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Massive MIMO plays an important role for future cellular networks since the large number of antenna elements is capable of increasing the spectral efficiency and the amount of usable spectrum. The 1-bit analog-to-digital converters can drastically reduce the resulting complexity and power consumption. Therefore, we investigate the Direction of Arrival (DoA) estimation using 1-bit measurements of many antenna elements in this paper. We extend Binary Iterative Hard Thresholding (BIHT), an efficient sparse recovery algorithm from the area of Compressed Sensing (CS) that takes the 1-bit quantization explicitly into account, to complex-valued signals and multiple measurement vectors such that it is applicable to 1-bit DoA estimation with multiple snapshots. The comparison of the resulting Complex-valued BIHT (CBIHT) algorithm to subspace-and CSbased methods in terms of both DoA estimation performance and computational complexity demonstrates that CBIHT is well suited for scenarios with many antenna elements and a few snapshots.

This paper addresses the problem of data detection for a massive Multiple-Input-Multiple-Output (MIMO) base station which utilizes 1-bit Analog-to-Digital Converters (ADCs) for quantizing the uplink signal. The existing literature on quantized massive MIMO systems deals with Cyclic Prefix (CP) transmission over frequency-selective channels. In this paper, we propose a computationally efficient block processing equalizer based on the Expectation Maximization (EM) algorithm in CPfree transmission for 1-bit quantized systems. We investigate the optimal block length and overlapping factor in relation to the Channel Impulse Response (CIR) length based on the Bit Error-Rate (BER) performance metric.As EM is a non-linear algorithm, the optimal estimate is found iteratively depending on the initial starting point of the algorithm. Through numerical simulations we show that initializing the EMalgorithm with a Wiener-Filter (WF) estimate, which takes the underlying quantization into account, achieves superior BERperformance compared to initialization with other starting points.

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The use of low resolution Analog to Digital Converters (ADCs) can significantly reduce the power consumption for massive Multiple Input Multiple Output (MIMO) systems. The existing literature on quantized massive MIMO systems deals with Cyclic Prefix (CP) transmission schemes in frequencyselective fading channels. In this paper, we propose a block processing Frequency Domain Equalization (FDE) technique in CP-free transmission schemes for massive MIMO systems having low resolution ADCs. The optimal block length for FDE is found by minimizing a computational complexity cost function and taking quantization distortion, channel impulse response and the number of transmit and receiver antennas into account. Through numerical simulation, it is shown that the optimal block length also guarantees good performance in terms of the Mean Square Error (MSE) and Bit Error-Rate (BER) criterion.

Abstract-Improving the power efficiency and spectral efficiency of communication systems has been one of the major research goals over the recent years. However, there is a tradeoff in achieving both goals at the same time. In this work, we consider the joint optimization of the power amplifier and a pulse shaping filter over a single-input single-output (SISO) additive white Gaussian noise (AWGN) channel using 1-bit analog-todigital (ADC) and digital-to-analog (DAC) converters. The goal of the optimization is the selection of the optimal system parameters in order to maximize the desired figure-of-merit (FOM) which is the product of power efficiency and spectral efficiency. Simulation results give an insight in choosing the optimal parameters of the pulse shaping filter and power amplifier to maximize the desired FOM.

We consider a downlink 1-bit quantized multiuser (MU) multiple-input-multiple-output (MIMO) system, where 1-bit digital-to-analog (DACs) and analog-to-digital converters (ADCs) are used at the transmitter and the receiver for economical and computational efficiency. We end up with a discrete memoryless channel with input and output vectors belonging to the QPSK constellation. In the context of massive (MIMO) systems the number of base station (BS) antennas is much larger than the number of receive antennas. This leads to high input cardinality of the channel. In this work we introduce a method to reduce the input set based on the mimimum bit-error-ratio (BER) criterion combined with a non-linear precoding technique. This method is denoted as spatial coding. Simulations show that this spatial coding improves the BER behavior significantly removing the error floor due to coarse quantization.

A long-haul transmission of 100 Gb/s without optical chromatic-dispersion (CD) compensation provides a range of benefits regarding cost effectiveness, power budget, and nonlinearity tolerance. The channel memory is largely dominated by CD in this case with an intersymbol-interference spread of more than 100 symbol durations. In this paper, we propose CD equalization technique based on nonmaximally decimated discrete Fourier transform (NMDFT) filter bank (FB) with non-trivial prototype filter and complex-valued infinite impulse response (IIR) all-pass filter per sub-band. The design of the sub-band IIR all-pass filter is based on minimizing the mean square error (MSE) in group delay and phase cost functions in an optimization framework. Necessary conditions are derived and incorporated in a multi-step and multi-band optimization framework to ensure the stability of the resulting IIR filter. It is shown that the complexity of the proposed method grows logarithmically with the channel memory, therefore, larger CD values can be tolerated with our approach.