Multi-Cell Massive MIMO Systems with Low Resolution ADCs

In this paper, we propose a low-complexity pseudo-de-quantization based (PDQ-based) iterative receiver in massive multi-user multi-input multi-output (MU-MIMO) systems with low resolution analog-to-digital converters (ADCs). First, a unified framework of massive MU-MIMO system with low resolution ADCs is presented. Then, a low-complexity iterative algorithm applying iterative soft decision interference cancellation (ISDIC) is introduced, where a soft information updating scheme is proposed for the PQD-based optimal detection and an iterative architecture of PDQ-based receiver is given. After that, the effect of quantization step size on the performance of PDQ-based iterative receiver is discussed and a new method is proposed to optimize the quantization step with given resolution ADCs. Finally, simulation results show that the PDQ-based iterative receiver can achieve significant improvement with iterative processing and provide a noticeable performance gain over the MMSE-ISDIC receiver with low resolution ADCs.

High power consumption and hardware cost are the two challenges for practical massive MIMO systems. One promising solution is to employ low resolution analog to digital converter (ADC). In this paper, massive MIMO relaying systems are investigated with double resolution ADCs at base station receiver, where some antennas are connected to low resolution ADCs, while the rest of the antennas are connected to high resolution ADCs. Closed form expression for uplink spectral efficiency (SE) is derived using the maximum ratio combining receiver assuming perfect channel state information. Some asymptotic analysis is discussed to explore the effects of system parameters on SE such as; the number of receive antennas, ADC quantization bits, user transmit power and the rate of high precision ADC in double resolution ADC structure. Moreover, energy efficiency with some system parameters is studied. It is shown that the performance wastage owing to low precision ADCs can be recompensed by growing the number of receive antennas $$ \varvec{N} $$, following a logarithmic scaling law instead of increasing the transmit power at both of source and relay. The double resolution ADCs can achieve a better tradeoff between power consumption and SE when using 4 quantization bits. By using only around 30 antennas, the double resolution ADCs achieves its optimal energy efficiency for selecting system parameters.

Due to the large swath of available bandwidth, millimeter wave (mmWave) spectrum is potentially suitable to meet enhanced network capacity requirements of future generation communication systems. However, the high Analog to Digital Converter (ADC) sampling rate required to process wide band signals in the mmWave regime results in significant power consumption in the receiver hardware. In this work, an Orthogonal Frequency Division Multiplexing (OFDM) based subsampling architecture is proposed to achieve power efficient receiver design. Subbanding in association with orthogonal spreading codes using subsampling technique reduces the high sampling rate requirement of the receiver. Power consumption in the receiver is further reduced by utilizing low resolution ADC with minimal degradation in Bit Error Rate (BER) performance. In addition to this, OFDM transmission with reduced number of Fast Fourier transform (FFT) points alleviates the multi-path effects significantly, and improves the spectral efficiency. The combined effect of ADC bit resolution and subsampling on the system error performance and power consumption is studied.

In this paper, we investigate the downlink achievable ergodic spectral efficiency (SE) of a single-cell multi-user millimeter wave system, in which a uniform rectangular array is used at the base station (BS) to serve multiple single-antenna users. We adopt a three-dimensional channel model by considering both the azimuth and elevation dimensions under single-path propagation. We derive the achievable ergodic SE for this system in with maximum ratio transmission precoding. This analytical expression enables the accurate and quantitative evaluation of the effect of the number of BS antennas, signal-to-noise ratio (SNR), and the crosstalk (squared inner product between different steering vectors) which is a function of the angles of departure (AoD) of users and the inter-antenna spacing. Results show that the achievable ergodic SE logarithmically increases with the number of BS antennas and converges to a value in the high SNR regime. To improve the achievable ergodic SE, we also propose a user scheduling scheme based on feedback of users’ AoD information and obtain the maximum achievable ergodic SE. Furthermore, we consider a dense user scenario where every user’s AoD becomes nearly identical and then derive the system’s minimum achievable SE.

The evolving 5G standards promise green communications with enhanced data services and significant link reliability. Massive multi input multi output (MIMO) techniques are the driving force behind green communications, since they provide better energy efficiency with reduced transmit power. The massive data generated from such mobile communication systems, is a rich data source of great value. Procuring useful analytics from this precious resource, a big data aware 5G mobile communication system can be developed. A particular choice of big analytics brings in the concept of large random matrix models and single ring law. In this paper, first, big data analytics is performed in the context of a mobile user communicating to, either a massive MIMO or a massive MIMO orthogonal frequency division multiplexing (OFDM) system. Constructive insights such as transmitted (source) signal correlation analysis (attributed to certain network events), channel correlation analysis (attributed to user mobility) have been extracted. Ring law also has its roots in signal detection, which suggests that few other signal detection algorithms may be suitable candidates for signal/channel correlation analysis. Therefore, second, a proposed extension of an information theoretic criterion (ITC) based signal detection algorithm, for correlation analysis, is compared with ring law. Using massive MIMO and MIMO-OFDM system simulations, the said correlation analyses have confirmed the prevalence of ring law. Third, it is deduced that integrating big data analytics with massive MIMO system improves spectral efficiency.

High resolution analog to digital converters (ADCs) are major sources of power consumption in conventional fully digital receivers. To reduce power consumption, it is suggested to use a small number of low resolution ADCs. However, for a given number of low resolution ADCs, the best modulation scheme and corresponding quantization strategy that maximizes the spectral efficiency are still unknown. In this paper, a point-to-point singleinput multiple-output system with a hybrid receiver using low resolution ADCs is considered. A novel deep learning based modulation scheme and the associated quantization strategy are developed to maximize the spectral efficiency. It is observed that the derived modulation scheme can achieve optimal high SNR rates and perform near Shannon capacity at low and intermediate SNRs. Our results provide new insights into the optimal utilization of low resolution ADCs and optimal power-performance trade-off in terms of the spectral efficiency.

In this paper, we derive the achievable rate of single-carrier frequency domain equalizer (SC-FDE) and orthogonal frequency division multiplexing (OFDM) systems, constrained on low resolution analog to digital converters (ADCs). We assume that there is a full knowledge of channels at both transmitter and receiver sides. We model low resolution ADCs with an additive uniform quantization noise. We consider an ultra-wide band millimeter wave (mm-Wave) system with a frequency selective channel. The channel is based on practical results at mm-Wave frequencies. Our numerical results show that SC-FDE systems with minimum mean square error (MMSE) equalizers outperform OFDM and SC-FDE systems with zero forcing (ZF) equalizers at high signal to noise ratio (SNR) regimes. Moreover, benefits of increasing the ADCs resolution are not significant due to exponentially increment in the power consumption of ADCs.

This paper investigates the multi-user full-duplex massive multiple-input multiple-output (MIMO) amplify-and-forward (AF) relaying system with low-resolution analog-to-digital converts (ADCs), where the zero-forcing transmitting/ zero-forcing receiving (ZFT/ ZFR) precoding scheme is employed. By modeling the loop-interference and quantization noise as additive noise, we perform the analysis of the achievable spectral efficiency (SE) that is given with closed-form expressions. Then, we present the asymptotic analyses under three different power-scaling scenarios. Comparison analysis shows that, with different power-scaling schemes, the system has different ability to restrict the effect of loop interference and low-resolution ADCs. Specially, the impact of loop interference and quantizer errors on achievable SE is restricted effectively only under the power-scaling scenario where the relay's transmit power is scaled down with the number of transmit antennas at relay. At the same time, the effect of loop interference can be also restricted under the case where both the transmit powers at sources and relay are scaled down with the relay's antenna number. The results can be used to guide the design of massive MIMO relaying systems so that the hardware cost and energy consume can be deduced significantly.

In this paper, we study the achievable rate and the energy efficiency of analog, hybrid, and digital combining (AC, HC, and DC) for millimeter wave (mmW) receivers. We take into account the power consumption of all receiver components, not just analog-to-digital converters (ADCs), determine some practical limitations of beamforming in each architecture, and develop performance analysis charts that enable comparison of different receivers simultaneously in terms of two metrics, namely, spectral efficiency (SE) and energy efficiency (EE). We present a multi-objective utility optimization interpretation to find the best SE-EE weighted tradeoff among AC, DC, and HC schemes. We consider an additive quantization noise model to evaluate the achievable rates with low resolution ADCs. Our analysis shows that AC is only advantageous if the channel rank is strictly one, the link has very low SNR, or there is a very stringent low power constraint at the receiver. Otherwise, we show that the usual claim that DC requires the highest power is not universally valid. Rather, either DC or HC alternatively results in the better SE versus EE tradeoff depending strongly on the considered power consumption characteristic values for each component of the mmW receiver.

The resolution of analog to digital converter (ADC) and the power consumption of digital signal processing (DSP) are becoming the bottleneck of optical communication system. To acquire and recover signals with low resolution ADCs, we propose a binarized coherent optical receiver based on opto-electronic neural network, which consists of three layers, the broadcast layer, the optical neural layer, and the electronic neural layer. The broadcast layer that composed of Mach-Zehnder interferometers (MZIs) is to duplicate and select the input optical signal into the opto-electronic neural network. The optical neural layer is constituted by specially designed structures to map the binarized weights into optical domain, which can be integrated on silicon together with the broadcast layer. The one-bit vertical resolution ADCs act as the binarized activation after the optical neural layer. This binarized coherent optical receiver is demonstrated in simulation of 50-Gb/s single polarization (SP) QPSK system and 100-Gb/s polarization multiplexing (PDM) QPSK system. The one-bit vertical resolution ADCs can be utilized to recover the complex modulation format signal successfully. The broadcast layer and the optical neural layer can be integrated based on silicon, which could increase the calculation speed and reduce the energy consumption of the total system.

The topic of anonymous unmanned aerial vehicle (UAV) localizing based on angle estimation has been frequently discussed in the past few years. However, the existing methodologies are inefficient in a massive sensor arrays scenario. To avoid such drawback, a cooperative three-dimensional (3D) positioning methodology is introduced. The critical idea of the proposed localizing method is to estimate the two-dimensional angle of the anonymous UAV via a polarized massive multi-input multi-output (MIMO) system. To reduce the computational burden and explore the nature of the multi-dimensional data, a tensor compressive sampling (TCS) framework is proposed. Moreover, a closed-form estimation strategy is developed for 2D direction finding. Our framework is shown to be more efficient than the existing algorithm in terms of hardware/software complexity. Besides, it is suitable for a polarized MIMO system with an arbitrary array geometry. Several simulation examples are provided to show its improvement of the new methodology.

This paper proposes an energy efficient millimeter wave (mmWave) hybrid multiple-input multiple-output (MIMO) beamformer with low resolution digital to analog converters (DACs) at the transmitter. We consider the case where all DACs have the same sampling resolution for each radio frequency (RF) chain and select the best subset of the active RF chains and the DAC resolution. A novel technique based on the Dinkelbach method and subset selection optimization is proposed to maximize the energy efficiency (EE) given a predefined power budget for transmission. We also implement an exhaustive search approach to serve as an upper bound on the EE performance and show the performance trade-offs. The simulation results verify that the proposed technique exhibits EE performance similar to the optimal exhaustive search technique while requiring lower computational complexity.

One way to reduce the power consumption in large-scale multiple-input multiple-output (MIMO) systems is to employ low-resolution analog-to-digital converters (ADCs). In this paper, we investigate antenna selection for large-scale MIMO receivers with low-resolution ADCs, thereby providing more flexibility in resolution and number of ADCs. To incorporate quantization effects, we generalize an existing objective function for a greedy capacity-maximization antenna selection approach. The derived objective function offers an opportunity to select an antenna with the best tradeoff between the additional channel gain and increase in quantization error. Using the generalized objective function, we propose an antenna selection algorithm based on a conventional antenna selection algorithm without an increase in overall complexity. Simulation results show that the proposed algorithm outperforms the conventional algorithm in achievable capacity for the same number of antennas.

A generalized uplink cell-free massive multi-input multi-output (MIMO) system assuming access points (APs) are paired with mixed-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) is investigated in this article. Specifically, the mixed-ADC/DAC architecture sacrifices part of performance gains and advocates a few low-precision ADCs/DACs to reduce the power consumption. Based on this antenna structure, we derive closed-form achievable rate expressions for maximal ratio combining (MRC) receiver filters by drawing support from the additive quantization noise model (AQNM) and analyze the impacts of the key design parameters, i.e., the number of total antenna arrays, the precision of coarse ADCs/DACs, the proportion of ideal ADCs/DACs in the mixed ADCs/DACs structure, and the power control strategy. In addition, the energy efficiency (EE) of the considered mixed-ADC/DAC architecture is also explored by recalling a generic power consumption model. Aside from this, to guarantee uniformly good service wherever users are geographically located, a max–min fairness power optimization algorithm is proposed, which can be determined by solving a geometric programming problem. Numerical simulations validate our theoretical analyses and examine the effectiveness of the proposed algorithm.

We consider antenna selection in massive multi-input multi-output (MIMO) systems. Most prior works ideally assume perfect channel estimation at the receiver side. In this paper, we present an antenna selection scheme based on a genetic algorithm for massive MIMO systems in the presence of channel estimation error (CEE). Numerical results suggest that the proposed algorithm outperforms traditional antenna selection algorithms in terms of channel capacity, especially for low signal-to-noise ratio (SNR) scenarios.