Initial access (IA) is a fundamental procedure in cellular systems where user equipment (UE) detects base station (BS) and acquires synchronization. Due to the necessity of using antenna arrays for IA in millimeter-wave (mmW) systems, BS simultaneously performs beam training to acquire angular channel state information. The state-of-the-art directional IA (DIA) uses a set of narrow sounding beams in IA, where different beam pairs are sequentially measured, and the best candidate is determined. However, the directional beam training accuracy depends on scanning beam angular resolution, and consequently its improvement requires additional dedicated radio resources, access latency, and overhead. To remedy the problem of access latency and overhead in DIA, this paper proposes to use quasi-omni pseudorandom sounding beams for IA, and develops an algorithm for joint initial access and fine resolution initial beam training without requiring additional radio resources. It comprehensively models realistic timing and frequency synchronization errors encountered in IA. We provide the analysis of the proposed algorithm's miss detection rate under timing synchronization errors, and we further derive Cramer–Rao lower bound of angular estimation under frequency offset, considering the 5G-NR compliant IA procedure. To accommodate the ever increasing bandwidth for beam training in standard evolution beyond 5G, we design the beam squint robust algorithm. For realistic performance evaluation under mmW channels, we use QuaDRiGa simulator with mmMAGIC model at 28 GHz to show that the proposed approach is advantageous to DIA. The proposed algorithm offers orders 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 three-dimensional locations of a mmW pico-cell with up to 140 m radius. Furthermore, our results demonstrate that the proposed beam squint robust algorithm is able to retain unaffected performance with increased beam training bandwidth.
The alternative approaches for beam training are based on parametric channel estimation @cite_31 @cite_24 @cite_32 @cite_18 @cite_25 @cite_17 @cite_42 @cite_45 . Exploiting the mmW sparse scattering nature, compressive sensing (CS) approaches have been considered to effectively estimate channel parameters based on channel observations obtained via various sounding beams. Works @cite_31 @cite_24 proposed a CS-based narrowband BF training with pseudorandom sounding beamformers in the downlink, and @cite_32 extended this approach for a wideband channel. Other related works include channel covariance estimation @cite_18 @cite_25 @cite_17 which requires periodic channel observations, and UE centric uplink training @cite_42 @cite_45 . It is worth nothing that all recent works focus on channel estimation alone while assuming perfect cell discovery and synchronization. The 5G-NR frame structure that supports IA is rarely considered, and further the feasibility of joint initial access and CS-based beam training has not been investigated.
@cite_18 @cite_42 @cite_32 @cite_24 @cite_45 @cite_31 @cite_25 @cite_17
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In millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems, channel estimation is a challenging task in terms of acquiring the instantaneous channel state information (CSI), because both the estimation complexity and the overhead required for pilot symbols and feedback grow drastically as the number of antennas increases. Alternatively, some channel statistics in the form of partial CSI are sufficient for transceiver optimization and noncoherent detection in time-varying wireless environments. To obtain such useful statistical information accurately and efficiently, this paper proposes a new channel statistics estimation method using the compressive covariance sensing technique, which directly estimates the desired second-order statistics of the channel while bypassing the intermediate recovery of the instantaneous channel matrix itself. A diagonal-search orthogonal matching pursuit (DS-OMP) algorithm is developed for fast channel estimation. The proposed algorithm has low computational complexity and reduced overhead in training and feedback, owing to its proper utilization of the joint sparsity structure of the channel covariance matrix. Configuring the hybrid precoders and combiners in a millimeter wave multiuser multiple-input multiple-output system is challenging in frequency selective channels. In this paper, we develop a system that uses compressive estimation on the uplink to configure precoders and combiners for the downlink. In the first step, the base station (BS) simultaneously estimates the channels from all the mobile stations on each subcarrier. To reduce the number of measurements required, compressed sensing techniques are developed that exploit common support on the different subcarriers. In the second step, exploiting reciprocity and the channel estimates the BS designs hybrid precoders and combiners. Two algorithms are developed for this purpose, with different performance and complexity tradeoffs: First, a factorization of the purely digital solution; and second, an iterative hybrid design. Extensive numerical experiments evaluate the proposed solutions comparing to the state-of-the-art strategies, and illustrating design tradeoffs in overhead, complexity, and performance. Channel estimation is useful in millimeter wave (mm-wave) 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 ratio. At mm-wave, 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 the prior work on channel estimation for hybrid architectures assumes a frequency-flat mm-wave channel model. In this paper, we consider a frequency-selective mm-wave 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 tradeoffs in complexity overhead performance as compared with those of previous approaches. Millimeter wave (mmWave) systems will likely employ directional beamforming with large antenna arrays at both the transmitters and receivers. Acquiring channel knowledge to design these beamformers, however, is challenging due to the large antenna arrays and small signal-to-noise ratio before beamforming. In this paper, we propose and evaluate a downlink system operation for multi-user mmWave systems based on compressed sensing channel estimation and conjugate analog beamforming. Adopting the achievable sum-rate as a performance metric, we show how many compressed sensing measurements are needed to approach the perfect channel knowledge performance. The results illustrate that the proposed algorithm requires an order of magnitude less training overhead compared with traditional lower-frequency solutions, while employing mmWave-suitable hardware. They also show that the number of measurements need to be optimized to handle the trade-off between the channel estimate quality and the training overhead. In this paper, we develop a low-complexity channel estimation for hybrid millimeter wave (mmWave) systems, where the number of radio frequency (RF) chains is much less than the number of antennas equipped at each transceiver. The proposed mmWave channel estimation algorithm first exploits multiple frequency tones to estimate the strongest angle-of-arrivals (AoAs) at both base station (BS) and user sides for the design of analog beamforming matrices. Then, all the users transmit orthogonal pilot symbols to the BS along the directions of the estimated strongest AoAs in order to estimate the channel. The estimated channel will be adopted to design the digital zero-forcing (ZF) precoder at the BS for the multi-user downlink transmission. The proposed channel estimation algorithm is applicable to both the non-sparse and sparse mmWave channel environments. Furthermore, we derive a tight achievable rate upper bound of the digital ZF precoding with the proposed channel estimation algorithm scheme. Our analytical and simulation results show that the proposed scheme obtains a considerable achievable rate of fully digital systems, where the number of RF chains equipped at each transceiver is equal to the number of antennas. Besides, considering the effect of various types of errors, i.e., random phase errors, transceiver analog beamforming errors, and equivalent channel estimation errors, we derive a closed-form approximation for the achievable rate of the considered scheme. We illustrate the robustness of the proposed channel estimation and multi-user downlink precoding scheme against the system imperfection. We propose and investigate a compressive architecture for estimation and tracking of sparse spatial channels in millimeter (mm) wave picocellular networks. The base stations are equipped with antenna arrays with a large number of elements (which can fit within compact form factors because of the small carrier wavelength) and employ radio frequency (RF) beamforming, so that standard least squares adaptation techniques (which require access to individual antenna elements) are not applicable. We focus on the downlink, and show that “compressive beacons,” transmitted using pseudorandom phase settings at the base station array, and compressively processed using pseudorandom phase settings at the mobile array, provide information sufficient for accurate estimation of the two-dimensional (2D) spatial frequencies associated with the directions of departure of the dominant rays from the base station, and the associated complex gains. This compressive approach is compatible with coarse phase-only control, and is based on a near-optimal sequential algorithm for frequency estimation which approaches the Cramer Rao Lower Bound. The algorithm exploits the geometric continuity of the channel across successive beaconing intervals to reduce the overhead to less than 1 even for very large ( @math ) arrays. Compressive beaconing is essentially omnidirectional, and hence does not enjoy the SNR and spatial reuse benefits of beamforming obtained during data transmission. We therefore discuss system level design considerations for ensuring that the beacon SNR is sufficient for accurate channel estimation, and that inter-cell beacon interference is controlled by an appropriate reuse scheme. Millimeter-wave (mm-wave) frequency bands provide an opportunity for much wider channel bandwidth compared with the traditional sub-6-GHz band. Communication at mm-waves is, however, quite challenging due to the severe propagation pathloss incurred by conventional isotropic antennas. To cope with this problem, directional beamforming both at the base station (BS) side and at the user equipment (UE) side is necessary in order to establish a strong path conveying enough signal power. Finding such beamforming directions is referred to as beam alignment (BA). This paper presents a new scheme for efficient BA. Our scheme finds a strong propagation path identified by an angle-of-arrival (AoA) and angle-of-departure (AoD) pair, by exploring the AoA–AoD domain through pseudo-random multi-finger beam patterns and constructing an estimate of the resulting second-order statistics (namely, the average received power for each pseudo-random beam configuration). The resulting under-determined system of equations is efficiently solved using non-negative constrained least-squares, yielding naturally a sparse non-negative vector solution whose maximum component identifies the optimal path. As a result, our scheme is highly robust to variations of the channel time dynamics compared with alternative concurrent approaches based on the estimation of the instantaneous channel coefficients, rather than of their second-order statistics. In the proposed scheme, the BS probes the channel in the downlink and trains simultaneously an arbitrarily large number of UEs. Thus, “beam refinement,” with multiple interactive rounds of downlink uplink transmissions, is not needed. This results in a scalable BA protocol, where the protocol overhead is virtually independent of the number of UEs, since all the UEs run the BA procedure at the same time. Extensive simulation results illustrate that our approach is superior to the state-of-the-art BA schemes proposed in the literature in terms of training overhead in multi-user scenarios and robustness to variations in the channel dynamics. Spatial channel covariance information can replace full knowledge of the entire channel matrix for designing analog precoders in hybrid multiple-input-multiple-output (MIMO) architecture. Spatial channel covariance estimation, however, is challenging for the hybrid MIMO architecture because the estimator operating at baseband can only obtain a lower dimensional pre-combined signal through fewer radio frequency chains than antennas. In this paper, we propose two approaches to covariance estimation based on compressive sensing techniques. One is to apply a time-varying sensing matrix, and the other is to exploit the prior knowledge that the covariance matrix is Hermitian. We present the rationale behind the two ideas and validate the superiority of the proposed methods by theoretical analysis and numerical simulations. We conclude the paper by extending the proposed algorithms from narrowband MIMO systems with a single receive antenna to wideband systems with multiple receive antennas.