A key challenge of future mobile communication research is to strike an attractive compromise between wireless network's area spectral efficiency and energy efficiency. This necessitates a clean-slate approach to wireless system design, embracing the rich body of existing knowledge, especially on multiple-input-multiple-ouput (MIMO) technologies. This motivates the proposal of an emerging wireless communications concept conceived for single-radio-frequency (RF) large-scale MIMO communications, which is termed as SM. The concept of SM has established itself as a beneficial transmission paradigm, subsuming numerous members of the MIMO system family. The research of SM has reached sufficient maturity to motivate its comparison to state-of-the-art MIMO communications, as well as to inspire its application to other emerging wireless systems such as relay-aided, cooperative, small-cell, optical wireless, and power-efficient communications. Furthermore, it has received sufficient research attention to be implemented in testbeds, and it holds the promise of stimulating further vigorous interdisciplinary research in the years to come. This tutorial paper is intended to offer a comprehensive state-of-the-art survey on SM-MIMO research, to provide a critical appraisal of its potential advantages, and to promote the discussion of its beneficial application areas and their research challenges leading to the analysis of the technological issues associated with the implementation of SM-MIMO. The paper is concluded with the description of the world's first experimental activities in this vibrant research field.

https://hal-supelec.archives-ouvertes.fr/hal-00840278/document

Spatial Modulation for Generalized MIMO: Challenges, Opportunities, and Implementation

"Written by the pioneers of the concept, this is the first complete guide to the physical and engineering principles of Massive MIMO. Assuming only a basic background in communications and statisti ...

Fundamentals of Massive MIMO

A key challenge of future mobile communication research is to strike an attractive compromise between wire- less network's area spectral efficiency and energy efficiency. This necessitates a clean-slate approach to wireless system design, embracing the rich body of existing knowledge, espe- cially on multiple-input-multiple-ouput (MIMO) technologies. This motivates the proposal of an emerging wireless commu- nications concept conceived for single-radio-frequency (RF) large-scale MIMO communications, which is termed as SM. The concept of SM has established itself as a beneficial transmission paradigm, subsuming numerous members of the MIMO system family. The research of SM has reached sufficient maturity to motivate its comparison to state-of-the-art MIMO communica- tions, as well as to inspire its application to other emerging wireless systems such as relay-aided, cooperative, small-cell, optical wireless, and power-efficient communications. Further- more, it has received sufficient research attention to be im- plemented in testbeds, and it holds the promise of stimulating further vigorous interdisciplinary research in the years to come. This tutorial paper is intended to offer a comprehensive state-of-the-art survey on SM-MIMO research, to provide a critical appraisal of its potential advantages, and to promote the discussion of its beneficial application areas and their research challenges leading to the analysis of the technological issues associated with the implementation of SM-MIMO. The paper is concluded with the description of the world's first experimental activities in this vibrant research field.

Spatial Modulation for Generalized MIMO: Challenges, Opportunities, and Implementation This tutorial paper offers a comprehensive overview of the state of the art in spatial modulation for generalized multiple-input-multiple-output (MIMO) technologies.

This paper proposes a unified transmission strategy for multiuser time division duplex (TDD)/frequency division duplex (FDD) massive multiple-input–multiple-output (MIMO) systems, including uplink (UL)/downlink (DL) channel estimation and user scheduling for data transmission. With the aid of antenna array theory and array signal processing, we build a spatial basis expansion model (SBEM) to represent the UL/DL channels with far fewer parameter dimensions. Hence, both the UL and DL channel estimations of multiusers can be carried out with a small amount of training resource, which significantly reduces the training overhead and feedback cost. Meanwhile, the pilot contamination problem in the UL training is immediately relieved by exploiting the spatial information of users. To enhance the spectral efficiency, we also design a greedy user scheduling scheme during the data transmission period. Compared with existing low-rank models, the newly proposed SBEM offers an alternative for channel acquisition without the need for channel statistics and can be applied to both TDD and FDD systems. Various numerical results are provided to corroborate the proposed studies.

A Unified Transmission Strategy for TDD/FDD Massive MIMO Systems With Spatial Basis Expansion Model

The concept of deploying a large number of antennas at the base station, often called massive multiple-input multiple-output (MIMO), has drawn considerable interest because of its potential ability to revolutionize current wireless communication systems. Most literature on massive MIMO systems assumes time division duplexing (TDD), although frequency division duplexing (FDD) dominates current cellular systems. Due to the large number of transmit antennas at the base station, currently standardized approaches would require a large percentage of the precious downlink and uplink resources in FDD massive MIMO be used for training signal transmissions and channel state information (CSI) feedback. To reduce the overhead of the downlink training phase, we propose practical open-loop and closed-loop training frameworks in this paper. We assume the base station and the user share a common set of training signals in advance. In open-loop training, the base station transmits training signals in a round-robin manner, and the user successively estimates the current channel using long-term channel statistics such as temporal and spatial correlations and previous channel estimates. In closed-loop training, the user feeds back the best training signal to be sent in the future based on channel prediction and the previously received training signals. With a small amount of feedback from the user to the base station, closed-loop training offers better performance in the data communication phase, especially when the signal-to-noise ratio is low, the number of transmit antennas is large, or prior channel estimates are not accurate at the beginning of the communication setup, all of which would be mostly beneficial for massive MIMO systems.

Downlink Training Techniques for FDD Massive MIMO Systems: Open-Loop and Closed-Loop Training With Memory

Accurate channel state information (CSI) is essential for attaining beamforming gains in single-user (SU) multiple-input multiple-output (MIMO) and multiplexing gains in multi-user (MU) MIMO wireless communication systems. State-of-the-art limited feedback schemes, which rely on pre-defined codebooks for channel quantization, are only appropriate for a small number of transmit antennas and low feedback overhead. In order to scale informed transmitter schemes to emerging massive MIMO systems with a large number of transmit antennas at the base station, one common approach is to employ time division duplexing (TDD) and to exploit the implicit feedback obtained from channel reciprocity. However, most existing cellular deployments are based on frequency division duplexing (FDD), hence it is of great interest to explore backwards compatible massive MIMO upgrades of such systems. For a fixed feedback rate per antenna, the number of codewords for quantizing the channel grows exponentially with the number of antennas, hence generating feedback based on look-up from a standard vector quantized codebook does not scale. In this paper, we propose noncoherent trellis-coded quantization (NTCQ), whose encoding complexity scales linearly with the number of antennas. The approach exploits the duality between source encoding in a Grassmannian manifold (for finding a vector in the codebook which maximizes beamforming gain) and noncoherent sequence detection (for maximum likelihood decoding subject to uncertainty in the channel gain). Furthermore, since noncoherent detection can be realized near-optimally using a bank of coherent detectors, we obtain a low-complexity implementation of NTCQ encoding using an off-the-shelf Viterbi algorithm applied to standard trellis coded quantization. We also develop advanced NTCQ schemes which utilize various channel properties such as temporal/spatial correlations. Monte Carlo simulation results show the proposed NTCQ and its extensions can achieve near-optimal performance with moderate complexity and feedback overhead.

Noncoherent Trellis Coded Quantization: A Practical Limited Feedback Technique for Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) techniques are regarded as a potential breakthrough in cellular network design, with transmit precoding to multiple downlink users playing a central role in the projected capacity gains. Most prior work assumes that the channel estimates required for precoder design are obtained by exploiting reciprocity in time division duplexed (TDD) systems. However, most cellular networks today are frequency division duplexed (FDD), for which reciprocity based channel side information (CSI) is not available, hence explicit channel feedback from mobiles to base station is required for downlink precoding. Conventional “limited feedback” approaches, based on quantizing the channel coefficients based on a predefined vector quantization codebook, do not scale to massive MIMO because of computational complexity and lack of flexibility. We propose a novel noncoherent trellis-coded quantization (NTCQ) which uses the duality between noncoherent sequence detection and vector quantization for maximizing beamforming gain. The advantages of NTCQ include linear complexity in the number of transmit antennas, with vector quantization accomplished using an off-the-shelf Viterbi algorithm, and flexibility in adjusting the feedback overhead to support a variable number of transmit antennas without changing the structure of the quantizer.

Noncoherent trellis-coded quantization for massive MIMO limited feedback beamforming

Linear coding schemes have been the main choice of coding for the additive white Gaussian noise broadcast channel (AWGN-BC) with noiseless feedback in the literature. The achievable rate regions of these schemes go well beyond the capacity region of the AWGN-BC without feedback. In this paper, a concatenated coding design for the $K$ -user AWGN-BC with noisy feedback is proposed that relies on linear schemes as inner codes to achieve rate tuples outside the no-feedback capacity region. The boundary of an achievable rate region of purely linear coding schemes for noiseless feedback is shown to be arbitrarily closely approached by the concatenated coding scheme for sufficiently small feedback noise levels. Then, a linear coding scheme for the $K$ -user symmetric AWGN-BC with noisy feedback is presented and optimized for use in the concatenated coding scheme. For noiseless feedback, the presented linear coding scheme achieves the optimal sum-rate of linear-feedback coding. An upper bound on the inner code blocklength needed to achieve a sum-rate above no-feedback sum-capacity is derived.

Concatenated Coding Using Linear Schemes for Gaussian Broadcast Channels With Noisy Channel Output Feedback

In the field of sensing, a typically unavoidable nuisance is the inherent bias of a sensor due to imperfections in timing, calibration, and other sources. The errors incurred by the bias ripple through higher-level processes such as tracking and sensor fusion, causing varying effects to each operation. In many different applications, such as track-to-track correlation, the overall effect of the biases on state estimation is modeled as a constant, translational shift in the position dimension of the track states. This assumption can be appropriate when the required precision of the track states is not stringent. However, in general, sensor bias can not only affect position estimates but also positional derivatives, i.e., velocity, acceleration, in a manner that can change dramatically depending on sensor-target geometry; for situations where high state estimation accuracy is required, these consequences become apparent and need to be handled. The contribution from measurement bias to state estimation error depends on many different aspects, e.g., measurement uncertainty, dynamic model uncertainty, sensor-target geometry. The focus of this work is the quantification of the relative significance of measurement error and measurement bias in the resultant state estimation error. In short, using the results in this work, it is straightforward to: (i) determine regimes where measurement bias becomes a predominant factor, (ii) bound the impact of the sensor bias on the outputted tracking information, (iii) analyze the dependence of the tracking error on sensor-target geometry, all of which can be of great impact when designing a tracking system architecture.

Error statistics of bias-naïve filtering in the presence of bias

—Target characterization is an important step in many defense missions, often relying on ﬁtting a known target model to observed data. Optimization of model parameters can be computationally expensive depending on the model complexity, thus having models that both describe the data well and that can be efﬁciently optimized is critical. This work introduces a class of radar models that can be used to represent the radar scattering response of a target at high frequencies while also enabling the use of gradient-based optimization.

Zachary Chance

Papers

Noncoherent Trellis Coded Quantization: A Practical Limited Feedback Technique for Massive MIMO Systems

Noncoherent trellis-coded quantization for massive MIMO limited feedback beamforming

Concatenated Coding Using Linear Schemes for Gaussian Broadcast Channels With Noisy Channel Output Feedback

Error statistics of bias-naïve filtering in the presence of bias

Differentiable Point Scattering Models for Efficient Radar Target Characterization