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Register a new userFull Downlink Channel Reconstruction using Incomplete Uplink Channel Measurements in Massive MIMO networks
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Added by
Aleksei Fedorov
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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While more and more antennas are integrated into single mobile user equipment to increase communication quality and throughput, the number of antennas used for transmission is commonly restricted due to the concerns on hardware complexity and energy consumption, making it impossible to achieve the maximum channel capacity. This paper investigates the problem of reconstructing the full downlink channel from incomplete uplink channel measurements in Massive MIMO systems. We present ARDI, a scheme that builds a bridge between the radio channel and physical signal propagation environment to link spatial information about the non-transmitting antennas with their radio channels. By inferring locations and orientations of the non-transmitting antennas from an incomplete set of uplink channels, ARDI can reconstruct the downlink channels for non-transmitting antennas. We derive a closed-form solution to reconstruct antenna orientation in both single-path and multi-path propagation environments. The performance of ARDI is evaluated using simulations with realistic human movement. The results demonstrate that ARDI is capable of accurately reconstructing full downlink channels when the signal-to-noise ratio is higher than 15dB, thereby expanding the channel capacity of Massive MIMO networks.
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Massive Multiple-Input Multiple-Output (massive MIMO) is a variant of multi-user MIMO in which the number of antennas at each Base Station (BS) is very large and typically much larger than the number of users simultaneously served. Massive MIMO can be implemented with Time Division Duplexing (TDD) or Frequency Division Duplexing (FDD) operation. FDD massive MIMO systems are particularly desirable due to their implementation in current wireless networks and their efficiency in situations with symmetric traffic and delay-sensitive applications. However, implementing FDD massive MIMO systems is known to be challenging since it imposes a large feedback overhead in the Uplink (UL) to obtain channel state information for the Downlink (DL). In recent years, a considerable amount of research is dedicated to developing methods to reduce the feedback overhead in such systems. In this paper, we use the sparse spatial scattering properties of the environment to achieve this goal. The idea is to estimate the support of the continuous, frequency-invariant scattering function from UL channel observations and use this estimate to obtain the support of the DL channel vector via appropriate interpolation. We use the resulting support estimate to design an efficient DL probing and UL feedback scheme in which the feedback dimension scales proportionally with the sparsity order of DL channel vectors. Since the sparsity order is much less than the number of BS antennas in almost all practically relevant scenarios, our method incurs much less feedback overhead compared with the currently proposed methods in the literature, such as those based on compressed-sensing. We use numerical simulations to assess the performance of our probing-feedback algorithm and compare it with these methods.
In this paper, we propose a novel method for efficient implementation of a massive Multiple-Input Multiple-Output (massive MIMO) system with Frequency Division Duplexing (FDD) operation. Our main objective is to reduce the large overhead incurred by Downlink (DL) common training and Uplink (UL) feedback needed to obtain channel state information (CSI) at the base station. Our proposed scheme relies on the fact that the underlying angular distribution of a channel vector, also known as the angular scattering function, is a frequency-invariant entity yielding a UL-DL reciprocity and has a limited angular support. We estimate this support from UL CSI and interpolate it to obtain the corresponding angular support of the DL channel. Finally we exploit the estimated support of the DL channel of all the users to design an efficient channel probing and feedback scheme that maximizes the total spectral efficiency of the system. Our method is different from the existing compressed-sensing (CS) based techniques in the literature. Using support information helps reduce the feedback overhead from O(s*log M) in CS techniques to O(s) in our proposed method, with $s$ and $M$ being sparsity order of the channel vectors and the number of base station antennas, respectively. Furthermore, in order to control the channel sparsity and therefore the DL common training and UL feedback overhead, we introduce the novel concept of active channel sparsification. In brief, when the fixed pilot dimension is less than the required amount for reliable channel estimation, we introduce a pre-beamforming matrix that artificially reduces the effective channel dimension of each user to be not larger than the DL pilot dimension, while maximizing both the number of served users and the number of probed angles. We provide numerical experiments to compare our method with the state-of-the-art CS technique.
In this paper, we study Full Duplex (FD) Multiple-Input Multiple-Output (MIMO) radios for simultaneous data communication and control information exchange. Capitalizing on a recently proposed FD MIMO architecture combining digital transmit and receive beamforming with reduced complexity multi-tap analog Self-Interference (SI) cancellation, we propose a novel transmission scheme exploiting channel reciprocity for joint downlink beamformed information data communication and uplink channel estimation through training data transmission. We adopt a general model for pilot-assisted channel estimation and present a unified optimization framework for all involved FD MIMO design parameters. Our representative Monte Carlo simulation results for an example algorithmic solution for the beamformers as well as for the analog and digital SI cancellation demonstrate that the proposed FD-based joint communication and control scheme provides 1.4x the downlink rate of its half duplex counterpart. This performance improvement is achieved with 50% reduction in the hardware complexity for the analog canceller than conventional FD MIMO architectures with fully connected analog cancellation.
Accurate downlink channel information is crucial to the beamforming design, but it is difficult to obtain in practice. This paper investigates a deep learning-based optimization approach of the downlink beamforming to maximize the system sum rate, when only the uplink channel information is available. Our main contribution is to propose a model-driven learning technique that exploits the structure of the optimal downlink beamforming to design an effective hybrid learning strategy with the aim to maximize the sum rate performance. This is achieved by jointly considering the learning performance of the downlink channel, the power and the sum rate in the training stage. The proposed approach applies to generic cases in which the uplink channel information is available, but its relation to the downlink channel is unknown and does not require an explicit downlink channel estimation. We further extend the developed technique to massive multiple-input multiple-output scenarios and achieve a distributed learning strategy for multicell systems without an inter-cell signalling overhead. Simulation results verify that our proposed method provides the performance close to the state of the art numerical algorithms with perfect downlink channel information and significantly outperforms existing data-driven methods in terms of the sum rate.
The gains afforded by cloud radio access network (C-RAN) in terms of savings in capital and operating expenses, flexibility, interference management and network densification rely on the presence of high-capacity low-latency fronthaul connectivity between remote radio heads (RRHs) and baseband unit (BBU). In light of the non-uniform and limited availability of fiber optics cables, the bandwidth constraints on the fronthaul network call, on the one hand, for the development of advanced baseband compression strategies and, on the other hand, for a closer investigation of the optimal functional split between RRHs and BBU. In this chapter, after a brief introduction to signal processing challenges in C-RAN, this optimal function split is studied at the physical (PHY) layer as it pertains to two key baseband signal processing steps, namely channel estimation in the uplink and channel encoding/ linear precoding in the downlink. Joint optimization of baseband fronthaul compression and of baseband signal processing is tackled under different PHY functional splits, whereby uplink channel estimation and downlink channel encoding/ linear precoding are carried out either at the RRHs or at the BBU. The analysis, based on information-theoretical arguments, and numerical results yields insight into the configurations of network architecture and fronthaul capacities in which different functional splits are advantageous. The treatment also emphasizes the versatility of deterministic and stochastic successive convex approximation strategies for the optimization of C-RANs.
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