Information Theory and Reliable Communication

Digital receivers are required to recover the transmitted symbols from their observed channel output. In multiuser multiple-input multiple-output (MIMO) setups, where multiple symbols are simultaneously transmitted, accurate symbol detection is challenging. A family of algorithms capable of reliably recovering multiple symbols is based on interference cancellation. However, these methods assume that the channel is linear, a model which does not reflect many relevant channels, as well as require accurate channel state information (CSI), which may not be available. In this work we propose a multiuser MIMO receiver which learns to jointly detect in a data-driven fashion, without assuming a specific channel model or requiring CSI. In particular, we propose a data-driven implementation of the iterative soft interference cancellation (SIC) algorithm which we refer to as DeepSIC. The resulting symbol detector is based on integrating dedicated machine-learning methods into the iterative SIC algorithm. DeepSIC learns to carry out joint detection from a limited set of training samples without requiring the channel to be linear and its parameters to be known. Our numerical evaluations demonstrate that for linear channels with full CSI, DeepSIC approaches the performance of iterative SIC, which is comparable to the optimal performance, and outperforms previously proposed learning-based MIMO receivers. Furthermore, in the presence of CSI uncertainty, DeepSIC significantly outperforms model-based approaches. Finally, we show that DeepSIC accurately detects symbols in non-linear channels, where conventional iterative SIC fails even when accurate CSI is available.

DeepSIC: Deep Soft Interference Cancellation for Multiuser MIMO Detection

In this paper, we design an uplink/downlink channel estimation method for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems and investigate the impact of beam squint effect that accompanies large array configuration. Specifically, we adopt the off-grid sparse Bayesian learning (SBL) that directly works on the continuous angle-delay parameter domain and avoids the grid mismatch problem. Hence, the proposed method achieves good channel estimation accuracy and handles the wideband direction of arrival (DOA) estimation problem for mmWave massive MIMO communications, where beam squint effect was previously ignored by many existing literatures. The Cramer-Rao bound for unknown parameters is derived to make the proposed study complete. More importantly, a much simplified downlink channel estimation scheme is designed with the aid of angle-delay reciprocity , which significantly reduces training and feedback overhead. The simulation results are provided to demonstrate the superior performance of the proposed method over existing ones.

Angle-Domain Aided UL/DL Channel Estimation for Wideband mmWave Massive MIMO Systems With Beam Squint

Digital receivers are required to recover the transmitted symbols from their observed channel output. In multiuser multiple-input multiple-output (MIMO) setups, where multiple symbols are simultaneously transmitted, accurate symbol detection is challenging. A family of algorithms capable of reliably recovering multiple symbols is based on interference cancellation. However, these methods assume that the channel is linear, a model which does not reflect many relevant channels, as well as require accurate channel state information (CSI), which may not be available. In this work we propose a multiuser MIMO receiver which learns to jointly detect in a data-driven fashion, without assuming a specific channel model or requiring CSI. In particular, we propose a data-driven implementation of the iterative soft interference cancellation (SIC) algorithm which we refer to as DeepSIC. The resulting symbol detector is based on integrating dedicated machine-learning (ML) methods into the iterative SIC algorithm. DeepSIC learns to carry out joint detection from a limited set of training samples without requiring the channel to be linear and its parameters to be known. Our numerical evaluations demonstrate that for linear channels with full CSI, DeepSIC approaches the performance of iterative SIC, which is comparable to the optimal performance, and outperforms previously proposed ML-based MIMO receivers. Furthermore, in the presence of CSI uncertainty, DeepSIC significantly outperforms model-based approaches. Finally, we show that DeepSIC accurately detects symbols in non-linear channels, where conventional iterative SIC fails even when accurate CSI is available.

Building on the ubiquity of electric power infrastructure, power line communications (PLC) has been successfully used in diverse application scenarios, including the smart grid and in-home broadband communications systems as well as industrial and home automation. However, the power line channel exhibits deleterious properties, one of which is its hostile noise environment. This article aims for providing a review of noise modeling and mitigation techniques in PLC. Specifically, a comprehensive review of representative noise models developed over the past fifty years is presented, including both the empirical models based on measurement campaigns and simplified mathematical models. Following this, we provide an extensive survey of the suite of noise mitigation schemes, categorizing them into mitigation at the transmitter as well as parametric and non-parametric techniques employed at the receiver. Furthermore, since the accuracy of channel estimation in PLC is affected by noise, we review the literature of joint noise mitigation and channel estimation solutions. Finally, a number of directions are outlined for future research on both noise modeling and mitigation in PLC.

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Fifty Years of Noise Modeling and Mitigation in Power-Line Communications

Communications over power lines in the frequency range above 2 MHz, commonly referred to as broadband (BB) power line communications (PLC), has been the focus of increasing research attention and standardization efforts in recent years. BB-PLC channels are characterized by a dominant colored non-Gaussian additive noise, as well as by periodic variations of the channel impulse response and of the noise statistics. In this paper, we study the fundamental rate limits for BB-PLC channels by bounding their capacity while accounting for the unique properties of these channels. We obtain explicit expressions for the derived bounds for several BB-PLC noise models, and illustrate the resulting fundamental limits in a numerical analysis.

On the Capacity of MIMO Broadband Power Line Communications Channels

In many communications scenarios, the channel exhibits periodic characteristics, e.g., power line communications and interference-limited communications. Additionally, certain approximations for mobile radio channels over finite time intervals may result in periodic channel models. In this paper, we study pilot-aided joint estimation of the channel impulse response (CIR) and of the carrier frequency offset (CFO) for linear periodic channels, in which both the CIR and the noise statistics vary periodically in time. We first consider the joint maximum likelihood estimator (JMLE) for the CIR and the CFO, and discuss the practical drawbacks associated with this estimator. When the coefficients of the delay-Doppler spread function of the CIR are approximately sparse, we propose two estimation schemes with higher spectral efficiency and lower computational complexity compared with the JMLE, which are obtained by exploiting both the periodicity and the sparsity of the channel, without requiring a priori knowledge of the sparsity pattern. Finally, we study the design of pilot sequences aimed at improving the estimation performance in sparse periodic channels. Simulation studies corresponding to practical scenarios of the proposed estimators demonstrate that substantial benefits can be obtained by properly accounting for the sparsity and periodicity in the design of estimation schemes.

Joint Estimation of Carrier Frequency Offset and Channel Impulse Response for Linear Periodic Channels

Periodic characteristics arise in many communications scenarios. Two major examples are interference-limited communications and power line communications. The most general point-to-point periodic channel model includes periodic variations of the channel transfer function and of the noise statistics. In this work we study carrier frequency offset estimation for linear channels with periodic characteristics. We design a maximum likelihood estimator (MLE), analytically characterize its asymptotic performance, and provide guidelines for its low-complexity implementation. We compare the strengths and weaknesses of the new estimator to those of an ad-hoc extended estimator obtained by adapting an MLE, originally designed for time-invariant channels, to periodically time-varying channels via a time partitioning approach. We numerically evaluate the performance of the new estimator and of the ad-hoc estimator, and illustrate the gain of rigorously accounting for the periodic characteristics of the channel, as opposed to the currently prevailing ad-hoc approach.

Carrier frequency offset estimation for linear channels with periodic characteristics

We study joint estimation of the channel impulse response (CIR) and of the carrier frequency offset (CFO) for linear channels in which both the CIR and the noise statistics vary periodically in time. This model corresponds to interference-limited communications as well as to power line communication and doubly selective channels. We first consider the joint maximum likelihood estimator (JMLE) for the CIR and the CFO and show it has a high computational complexity and relatively low spectral efficiency. This motivates the derivation of two estimation schemes with higher spectral efficiency and lower computational complexity compared to the JMLE, obtained by exploiting both the periodicity of the channel and the fact that, typically, the delay-Doppler spreading function of the CIR is approximately sparse, without requiring a-priori knowledge of the sparsity pattern. The proposed estimation schemes are numerically tested and the results demonstrate that substantial benefits can be obtained by properly accounting for the approximate sparsity and periodicity in the design of the estimation scheme.

Joint carrier frequency offset and channel impulse response estimation for linear periodic channels

Communications over power lines in the frequency range above 2 MHz, commonly referred to as broadband (BB) power line communications (PLC), is a central communications scenario for smart power grids. BB-PLC channels are characterized by a dominant colored non-Gaussian additive noise, as well as by periodic variations of the channel impulse response and the noise statistics, induced by the mains voltage. In this work we study the fundamental rate limits for multiple input-multiple output (MIMO) BB-PLC channels, modeled as periodic channels with additive non-Gaussian noise and finite memory. We present bounds on the capacity of these channels by exploiting a bijection with time-invariant MIMO channels of extended dimensions. We illustrate the resulting fundamental limits in a numerical analysis corresponding to practical MIMO BB-PLC channels.

Roee Shaked

Papers

On the Capacity of MIMO Broadband Power Line Communications Channels

Joint Estimation of Carrier Frequency Offset and Channel Impulse Response for Linear Periodic Channels

Carrier frequency offset estimation for linear channels with periodic characteristics

Joint carrier frequency offset and channel impulse response estimation for linear periodic channels

Bounds on the Capacity of MIMO Broadband Power Line Communications Channels