Beam Squint and Channel Estimation for Wideband mmWave Massive MIMO-OFDM Systems

With the increasing scale of antenna arrays in wideband millimeter-wave (mmWave) communications, the physical propagation delays of electromagnetic waves traveling across the whole array will become large and comparable to the time-domain sample period, which is known as the spatial-wideband effect. In this case, different subcarriers in an orthogonal frequency division multiplexing (OFDM) system will "see" distinct angles of arrival (AoAs) for the same path. This effect is known as beam squint, resulting from the spatial-wideband effect, and makes the approaches based on the conventional multiple-input multiple-output (MIMO) model, such as channel estimation and precoding, inapplicable. After discussing the relationship between beam squint and the spatial-wideband effect, we propose a channel estimation scheme for frequency-division duplex (FDD) mmWave massive MIMO-OFDM systems with hybrid analog/digital precoding, which takes the beam squint effect into consideration. A super-resolution compressed sensing approach is developed to extract the frequency-insensitive parameters of each uplink channel path, i.e., the AoA and the time delay, and the frequency-sensitive parameter, i.e., the complex channel gain. With the help of the reciprocity of these frequency-insensitive parameters in FDD systems, the downlink channel estimation can be greatly simplified, where only limited pilots are needed to obtain downlink complex gains and reconstruct downlink channels. Furthermore, the uplink and downlink channel covariance matrices can be constructed from these frequency-insensitive channel parameters rather than through a long-term average, which enables the minimum mean-squared error (MMSE) channel estimation to further enhance performance. Numerical results demonstrate the superiority of the proposed scheme over the conventional methods in mmWave communications

Spatial- and Frequency-Wideband Effects in Millimeter-Wave Massive MIMO Systems

When there are a large number of antennas in massive MIMO systems, the transmitted wideband signal will be sensitive to the physical propagation delay of electromagnetic waves across the large array aperture, which is called the spatial-wideband effect. In this scenario, transceiver design is different from most of the existing works, which presume that the bandwidth of the transmitted signals is not that wide, ignore the spatial-wideband effect, and only address the frequency selectivity. In this paper, we investigate spatial- and frequency-wideband effects, called dual-wideband effects, in massive MIMO systems from array signal processing point of view. Taking mmWave-band communications as an example, we describe the transmission process to address the dual-wideband effects. By exploiting the channel sparsity in the angle domain and the delay domain, we develop the efficient uplink and downlink channel estimation strategies that require much less amount of training overhead and cause no pilot contamination. Thanks to the array signal processing techniques, the proposed channel estimation is suitable for both TDD and FDD massive MIMO systems. Numerical examples demonstrate that the proposed transmission design for massive MIMO systems can effectively deal with the dual-wideband effects.Comment: 13 pages, 10 figures. Index terms: Massive MIMO, mmWave, array signal processing, wideband, spatial-wideband, beam squint, angle reciprocity, delay reciprocity. Submitted to IEEE Transactions on Signal Processin

A Robust Time-Domain Beam Alignment Scheme for Multi-User Wideband mmWave Systems

Millimeter wave (mmWave) communication with large array gains is a key ingredient of next generation (5G) wireless networks. Effective communication in mmWaves usually depends on the knowledge of the channel. We refer to the problem of finding a narrow beam pair at the transmitter and at the receiver, yielding high Signal to Noise Ratio (SNR) as Beam Alignment (BA). Prior BA schemes typically considered deterministic channels, where the instantaneous channel coefficients are assumed to stay constant for a long time. In this paper, in contrast, we propose a time-domain BA scheme for wideband mmWave systems, where the channel is characterized by multi-path components, different delays, Angle-of-Arrivals/Angle-of-Departures (AoAs/AoDs), and Doppler shifts. In our proposed scheme, the Base Station (BS) probes the channel in the downlink by some sequences with good autocorrelation property (e.g., Pseudo-Noise (PN) sequences), letting each user estimate its best AoA-AoD that connects the user to the BS with two-sided high beamforming gain. We leverage the sparse nature of mmWaves in the AoA-AoD-time domain, and formulate the BA problem as a Compressed Sensing (CS) of a non-negative sparse vector. We use the recently developed Non-Negative Least Squares (NNLS) technique to efficiently find the strongest path connecting the BS and each user. Simulation results show that the proposed scheme outperforms its counterpart in terms of the training overhead and robustness to fast channel variations

Channel Tracking and Hybrid Precoding for Wideband Hybrid Millimeter Wave MIMO Systems

A major source of difficulty when operating with large arrays at mmWave frequencies is to estimate the wideband channel, since the use of hybrid architectures acts as a compression stage for the received signal. Moreover, the channel has to be tracked and the antenna arrays regularly reconfigured to obtain appropriate beamforming gains when a mobile setting is considered. In this paper, we focus on the problem of channel tracking for frequency-selective mmWave channels, and propose two novel channel tracking algorithms that leverage prior statistical information on the angles-of-arrival and angles-of-departure. Exploiting this prior information, we also propose a precoding and combining design method to increase the received SNR during channel tracking, such that near-optimum data rates can be obtained with low-overhead. In our numerical results, we analyze the performance of our proposed algorithms for different system parameters. Simulation results show that, using channel realizations extracted from the 5G New Radio channel model, our proposed channel tracking framework is able to achieve near-optimum data rates

Beamforming Algorithm for Multiuser Wideband Millimeter-Wave Systems with Hybrid and Subarray Architectures

We present a beamforming algorithm for multiuser wideband millimeter wave (mmWave) communication systems where one access point uses hybrid analog/digital beamforming while multiple user stations have phased-arrays with a single RF chain. The algorithm operates in a more general mode than others available in literature and has lower computational complexity and training overhead. Throughout the paper, we describe: i) the construction of novel beamformer sets (codebooks) with wide sector beams and narrow beams based on the orthogonality property of beamformer vectors, ii) a beamforming algorithm that uses training transmissions over the codebooks to select the beamformers that maximize the received sumpower along the bandwidth, and iii) a numerical validation of the algorithm in standard indoor scenarios for mmWave WLANs using channels obtained with both statistical and raytracing models. Our algorithm is designed to serve multiple users in a wideband OFDM system and does not require channel matrix knowledge or a particular channel structure. Moreover, we incorporate antenna-specific aspects in the analysis, such as antenna coupling, element radiation pattern, and beam squint. Although there are no other solutions for the general system studied in this paper, we characterize the algorithm's achievable rate and show that it attains more than 70 percent of the spectral efficiency (between 1.5 and 3 dB SNR loss) with respect to ideal fully-digital beamforming in the analyzed scenarios. We also show that our algorithm has similar sum-rate performance as other solutions in the literature for some special cases, while providing significantly lower computational complexity (with a linear dependence on the number of antennas) and shorter training overhead

Frequency-domain Compressive Channel Estimation for Frequency-Selective Hybrid mmWave MIMO Systems

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 frequency-flat 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-overhead-performance as compared to those of previous approaches

An Overview of Signal Processing Techniques for Millimeter Wave MIMO Systems

Communication at millimeter wave (mmWave) frequencies is defining a new era of wireless communication. The mmWave band offers higher bandwidth communication channels versus those presently used in commercial wireless systems. The applications of mmWave are immense: wireless local and personal area networks in the unlicensed band, 5G cellular systems, not to mention vehicular area networks, ad hoc networks, and wearables. Signal processing is critical for enabling the next generation of mmWave communication. Due to the use of large antenna arrays at the transmitter and receiver, combined with radio frequency and mixed signal power constraints, new multiple-input multiple-output (MIMO) communication signal processing techniques are needed. Because of the wide bandwidths, low complexity transceiver algorithms become important. There are opportunities to exploit techniques like compressed sensing for channel estimation and beamforming. This article provides an overview of signal processing challenges in mmWave wireless systems, with an emphasis on those faced by using MIMO communication at higher carrier frequencies.Comment: Submitted to IEEE Journal of Selected Topics in Signal Processin

Millimeter Wave Beam-Selection Using Out-of-Band Spatial Information

Millimeter wave (mmWave) communication is one feasible solution for high data-rate applications like vehicular-to-everything communication and next generation cellular communication. Configuring mmWave links, which can be done through channel estimation or beam-selection, however, is a source of significant overhead. In this paper, we propose to use spatial information extracted at sub-6 GHz to help establish the mmWave link. First, we review the prior work on frequency dependent channel behavior and outline a simulation strategy to generate multi-band frequency dependent channels. Second, assuming: (i) narrowband channels and a fully digital architecture at sub-6 GHz; and (ii) wideband frequency selective channels, OFDM signaling, and an analog architecture at mmWave, we outline strategies to incorporate sub-6 GHz spatial information in mmWave compressed beam selection. We formulate compressed beam-selection as a weighted sparse signal recovery problem, and obtain the weighting information from sub-6 GHz channels. In addition, we outline a structured precoder/combiner design to tailor the training to out-of-band information. We also extend the proposed out-of-band aided compressed beam-selection approach to leverage information from all active OFDM subcarriers. The simulation results for achievable rate show that out-of-band aided beam-selection can reduce the training overhead of in-band only beam-selection by 4x.Comment: 30 pages, 11 figure

Compressive Initial Access and Beamforming Training for Millimeter-Wave Cellular Systems

Initial access (IA) is a fundamental physical layer procedure in cellular systems where user equipment (UE) detects nearby base station (BS) as well as acquire synchronization. Due to the necessity of using antenna array in millimeter-wave (mmW) IA, the channel spatial information can also be inferred. The state-of-the-art directional IA (DIA) uses sector sounding beams with limited angular resolution, and thus requires additional dedicated radio resources, access latency and overhead for refined beam training. To remedy the problem of access latency and overhead in DIA, this work proposes to use a quasi-omni pseudorandom sounding beam for IA, and develops a novel algorithm for joint initial access and fine resolution initial beam training without requiring extra radio resources. We provide the analysis of the proposed algorithm miss detection rate under synchronization error, and further derive Cram\'er-Rao lower bound of angular estimation under frequency offset. Using QuaDRiGa simulator with mmMAGIC model at 28 GHz, the numerical results show that the proposed approach is advantageous to DIA with hierarchical beam training. The proposed algorithm offers up to two order of magnitude access latency saving compared to DIA, when the same discovery, post training SNR, and overhead performance are targeted. This conclusion holds true in various propagation environments and 3D locations of a mmW pico-cell with up to 140m radius.Comment: 14 pages, 7 figures, submitted to IEEE Journal of Selected Topics in Signal Processin

A Hardware-Efficient Hybrid Beamforming Solution for mmWave MIMO Systems

In millimeter wave (mmWave) communication systems, existing hybrid beamforming solutions generally require a large number of high-resolution phase shifters (PSs) to realize analog beamformers, which still suffer from high hardware complexity and power consumption. Targeting at this problem, this article introduces a novel hardware-efficient hybrid precoding/combining architecture, which only employs a limited number of simple phase over-samplers (POSs) and a switch (SW) network to achieve maximum hardware efficiency while maintaining satisfactory spectral efficiency performance. The POS can be realized by a simple circuit and simultaneously outputs several parallel signals with different phases. With the aid of a simple switch network, the analog precoder/combiner is implemented by feeding the signals with appropriate phases to antenna arrays or RF chains. We analyze the design challenges of this POS-SW-based hybrid beamforming architecture and present potential solutions to the fundamental issues, especially the precoder/combiner design and the channel estimation strategy. Simulation results demonstrate that this hardware-efficient structure can achieve comparable spectral efficiency but much higher energy efficiency than that of the traditional structures