Massive MIMO 1-Bit DAC Transmission: A Low-Complexity Symbol Scaling Approach

We study multi-user massive multiple-input single-output (MISO) systems and focus on downlink transmission, where the base station (BS) employs a large antenna array with low-cost 1-bit digital-to-analog converters (DACs). The direct combination of existing beamforming schemes with 1-bit DACs is shown to lead to an error floor at medium-to-high SNR regime, due to the coarse quantization of the DACs with limited precision. In this paper, based on the constructive interference we consider both a quantized linear beamforming scheme where we analytically obtain the optimal beamforming matrix, and a non-linear mapping scheme where we directly design the transmit signal vector. Due to the 1-bit quantization, the formulated optimization for the non-linear mapping scheme is shown to be non-convex. To solve this problem, the non-convex constraints of the 1-bit DACs are firstly relaxed, followed by an element-wise normalization to satisfy the 1-bit DAC transmission. We further propose a low-complexity symbol scaling scheme that consists of three stages, in which the quantized transmit signal on each antenna element is selected sequentially. Numerical results show that the proposed symbol scaling scheme achieves a comparable performance to the optimization-based non-linear mapping approach, while its corresponding complexity is negligible compared to that of the non-linear scheme.Comment: 15 page

A reduced-complexity and asymptotically efficient time-delay estimator

This paper considers the problem of estimating the time delays of multiple replicas of a known signal received by an array of antennas. Under the assumptions that the noise and co-channel interference (CCI) are spatially colored Gaussian processes and that the spatial signatures are arbitrary, the maximum likelihood (ML) solution to the general time delay estimation problem is derived. The resulting criterion for the delays yields consistent and asymptotically efficient estimates. However, the criterion is highly non-linear, and not conducive to simple minimization procedures. We propose a new cost function that is shown to provide asymptotically efficient delay estimates. We also outline a heuristic way of deriving this cost function. The form of this new estimator lends itself to minimization by the computationally attractive iterative quadratic maximum likelihood (IQML) algorithm. The existence of simple yet accurate initialization schemes based on ESPRIT and identity weightings makes the approach viable for practical implementation.Peer ReviewedPostprint (published version

Joint Waveform and Filter Designs for STAP-SLP-based MIMO-DFRC Systems

Dual-function radar-communication (DFRC), which can simultaneously perform both radar and communication functionalities using the same hardware platform, spectral resource and transmit waveform, is a promising technique for realizing integrated sensing and communication (ISAC). Spacetime adaptive processing (STAP) in multi-antenna radar systems is the primary tool for detecting moving targets in the presence of strong clutter. The idea of joint spatial-temporal optimization in STAP-based radar systems is consistent with the concept of symbol-level precoding (SLP) for multi-input multi-output (MIMO) communications, which optimizes the transmit waveform for each of the transmitted symbols. In this paper, we combine STAP and SLP and propose a novel STAP-SLP-based DFRC system that enjoys the advantages of both techniques. The radar output signal-to-interference-plus-noise ratio (SINR) is maximized by jointly optimizing the transmit waveform and receive filter, while satisfying the communication quality-of-service (QoS) constraint and various waveform constraints including constant-modulus, similarity and peak-to-average power ratio (PAPR). An efficient algorithm framework based on majorization-minimization (MM) and nonlinear equality constrained alternative direction method of multipliers (neADMM) methods is proposed to solve these complicated non-convex optimization problems. Simulation results verify the effectiveness of the proposed STAP-SLP-based MIMO-DRFC scheme and the associate algorithms.Comment: accepted by IEEE Journal on Selected Areas in Communication

Downlink Achievable Rate Analysis in Massive MIMO Systems with One-Bit DACs

In this letter, we investigate the downlink performance of massive multiple-input multiple-output (MIMO) systems where the base station is equipped with one-bit analog-to-digital/digital-to-analog converters (ADC/DACs). We assume that the base station employs the linear minimum mean-squared-error channel estimator and treats the channel estimate as the true channel to precode the data symbols. We derive an expression for the downlink achievable rate for matched-filter precoding. A detailed analysis of the resulting power efficiency is pursued using our expression of the achievable rate. Numerical results are presented to verify our analysis. In particular, it is shown that, compared with conventional massive MIMO systems, the performance loss in one-bit massive MIMO systems can be compensated for by deploying approximately 2.5 times more antennas at the BS

How much training is needed in one-bit massive MIMO systems at low SNR?

This paper considers training-based transmissions in massive multi-input multi-output (MIMO) systems with one-bit analog-to-digital converters (ADCs). We assume that each coherent transmission block consists of a pilot training stage and a data transmission stage. The base station (BS) first employs the linear minimum mean-square-error (LMMSE) method to estimate the channel and then uses the maximum-ratio combining (MRC) receiver to detect the data symbols. We first obtain an approximate closed-form expression for the uplink achievable rate in the low SNR region. Then based on the result, we investigate the optimal training length that maximizes the sum spectral efficiency for two cases: i) The training power and the data transmission power are both optimized; ii) The training power and the data transmission power are equal. Numerical results show that, in contrast to conventional massive MIMO systems, the optimal training length in one-bit massive MIMO systems is greater than the number of users and depends on various parameters such as the coherence interval and the average transmit power. Also, unlike conventional systems, it is observed that in terms of sum spectral efficiency, there is relatively little benefit to separately optimizing the training and data power

Channel estimation and uplink achievable rates in one-bit massive MIMO systems

This paper considers channel estimation and achievable rates for the uplink of a massive multiple-input multiple-output (MIMO) system where the base station is equipped with one-bit analog-to-digital converters (ADCs). By rewriting the nonlinear one-bit quantization using a linear expression, we first derive a simple and insightful expression for the linear minimum mean-square-error (LMMSE) channel estimator. Then employing this channel estimator, we derive a closed-form expression for the lower bound of the achievable rate for the maximum ratio combiner (MRC) receiver. Numerical results are presented to verify our analysis and show that our proposed LMMSE channel estimator outperforms the near maximum likelihood (nML) estimator proposed previously