Spatio-Temporal Waveform Design for Multiuser Massive MIMO Downlink With 1-bit Receivers

Abstract

Abstract-Internet-of-Things (IoT) refers to a high-density network of low-cost low-bitrate terminals and sensors where also low energy consumption is one central feature. As the powerbudget of classical receiver chains is dominated by the highresolution analog-to-digital converters (ADCs), there is a growing interest towards deploying receiver architectures with reducedbit or even 1-bit ADCs. In this paper, we study waveform design, optimization and detection aspects of multi-user massive MIMO downlink where user terminals adopt very simple 1-bit ADCs with oversampling. In order to achieve spectral efficiency higher than 1 bit/s/Hz per real-dimension, and per receiver antenna, we propose a two-stage precoding structure, namely a novel quantization precoder followed by maximum-ratio transmission (MRT) or zero-forcing (ZF) type spatial channel precoder which jointly form the multi-user-multiantenna transmit waveform. The quantization precoder outputs are designed and optimized, under appropriate transmitter and receiver filter bandwidth constraints, to provide controlled inter-symbol-interference (ISI) enabling the input symbols to be uniquely detected from 1-bit quantized observations with a low-complexity symbol detector in the absence of noise. An additional optimization constraint is also imposed in the quantization precoder design to increase the robustness against noise and residual inter-user-interference (IUI). The purpose of the spatial channel precoder, in turn, is to suppress the IUI and provide high beamforming gains such that good symbol-error rates (SERs) can be achieved in the presence of noise and interference. Extensive numerical evaluations illustrate that the proposed spatio-temporal precoder based multiantenna waveform design can facilitate good multiuser link performance, despite the extremely simple 1-bit ADCs in the receivers, hence being one possible enabling technology for the future low-complexity IoT networks.Index Terms-1-bit ADC, 5G, energy-efficiency, internet-ofthings (IoT), low-cost, massive MIMO, multi-dimensional waveform design and optimization, quantization, sensor receivers, spatio-temporal precoding.

1-bit quantization and oversampling at the receiver: Sequence-based communication

“…For the high SNR regime, channel state information can be exploited at the transmitter for a channel inversion strategy for the construction of receive signals appropriate for 1-bit quantization. Moreover, the sequence design approach described in [11] for the singleinput single-output channel has been recently extended for the massive multiple-input single-output scenario in [15] and for the massive MIMO scenario in [16].…”

Zero-Crossing Precoding with Maximum Distance to the Decision Threshold for Channels with 1-Bit Quantization and Oversampling

“…The proposed waveform is presented in a flexible fashion with variable signaling rate including faster-than-Nyquist signaling and variable sampling rate. Numerical results show a superior performance in terms of BER in comparison to the state-of-the-art method [11].…”

“…A more practical approach was presented in [6], where filter coefficients are optimized for a 16-QAM transmission, in terms of the maximization of the minimum distance to the decision threshold, which is widely used also in other studies with quantization [7][8][9]. Later the approach in [6] was improved and extended for the multipleinput-single-output channel [10] and a more advanced waveform design was proposed for the multiuser multiple-input multiple-output (MIMO) downlink in [11], with a separate precoding for time and space.…”

“…In the presence of a design restriction by bandlimitation, a desired output pattern with less zero-crossings is promising for the waveform optimization. Different to the existing method [11] precoding and detection process are no longer symbol-by-symbol operations and rely on the sign of the previous encoded symbol and received symbol, respectively. Moreover, a Gray code is devised for the zero-crossing modulation.…”

Large-Scale Mimo Mc-Cdma System Using Combined Multiple Beamforming and Spatial Multiplexing

“…Various schemes for spatial multiplexing systems have been proposed [7]. Channel capacity maximization can be achieved by the water-filling solution using the SVD (Singular Value Decomposition) of the channel matrix [8], or other channel matrix precoding design techniques [8], [9].…”