Massive multiple-input multiple-output (MIMO) systems have gained a lot of attention due to their notable benefits with respect to spectral and energy efficiency. In order to make these systems more cost and power-efficient, using low-resolution digital-to-analog converters (DAC) at each antenna is a preferred implementation method. However, using such low-resolution DACs can introduce quantization distortion which needs to be mitigated by employing various quantized precoding algorithms. In this study, an iterative oversampling precoder (IDAPR) that can be used with quantized massive MIMO under Rician fading channels is presented. It is demonstrated IDAPR outperforms existing oversampling precoders of similar computational complexity with respect to bit error-rate and/or out-of-band (OOB) emissions under spatially correlated mmWave Rician fading channels. Additionally, it is shown that IDAPR can achieve the performance of unquantized precoders for many cases.

Massive multiple-input multiple-output (MIMO) systems have received significant attention in academia and industry for their remarkable advantages regarding spectral and energy efficiency. Employing a pair of low-resolution digital-to-analog converters (DAC) at each antenna of the massive MIMO array is a cost and power efficient implementation scheme for such systems. To mitigate the resulting quantization distortion introduced by low-resolution DAC, various quantized precoding algorithms are proposed in the related literature. This study proposes an iterative distortion-aware precoder that can work with oversampling DACs in massive MIMO systems. We show that the precoder we propose outperforms the existing oversampling precoders of comparable computational complexity in the literature in terms of error-rate and/or out-of-band (OOB) emission performances. We also show that the proposed detector can perform close to the ideal unquantized zero-forcing precoder in many presented cases.

Employing low resolution analog-to-digital converters in massive multiple-input multiple-output (MIMO) has many advantages in terms of total power consumption, cost and feasibility. However, such advantages come together with significant challenges in channel estimation and data detection due to the severe quantization noise present. In this study, we first derive linear minimum mean-square-error channel estimator for cyclic-prefix (CP) free single carrier MIMO (SC-MIMO) under frequency-selective channel. Then, we propose a novel iterative receiver for quantized CP-free uplink wideband SC-MIMO for uncorrelated or correlated Rayleigh and Rician fading channels. This detector utilizes an efficient message passing algorithm based on Bussgang decomposition, reduced state sequence estimation and Ungerboeck factorization. Therefore, it achieves remarkable complexity reduction and exhibits significant performance advantages compared to the existing quantized SC-MIMO receivers from the literature.

Massive multiple-input multiple-output (MIMO) systems have attracted much attention lately due to the many advantages they provide over single-antenna systems. Owing to the many antennas, low-cost implementation and low power consumption per antenna are desired. To that end, massive MIMO structures with low-resolution analog-to-digital converters (ADC) have been investigated in many studies. However, the effect of a strong interferer in the adjacent band on quantized massive MIMO systems have not been examined yet. In this study, we analyze the performance of uplink massive MIMO with low-resolution ADCs under frequency selective fading with orthogonal frequency division multiplexing (OFDM) in the perfect and imperfect receiver channel state information cases. We derive analytical expressions for the bit error rate and ergodic capacity. We show that the interfering band can be suppressed by increasing the number of antennas or the oversampling rate when a zero-forcing receiver is employed.

Massive multiple-input multiple-output (MIMO) systems have attracted much attention lately due to the many advantages they provide over single- antenna systems. Owing to the many antennas, low- cost implementation and low power consumption per antenna are desired. To that end, massive MIMO structures with one-bit analog-to-digital converters (ADC) have been investigated in many studies. However, the effect of a strong interferer in the adjacent band on one-bit quantized massive MIMO systems have not been examined yet. In this study, we analyze the performance of uplink massive MIMO with one-bit ADCs under frequency selective fading with orthogonal frequency division multiplexing (OFDM) in the perfect and imperfect receiver channel state information cases. We derive analytical expressions for the bit error rate and ergodic rate. We show that the interfering band can be suppressed by increasing the number of antennas or the oversampling rate when a zero-forcing receiver is employed.

Use of 1-bit analog-to-digital converters in massive multiple-input multiple-output (MIMO) systems has been investigated widely due to the very low power consumption and implementation complexity they have. In this study, we present the benefits of temporal oversampling at the receiver side in 1-bit quantized uplink wideband massive MIMO systems. By expressing the oversampled system in a simple form, we derive the zero-forcing (ZF) detector. With ZF detector, oversampling provides up to 9 dB SNR gain in terms of symbol error rate compared to symbol rate sampling case. We also make the error rate analysis of such systems. Moreover, we show that the necessary number of antennas at the receiver side can be reduced significantly with temporal oversampling.

Owing to their low power consumption and cost, employing 1-bit analog-to-digital converters (ADC) in massive multiple-input multiple-output (MIMO) systems has attracted significant attention, which resulted in such systems to be examined widely in the recent literature. In our previous studies, benefits of temporal oversampling in 1-bit quantized uplink massive MIMO systems is investigated by employing a high complexity zero-forcing (ZF) detector. In this study, we propose a sequential linear receiver as an alternative to the existing ZF receiver for the temporal oversampling case in literature. The complexity of the proposed receiver for temporally oversampled uplink massive MIMO with 1-bit ADC increases linearly with block length while the complexity of the existing ZF receiver grows with the cube of block length. Furthermore, significant reduction for the delay in the estimation of the transmitted data symbols is achieved with the proposed receiver.

Low-resolution analog-to-digital converters (ADCs) have attracted much attention lately for massive multiple-input multiple-output (MIMO) communication and systems with large bandwidth. Especially, 1-bit ADCs are suitable for such systems due to their low-power consumption and cost. In this paper, we illustrate the benefits of using faster than symbol rate (FTSR) sampling in an uplink massive MIMO system with 1-bit ADCs in terms of symbol error rate (SER). We show that the FTSR sampling provides about 5-dB signal-to-noise ratio (SNR) advantage in terms of SER and achievable rate with a linear zero-forcing-type receiver. We also develop analytical bounds on the SER and achievable rate performance of uplink massive MIMO structures with 1-bit quantization for the FTSR scenario for the whole SNR region. The proposed analytical bound holds not only for the FTSR case but also to yield more accurate results compared with some other analytical expressions in the literature. Our results establish a tradeoff between temporal oversampling and the number of receive antennas.

Low resolution analog-to-digital converters (ADC) attracted much attention lately for massive multiple-input multiple-output (MIMO) communication and systems with high bandwidth. Especially, 1-bit ADCs are suitable for such systems due to their low power consumption and cost. In this study, we illustrate the benefits of using faster than symbol rate (FTSR) sampling in an uplink massive MIMO system with 1-bit ADCs in terms of symbol error rate (SER). We show that FTSR sampling provides about 4 dB signal-to-noise ratio (SNR) advantage in terms of SER with a linear low complexity zero-forcing type receiver. We also obtain an analytical bound for the SER performance of uplink massive MIMO structures with 1-bit quantization for the FTSR sampling scenario for low, medium and high SNR regimes. The proposed analytical bound is also applicable to no FTSR sampling case and shown to yield more accurate results compared to some other analytical expressions in the literature. Our results establish a tradeoff between temporal oversampling and the number of receive antennas.