数据速率。另外,已知最佳定向波束形成矢量,已经证明大型天线阵列可以克服毫米波中严重的信号衰减以及干扰问题。但是,仍然存在对最佳波束形成向量的可实现学习速率的基本限制。本文考虑了在通信的初始访问阶段,波束成形矢量的自适应和顺序优化问题。使用单路径信道模型,可以将问题减少到主动学习从用户发送到基站(BS)的信号的到达角(AoA)。借鉴分层波束形成码本,顺序测量相关的噪声搜索策略以及从不完美贴标机主动学习的最新设计成果,提出了一种自适应顺序对齐算法。对于估计的AoA的任何给定分辨率和错误概率,将通过外部Jensen-Shannon发散来推导所提出算法的预期搜索时间的上限。上限表明,所提算法的搜索时间渐近地与无噪声二等分搜索的性能匹配,直至达到一个恒定因子,从而有效地表征了AoA采集速率。此外,上限表明,所获得的AoA错误概率随搜索时间呈指数级衰减,其指数是获取速率的下降函数。在数值上,将所提出的算法与先前的工作进行比较,在先前的工作中可以观察到系统通信速率的显着提高。最值得注意的是,在低原始SNR(-10 dB至+5 dB)的相关范围内,这为首次访问建立了第一个切实可行的解决方案,因此,建立了独立毫米波通信的第一个演示。

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