In this paper, we consider a distributed multi-input-multi-output (D-MIMO) system wherein multiple radio remote heads (RRHs) distributed in a cell are connected with a single baseband unit. To enable coherent joint transmission from multiple RRHs in the D-MIMO system, we propose several channel state information (CSI) codebooks as candidates for enhancements in the context of 3rd Generation Partnership Project (3GPP) 5G New Radio (NR) standardization. The proposed codebooks are developed based on the 5G Release-16 Type-II CSI codebook framework. In addition, we propose dynamic RRH selection (DRS) methods that are able to obtain performance gain and reduce the amount of feedback by sending the CSI only for the selected RRHs having dominant channel qualities. System-level simulation (SLS) results under realistic scenarios are provided to validate the potential of the proposed CSI codebooks and DRS methods.

High-resolution Type II CSI feedback is a key differentiator between 4G (LTE) and 5G (NR). The large feedback overhead of Type II CSI reporting, however, is a bottleneck for UE implementations in real deployments. In this paper, a space-frequency compression based Type II CSI overhead reduction has been proposed. The system-level simulation results are provided to show that the performance similar to Type II CSI is achievable with significantly reduced CSI overhead.

Enabling the high data rates of millimeter wave (mmWave) cellular systems requires deploying large antenna arrays at both the basestations and mobile users. Prior work on coverage and rate of mmWave cellular networks focused on the case when basestations and mobile beamforming vectors are predesigned for maximum beamforming gains. Designing beamforming/combining vectors, though, requires training, which may impact both the SINR coverage and rate of mmWave systems. This paper evaluates mmWave cellular network performance while accounting for the beam training/association overhead. First, a model for the initial beam association is developed based on beam sweeping and downlink control pilot reuse. To incorporate the impact of beam training, a new metric, called the effective reliable rate, is defined and adopted. Using stochastic geometry, the effective rate of mmWave cellular networks is derived for two special cases: near-orthogonal pilots and full pilot reuse. Analytical and simulation results provide insights into the answers of two important questions. First, what is the impact of beam association on mmWave network performance? Then, should orthogonal or reused pilots be employed? The results show that unless the employed beams are very wide, initial beam training with full pilot reuse is nearly as good as perfect beam alignment.

A concept of full-dimension MIMO (FD-MIMO) adopting 2-dimensional (2D) antenna array has been proposed with intent to cope with practical challenges of massive MIMO systems. In this paper, we investigate an FD-MIMO operation that can be transparently applied with current 3GPP LTE specifications, and propose a novel method that facilitates an accurate estimation of channel quality indicator (CQI) at base station (BS). We first introduce a feedback scheme in which horizontal and vertical dimension channel information are separately reported. To further improve accuracy of modulation and coding scheme (MCS) selection to maximize system throughput, we propose an enhanced feedback scheme that is helpful to more accurate CQI estimation at the BS. In the evaluation results, we show performance improvement of the proposed scheme over the conventional MIMO scheme.

We consider a video delivery problem in which a server multicasts a video to several users over a wireless broadcast channel. Users arrive and depart asynchronously, and when a user arrives, he/she wishes to watch the video from the beginning. Existing approaches for this problem assume that the server is either completely aware of the state of the users or is completely oblivious to their states, the former being most useful for small numbers of users and the latter being most useful for large networks. We propose a dynamic-programming-based scheduler that can operate in both of these regimes, as well as the intermediate regime in which feedback is available but incomplete. The scheduler is validated both numerically and experimentally.

The future wireless networks, such as Centralized Radio Access Network (C-RAN), will need to deliver data rate about 100 times to 1000 times the current 4G technology. For C-RAN based network architecture, there is a pressing need for tremendous enhancement of the effective data rate of the Common Public Radio Interface (CPRI). Compression of CPRI data is one of the potential enhancements. We introduce a vector quantization based compression algorithm for CPRI links, utilizing Lloyd algorithm. Methods to vectorize the I/Q samples and enhanced initialization of Lloyd algorithm for codebook training are investigated for improved performance. Multi-stage vector quantization is considered to reduce codebook search complexity. Simulation results show that our solution can achieve compression of 4 times for uplink and 4.5 times for downlink, within 2% Error Vector Magnitude (EVM) distortion. Remarkably, vector quantization codebook proves to be quite robust against data modulation mismatch, fading, signal-to-noise (SNR) and Doppler spread.

To support downlink full-dimension (FD-MIMO) in FDD scenarios, an accurate yet reasonably efficient channel-state-information (CSI) feedback scheme is required to harness the potential beamforming gain offered by the use of a large number of antenna elements in a 2D active antenna array. In this paper, a reduced-spaced channel quantization and feedback scheme which exploits slow variation of the angle-of-departure (AoD) distribution is proposed. By perceiving the instantaneous spatial channel to be a linear combination of slowly varying subset of matrices, it is demonstrated that the proposed scheme offers up to 62.5% dimensionality reduction with negligible throughput loss in 16-antenna FD-MIMO system. When used together with vector quantization, the proposed scheme offers substantial gain over that supported by 3GPP LTE.

We determine the rate region of the vector Gaussian one-helper source-coding problem under a covariance matrix distortion constraint. The rate region is achieved by a simple scheme that separates the lossy vector quantization from the lossless spatial compression. The converse is established by extending and combining three analysis techniques that have been employed in the past to obtain partial results for the problem.

We determine the rate region of the Gaussian one-helper source-coding problem in which the helper observes a scalar, the main encoder observes a vector, and the distortion constraint is a positive semidefinite upper bound on the error covariance matrix of the main source. The rate region is achieved by a Gaussian achievable scheme. We introduce a novel outer bounding technique to establish the converse. Our approach is to create a reduced dimensional problem by projecting the main source and the distortion constraint in certain directions determined by the optimal Gaussian scheme. We also provide an outer bound to the rate region of the more general problem in which there are distortion constraints on both sources. This outer bound is partially tight in general and completely tight in some nontrivial cases.

We study a hypothesis testing problem in which data are compressed distributively and sent to a detector that seeks to decide between two possible distributions for the data. The aim is to characterize all achievable encoding rates and exponents of the type 2 error probability when the type 1 error probability is at most a fixed value. For related problems in distributed source coding, schemes based on random binning perform well and are often optimal. For distributed hypothesis testing, however, the use of binning is hindered by the fact that the overall error probability may be dominated by errors in the binning process. We show that despite this complication, binning is optimal for a class of problems in which the goal is to “test against conditional independence.” We then use this optimality result to give an outer bound for a more general class of instances of the problem.

We determine the rate region of the Gaussian scalar-help-vector source-coding problem under a covariance matrix distortion constraint. The rate region is achieved by a Gaussian achievable scheme. We introduce a novel lower bounding technique to establish the converse of the main result. Our approach is based on lower bounding the problem with a potentially reduced dimensional problem by projecting the main source and imposing the distortion constraint in certain directions determined by the optimal Gaussian scheme. We also provide several properties that the optimal solution to the point-to-point rate-distortion problem for a vector Gaussian source under a covariance matrix distortion constraint satisfies. These properties play an important role in our converse proof.

We study the vector Gaussian versions of two problems: hypothesis testing under a communication constraint and the lossy one-helper problem. In the hypothesis testing problem, a test against independence is considered when a vector Gaussian source is available at the detector which receives a message about another vector Gaussian source at a specified rate. Two equivalent characterizations of the optimal type 2 error exponent are given when the type 1 error is at most a fixed constant. The first characterization is based on enhancement technique introduced by Weingarten et. al. and the other is transform-based. The transform-based characterization directly yields a water pouring interpretation, and establishes successive refinability. For the lossy one-helper problem, we determine a portion of the boundary of the rate region.