Coordinated Beam Selection in Millimeter Wave Multi-User MIMO using Out-of-Band Information

Abstract

Using out-of-band (OOB) side-information has recently been shown to accelerate beam selection in single-user millimeter wave (mmWave) massive MIMO (m-MIMO) communications. In this paper, we propose a novel OOB-aided beam selection framework for a mmWave uplink multi-user system.In particular, we exploit spatial information extracted from lower (sub-6 GHz) bands in order to assist with an inter-user coordination scheme at mmWave bands. To enforce coordination, we propose an exchange protocol exploiting device-to-device (D2D) communications. In particular, low-rate beam-related information is exchanged between the mobile terminals. The decentralized coordination mechanism allows the suppression of the so-called co-beam interference which would otherwise lead to irreducible interference at the base station (BS) side, thereby triggering substantial spectral efficiency (SE) gains.

Deep Learning for TDD and FDD Massive MIMO: Mapping Channels in Space and Frequency

“…Estimating the channels at one frequency band using the channel knowledge at a different frequency band is attracting increasing interest [7]- [10]. In [7], the parameters of the uplink channels, such as the angles of arrival and path delays were estimated and used to construct the downlink channels at an adjacent frequency band.…”

“…This frequency extrapolation concept was further studied in [8] where lower bounds on the mean squared error of the extrapolated channels were derived. On a relevant line of work, [9], [10] proposed to leverage the channel knowledge at one frequency band (sub-6 GHz) to reduce the training overhead associated with design the mmWave beamforming vectors, leveraging the spatial correlation between the two frequency bands. A common feature of all the prior work in [7]- [10] is the requirement to first estimate some spatial knowledge (such as the angles of arrival) about the channels in one frequency band and then leverage this knowledge in the other frequency band.…”

“…On a relevant line of work, [9], [10] proposed to leverage the channel knowledge at one frequency band (sub-6 GHz) to reduce the training overhead associated with design the mmWave beamforming vectors, leveraging the spatial correlation between the two frequency bands. A common feature of all the prior work in [7]- [10] is the requirement to first estimate some spatial knowledge (such as the angles of arrival) about the channels in one frequency band and then leverage this knowledge in the other frequency band. This channel parameter estimation process, however, is fundamentally limited by the system and hardware capability in resolving these channel parameters, which highly affects the quality of the extrapolated channels.…”

Deep Learning Assisted mmWave Beam Prediction for Heterogeneous Networks: A Dual-Band Fusion Approach

“…7, we further compare the two methods for calculating the wide beam probabilities in terms of the normalized beamforming gain performance G N of our proposed fixed-n based and sum-p based schemes. Specifically, the first method predicts the probability of the optimal wide beam directly according to the wide beam label with the largest received power, while the second method, as proposed in (14), adds up the probabilities of the narrow beams within the angular range of each wide beam. The G N performance is illustrated as the function of the mmWave measurement overhead O, which corresponds to the number of measured wide beams K w for our proposed schemes, and each point in the curve of the sum-p based scheme depicts the corresponding overhead O and beamforming gain G N at a given probability sum threshold from η w ∈ {0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85}.…”

“…In [13], field experiments demonstrated that the power azimuth spectrums (PASs) of sub-6 GHz and mmWave channels are almost congruent in the co-located scenarios. Based on these characteristics, the works [13], [14] proposed to only sweep the mmWave beams angularly neighboring to a few dominant sub-6 GHz paths for reducing the training overhead. Furthermore, other studies [15], [16] formulated the beam selection as a sparse signal recovery problem weighted by the sub-6 GHz PAS, which can reach high achievable rates under the smaller size of sparse training codebooks.…”