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.
Millimeter (mm) wave massive MIMO has the potential for delivering orders of magnitude increases in mobile data rates, with compact antenna arrays providing narrow steerable beams for unprecedented levels of spatial reuse. A fundamental technical bottleneck, however, is rapid spatial channel estimation and beam adaptation in the face of mobility and blockage. Recently proposed compressive techniques which exploit the sparsity of mm wave channels are a promising approach to this problem, with overhead scaling linearly with the number of dominant paths and logarithmically with the number of array elements. Further, they can be implemented with RF beamforming with low-precision phase control. However, these methods make implicit assumptions on long-term phase coherence that are not satisfied by existing hardware. In this paper, we propose and evaluate a noncoherent compressive channel estimation technique which can estimate a sparse spatial channel based on received signal strength (RSS) alone, and is compatible with off-the-shelf hardware. The approach is based on cascading phase retrieval (i.e., recovery of complex-valued measurements from RSS measurements, up to a scalar multiple) with coherent compressive estimation. While a conventional cascade scheme would multiply two measurement matrices to obtain an overall matrix whose entries are in a continuum, a key novelty in our scheme is that we constrain the overall measurement matrix to be implementable using coarsely quantized pseudorandom phases, employing a virtual decomposition of the matrix into a product of measurement matrices for phase retrieval and compressive estimation. Theoretical and simulation results show that our noncoherent method scales almost as well with array size as its coherent counterpart, thus inheriting the scalability and low overhead of the latter. We propose an algorithm for mmWave channel estimation in the presence of a non-negligible carrier frequency offset (CFO) impairment. The algorithm exploits inherent sparsity of the mmWave channel in the angle of departure (AoD) and arrival (AoA) domain, employs sparse channel sensing protocol, and performs blind CFO compensation and estimation of channel paths in the angular domain. The simulation tests show that the proposed algorithm considerably outperforms the conventional orthogonal matching pursuit (OMP)-based mmWave channel estimation algorithm under different CFO and signal-to-noise ratio (SNR) regimes. Aside from the mmWave channel estimation problem, the algorithm is more generally applicable for greedy sparse recovery problems associated with estimating a sparse vector from measurements impaired by frequency offset. The abundant spectrum at millimeter-wave (mmWave) has the potential to greatly increase the capacity of 5G cellular systems. However, to overcome the high pathloss in the mmWave frequencies, beamforming with large antenna arrays is required at both the base station and user equipments for sufficient link budget. This feature is a challenge for beamforming training during initial access due to low SNR and poor synchronization. A recently developed compressive sensing (CS) based training algorithm exploits channel sparsity but it is vulnerable to phase error from poor synchronization. We propose a novel CS-based algorithm that tracks and compensates frequency offset and phase noise. Simulation results show that the proposed method improves achievable rate by 10 times compared with existing CS-based method during initial beamforming training. Initial access at millimeter wave frequencies is a challenging problem due to hardware non-idealities and low SNR measurements prior to beamforming. Prior work has exploited the observation that mmWave MIMO channels are sparse in the spatial angle domain and has used compressed sensing based algorithms for channel estimation. Most of them, however, ignore hardware impairments like carrier frequency offset and phase noise, and fail to perform well when such impairments are considered. In this paper, we develop a compressive channel estimation algorithm for narrowband mmWave systems, which is robust to such non idealities. We address this problem by constructing a tensor that models both the mmWave channel and CFO, and estimate the tensor while still exploiting the sparsity of the mmWave channel. Simulation results show that under the same settings, our method performs better than comparable algorithms that are robust to phase errors. There is much interest in integrating millimeter wave radios (mmWave) into wireless LANs and 5G cellular networks to benefit from their multi-GHz of available spectrum. Yet, unlike existing technologies, e.g., WiFi, mmWave radios require highly directional antennas. Since the antennas have pencil-beams, the transmitter and receiver need to align their beams before they can communicate. Existing systems scan the space to find the best alignment. Such a process has been shown to introduce up to seconds of delay, and is unsuitable for wireless networks where an access point has to quickly switch between users and accommodate mobile clients. This paper presents Agile-Link, a new protocol that can find the best mmWave beam alignment without scanning the space. Given all possible directions for setting the antenna beam, Agile-Link provably finds the optimal direction in logarithmic number of measurements. Further, Agile-Link works within the existing 802.11ad standard for mmWave LAN, and can support both clients and access points. We have implemented Agile-Link in a mmWave radio and evaluated it empirically. Our results show that it reduces beam alignment delay by orders of magnitude. In particular, for highly directional mmWave devices operating under 802.11ad, the delay drops from over a second to 2.5 ms. Millimeter (mm) wave picocellular networks have the potential for providing the 1000X capacity increase required to keep up with the explosive growth of mobile data. However, maintaining beams towards mobile users and adapting to frequent blockage, requires efficient, dynamic path tracking algorithms. In this paper, we develop and experimentally demonstrate a novel noncoherent compressive strategy for this problem, and compare it with conventional hierarchical and exhaustive beam scanning. To the best of our knowledge, this is the first experimental demonstration of practical, scalable path estimation for mmWave 60GHz picocells. Our results indicate the feasibility of sub-second path tracking with low overhead on today's mmWave hardware, and open up a rich space for design of 5G mmWave networks.