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.
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.
In this paper, data-transmission using the nonlinear Fourier transform for jointly modulated discrete and continuous spectra is investigated. A recent method for purely discrete eigenvalue removal at the detector is extended to signals with additional continuous spectral support. At first, the eigenvalues are sequentially detected and removed from the jointly modulated received signal. After each successful removal, the time-support of the resulting signal for the next iteration can be narrowed, until all eigenvalues are removed. The resulting truncated signal, ideally containing only continuous spectral components, is then recovered by a standard NFT algorithm. Numerical simulations without a fiber channel show that, for jointly modulated discrete and continuous spectra, the mean-squared error between transmitted and received eigenvalues can be reduced using the eigenvalue removal approach, when compared to state-of-the-art detection methods. Additionally, the computational complexity for detection of both spectral components can be decreased when, by the choice of the modulated eigenvalues, the time-support after each removal step can be reduced. Numerical simulations are also carried out for transmission over a Raman-amplified, lossy SSMF channel. The mutual information is approximated and the eigenvalue removal method is shown to result in achievable rate improvements.
Achievable information rates of bipolar 4-and 8-ary constellations are experimentally compared to those of intensity modulation (IM) when using an oversampled direct detection receiver. The bipolar constellations gain up to 1.8 dB over their IM counterparts.
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