24 research outputs found
Energy efficiency of mmWave massive MIMO precoding with low-resolution DACs
With the congestion of the sub-6 GHz spectrum, the interest in massive
multiple-input multiple-output (MIMO) systems operating on millimeter wave
spectrum grows. In order to reduce the power consumption of such massive MIMO
systems, hybrid analog/digital transceivers and application of low-resolution
digital-to-analog/analog-to-digital converters have been recently proposed. In
this work, we investigate the energy efficiency of quantized hybrid
transmitters equipped with a fully/partially-connected phase-shifting network
composed of active/passive phase-shifters and compare it to that of quantized
digital precoders. We introduce a quantized single-user MIMO system model based
on an additive quantization noise approximation considering realistic power
consumption and loss models to evaluate the spectral and energy efficiencies of
the transmit precoding methods. Simulation results show that
partially-connected hybrid precoders can be more energy-efficient compared to
digital precoders, while fully-connected hybrid precoders exhibit poor energy
efficiency in general. Also, the topology of phase-shifting components offers
an energy-spectral efficiency trade-off: active phase-shifters provide higher
data rates, while passive phase-shifters maintain better energy efficiency.Comment: Published in IEEE Journal of Selected Topics in Signal Processin
Green joint radar-communications: RF selection with low resolution DACs and hybrid precoding
This paper considers a multiple-input multiple-output (MIMO) joint radar-communication (JRC) transmission with hybrid precoding and low resolution digital to analog converters (DACs). An energy efficient radio frequency (RF) chain and DAC bit selection approach is presented for a subarrayed hybrid MIMO JRC system. We introduce a weighting formulation to represent the combined radar-communications information rate. The presented selection mechanism is incorporated with fractional programming to solve an energy efficiency maximization problem for JRC which selects the optimal number of RF chains and DAC bit resolution. Subsequently, a weighted minimization problem to compute the precoding matrices is formulated, which is solved using an alternating minimization approach. The numerical results show the effectiveness of the proposed method in terms of high energy efficiency whilst maintaining good rate and desirable radar beampattern performance
Energy Efficiency Maximization Precoding for Quantized Massive MIMO Systems
The use of low-resolution digital-to-analog and analog-to-digital converters (DACs and ADCs) significantly benefits energy efficiency (EE) at the cost of high quantization noise for massive multiple-input multiple-output (MIMO) systems. This paper considers a precoding optimization problem for maximizing EE in quantized downlink massive MIMO systems. To this end, we jointly optimize an active antenna set, precoding vectors, and allocated power; yet acquiring such joint optimal solution is challenging. To resolve this challenge, we decompose the problem into precoding direction and power optimization problems. For precoding direction, we characterize the first-order optimality condition, which entails the effects of quantization distortion and antenna selection. We cast the derived condition as a functional eigenvalue problem, wherein finding the principal eigenvector attains the best local optimal point. To this end, we propose generalized power iteration based algorithm. To optimize precoding power for given precoding direction, we adopt a gradient descent algorithm for the EE maximization. Alternating these two methods, our algorithm identifies a joint solution of the active antenna set, the precoding direction, and allocated power. In simulations, the proposed methods provide considerable performance gains. Our results suggest that a few-bit DACs are sufficient for achieving high EE in massive MIMO systems
A survey on hybrid beamforming techniques in 5G : architecture and system model perspectives
The increasing wireless data traffic demands have driven the need to explore suitable spectrum regions for meeting the projected requirements. In the light of this, millimeter wave (mmWave) communication has received considerable attention from the research community. Typically, in fifth generation (5G) wireless networks, mmWave massive multiple-input multiple-output (MIMO) communications is realized by the hybrid transceivers which combine high dimensional analog phase shifters and power amplifiers with lower-dimensional digital signal processing units. This hybrid beamforming design reduces the cost and power consumption which is aligned with an energy-efficient design vision of 5G. In this paper, we track the progress in hybrid beamforming for massive MIMO communications in the context of system models of the hybrid transceivers' structures, the digital and analog beamforming matrices with the possible antenna configuration scenarios and the hybrid beamforming in heterogeneous wireless networks. We extend the scope of the discussion by including resource management issues in hybrid beamforming. We explore the suitability of hybrid beamforming methods, both, existing and proposed till first quarter of 2017, and identify the exciting future challenges in this domain
Intelligent Reflecting Surfaces and Next Generation Wireless Systems
Intelligent reflecting surface (IRS) is a potential candidate for massive
multiple-input multiple-output (MIMO) 2.0 technology due to its low cost, ease
of deployment, energy efficiency and extended coverage. This chapter
investigates the slot-by-slot IRS reflection pattern design and two-timescale
reflection pattern design schemes, respectively. For the slot-by-slot
reflection optimization, we propose exploiting an IRS to improve the
propagation channel rank in mmWave massive MIMO systems without need to
increase the transmit power budget. Then, we analyze the impact of the
distributed IRS on the channel rank. To further reduce the heavy overhead of
channel training, channel state information (CSI) estimation, and feedback in
time-varying MIMO channels, we present a two-timescale reflection optimization
scheme, where the IRS is configured relatively infrequently based on
statistical CSI (S-CSI) and the active beamformers and power allocation are
updated based on quickly outdated instantaneous CSI (I-CSI) per slot. The
achievable average sum-rate (AASR) of the system is maximized without excessive
overhead of cascaded channel estimation. A recursive sampling particle swarm
optimization (PSO) algorithm is developed to optimize the large-timescale IRS
reflection pattern efficiently with reduced samplings of channel samples.Comment: To appear as a chapter of the book "Massive MIMO for Future Wireless
Communication Systems: Technology and Applications", to be published by
Wiley-IEEE Press. arXiv admin note: text overlap with arXiv:2206.0727
Efficient DSP and Circuit Architectures for Massive MIMO: State-of-the-Art and Future Directions
Massive MIMO is a compelling wireless access concept that relies on the use
of an excess number of base-station antennas, relative to the number of active
terminals. This technology is a main component of 5G New Radio (NR) and
addresses all important requirements of future wireless standards: a great
capacity increase, the support of many simultaneous users, and improvement in
energy efficiency. Massive MIMO requires the simultaneous processing of signals
from many antenna chains, and computational operations on large matrices. The
complexity of the digital processing has been viewed as a fundamental obstacle
to the feasibility of Massive MIMO in the past. Recent advances on
system-algorithm-hardware co-design have led to extremely energy-efficient
implementations. These exploit opportunities in deeply-scaled silicon
technologies and perform partly distributed processing to cope with the
bottlenecks encountered in the interconnection of many signals. For example,
prototype ASIC implementations have demonstrated zero-forcing precoding in real
time at a 55 mW power consumption (20 MHz bandwidth, 128 antennas, multiplexing
of 8 terminals). Coarse and even error-prone digital processing in the antenna
paths permits a reduction of consumption with a factor of 2 to 5. This article
summarizes the fundamental technical contributions to efficient digital signal
processing for Massive MIMO. The opportunities and constraints on operating on
low-complexity RF and analog hardware chains are clarified. It illustrates how
terminals can benefit from improved energy efficiency. The status of technology
and real-life prototypes discussed. Open challenges and directions for future
research are suggested.Comment: submitted to IEEE transactions on signal processin