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Register a new userEnergy Efficiency Optimization for Millimeter Wave System with Resolution-Adaptive ADCs
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Added by
Hualian Sheng
Publication date
2020
fields
Electronic Engineering
and research's language is
English
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This letter investigates the uplink of a multi-user millimeter wave (mmWave) system, where the base station (BS) is equipped with a massive multiple-input multiple-output (MIMO) array and resolution-adaptive analog-to-digital converters (RADCs). Although employing massive MIMO at the BS can significantly improve the spectral efficiency, it also leads to high hardware complexity and huge power consumption. To overcome these challenges, we seek to jointly optimize the beamspace hybrid combiner and the ADC quantization bits allocation to maximize the system energy efficiency (EE) under some practical constraints. The formulated problem is non-convex due to the non-linear fractional objective function and the non-convex feasible set which is generally intractable. In order to handle these difficulties, we first apply some fractional programming (FP) techniques and introduce auxiliary variables to recast this problem into an equivalent form amenable to optimization. Then, we propose an efficient double-loop iterative algorithm based on the penalty dual decomposition (PDD) and the majorization-minimization (MM) methods to find local stationary solutions. Simulation results reveal significant gain over the baselines.
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Millimeter wave (mmWave) communication systems using adaptive-resolution analog-to-digital converters (RADCs) have recently drawn considerable interests from the research community as benefit of their high energy efficiency and low implementation cost. In this paper, we focus on the mmWave uplink using RADCs and investigate the joint user scheduling and resource allocation problem. Specifically, we seek to maximize the system throughput of the scheduled users by jointly optimizing their transmit power level and hybrid combiners as well as the number of quantization bits, subject to practical constraints. By relying on fractional programming (FP) techniques, we first covert this problem into a form amenable to optimization and exploit the specific structures in its solutions with the aid of the so-called Ky Fan n-norm. Then, the resultant optimization problem is solved using a penalty block successive concave approximation (P-BSCA) algorithm. Our numerical results reveal that the proposed algorithm substantially enhances the throughput of the scheduled users compared to the state-of-theart benchmark schemes and provides more flexible and efficient resource allocation control.
Analog-to-digital converters (ADCs) allow physical signals to be processed using digital hardware. The power consumed in conversion grows with the sampling rate and quantization resolution, imposing a major challenge in power-limited systems. A common ADC architecture is based on sample-and-hold (S/H) circuits, where the analog signal is being tracked only for a fraction of the sampling period. In this paper, we propose the concept of eSampling ADCs, which harvest energy from the analog signal during the time periods where the signal is not being tracked. This harvested energy can be used to supplement the ADC itself, paving the way to the possibility of zero-power consumption and power-saving ADCs. We analyze the tradeoff between the ability to recover the sampled signal and the energy harvested, and provide guidelines for setting the sampling rate in the light of accuracy and energy constraints. Our analysis indicates that eSampling ADCs operating with up to 12 bits per sample can acquire bandlimited analog signals such that they can be perfectly recovered without requiring power from the external source. Furthermore, our theoretical results reveal that eSampling ADCs can in fact save power by harvesting more energy than they consume. To verify the feasibility of eSampling ADCs, we present a circuit-level design using standard complementary metal oxide semiconductor (CMOS) 65 nm technology. An eSampling 8-bit ADC which samples at 40 MHZ is designed on a Cadence Virtuoso platform. Our experimental study involving Nyquist rate sampling of bandlimited signals demonstrates that such ADCs are indeed capable of harvesting more energy than that spent during analog-to-digital conversion, without affecting the accuracy.
This paper develops a new deep neural network optimized equalization framework for massive multiple input multiple output orthogonal frequency division multiplexing (MIMOOFDM) systems that employ low-resolution analog-to-digital converters (ADCs) at the base station (BS). The use of lowresolution ADCs could largely reduce hardware complexity and circuit power consumption, however, it makes the channel station information almost blind to the BS, hence causing difficulty in solving the equalization problem. In this paper, we consider a supervised learning architecture, where the goal is to learn a representative function that can predict the targets (constellation points) from the inputs (outputs of the low-resolution ADCs) based on the labeled training data (pilot signals). Especially, our main contributions are two-fold: 1) First, we design a new activation function, whose outputs are close to the constellation points when the parameters are finally optimized, to help us fully exploit the stochastic gradient descent method for the discrete optimization problem. 2) Second, an unsupervised loss is designed and then added to the optimization objective, aiming to enhance the representation ability (so-called generalization). Lastly, various experimental results confirm the superiority of the proposed equalizer over some existing ones, particularly when the statistics of the channel state information are unclear.
In this paper, a framework of beamspace channel estimation in millimeter wave (mmWave) massive MIMO system is proposed. The framework includes the design of hybrid precoding and combining matrix as well as the search method for the largest entry of over-sampled beamspace receiving matrix. Then based on the framework, three channel estimation schemes including identity matrix approximation (IA)-based scheme, scattered zero off-diagonal (SZO)-based scheme and concentrated zero off-diagonal (CZO)-based scheme are proposed. These schemes together with the existing channel estimation schemes are compared in terms of computational complexity, estimation error and total time slots for channel training. Simulation results show that the proposed schemes outperform the existing schemes and can approach the performance of the ideal case. In particular, total time slots for channel training can be substantially reduced.
In this paper, we investigate the downlink performance of a three-tier heterogeneous network (HetNet). The objective is to enhance the edge capacity of a macro cell by deploying unmanned aerial vehicles (UAVs) as flying base stations and small cells (SCs) for improving the capacity of indoor users in scenarios such as temporary hotspot regions or during disaster situations where the terrestrial network is either insufficient or out of service. UAVs are energy-constrained devices with a limited flight time, therefore, we formulate a two layer optimization scheme, where we first optimize the power consumption of each tier for enhancing the system energy efficiency (EE) under a minimum quality-of-service (QoS) requirement, which is followed by optimizing the average hover time of UAVs. We obtain the solution to these nonlinear constrained optimization problems by first utilizing the Lagrange multipliers method and then implementing a sub-gradient approach for obtaining convergence. The results show that through optimal power allocation, the system EE improves significantly in comparison to when maximum power is allocated to users (ground cellular users or connected vehicles). The hover time optimization results in increased flight time of UAVs thus providing service for longer durations.
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