Hybrid analog and digital precoding allows millimeter wave (mmWave) systems to achieve both array and multiplexing gain. The design of the hybrid precoders and combiners, though, is usually based on knowledge of the channel. Prior work on mmWave channel estimation with hybrid architectures focused on narrowband channels. Since mmWave systems will be wideband with frequency selectivity, it is vital to develop channel estimation solutions for hybrid architectures based wideband mmWave systems. In this paper, we develop a sparse formulation and compressed sensing based solutions for the wideband mmWave channel estimation problem for hybrid architectures. First, we leverage the sparse structure of the frequency selective mmWave channels and formulate the channel estimation problem as a sparse recovery in both time and frequency domains. Then, we propose explicit channel estimation techniques for purely time or frequency domains and for combined time/frequency domains.Our solutions are suitable for both SC-FDE and OFDM systems. Simulation results show that the proposed solutions achieve good channel estimation quality, while requiring small training overhead.Leveraging the hybrid architecture at the transceivers gives further improvement in estimation error performance and achievable rates. Index TermsMillimeter wave communications, channel estimation, frequency selective channel, hybrid precodercombiner, compressed sensing, sparse recovery, multi-stream MIMO, IEEE 802.11ad.

Channel estimation is useful in millimeter wave (mmWave) MIMO communication systems. Channel state information allows optimized designs of precoders and combiners under different metrics such as mutual information or signal-to-interference-noise (SINR) ratio. At mmWave, MIMO precoders and combiners are usually hybrid, since this architecture provides a means to trade-off power consumption and achievable rate. Channel estimation is challenging when using these architectures, however, since there is no direct access to the outputs of the different antenna elements in the array. The MIMO channel can only be observed through the analog combining network, which acts as a compression stage of the received signal. Most of prior work on channel estimation for hybrid architectures assumes a frequencyflat mmWave channel model. In this paper, we consider a frequency-selective mmWave channel and propose compressed-sensing-based strategies to estimate the channel in the frequency domain. We evaluate different algorithms and compute their complexity to expose trade-offs in complexity-overheadperformance as compared to those of previous approaches.

Emerging applications involving device-to-device communication among wearable electronics require Gbps throughput, which can be achieved by utilizing millimeter wave (mmWave) frequency bands.When many such communicating devices are indoors in close proximity, like in a train car or airplane cabin, interference can be a serious impairment. This paper uses stochastic geometry to analyze the performance of mmWave networks with a finite number of interferers in a finite network region. Prior work considered either lower carrier frequencies with different antenna and channel assumptions, or a network with an infinite spatial extent. In this paper, human users not only carry potentially interfering devices, but also act to block interfering signals. Using a sequence of simplifying assumptions, accurate expressions for coverage and rate are developed that capture the effects of key antenna characteristics like directivity and gain, and are a function of the finite area and number of users. The assumptions are validated through a combination of analysis and simulation. The main conclusions are that mmWave frequencies can provide Gbps throughput even with omni-directional transceiver antennas, and larger, more directive antenna arrays give better system performance.

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