191 research outputs found
MSE-optimal 1-bit Precoding for Multiuser MIMO via Branch and Bound
In this paper, we solve the sum mean-squared error (MSE)-optimal 1-bit
quantized precoding problem exactly for small-to-moderate sized multiuser
multiple-input multiple-output (MU-MIMO) systems via branch and bound. To this
end, we reformulate the original NP-hard precoding problem as a tree search and
deploy a number of strategies that improve the pruning efficiency without
sacrificing optimality. We evaluate the error-rate performance and the
complexity of the resulting 1-bit branch-and-bound (BB-1) precoder, and compare
its efficacy to that of existing, suboptimal algorithms for 1-bit precoding in
MU-MIMO systems
Interference Exploitation 1-Bit Massive MIMO Precoding: A Partial Branch-and-Bound Solution With Near-Optimal Performance
In this paper, we focus on 1-bit precoding approaches for downlink massive multiple-input multiple-output (MIMO) systems, where we exploit the concept of constructive interference (CI). For both PSK and QAM signaling, we firstly formulate the optimization problem that maximizes the CI effect subject to the requirement of the 1-bit transmit signals. We then mathematically prove that, when employing the CI formulation and relaxing the 1-bit constraint, the majority of the transmit signals already satisfy the 1-bit formulation. Building upon this important observation, we propose a 1-bit precoding approach that further improves the performance of the conventional 1-bit CI precoding via a partial branch-and-bound (P-BB) process, where the BB procedure is performed only for the entries that do not comply with the 1-bit requirement. This operation allows a significant complexity reduction compared to the fully-BB (F-BB) process, and enables the BB framework to be applicable to the complex massive MIMO scenarios. We further develop an alternative 1-bit scheme through an ‘Ordered Partial Sequential Update’ (OPSU) process that allows an additional complexity reduction. Numerical results show that both proposed 1-bit precoding methods exhibit a significant signal-to-noise ratio (SNR) gain for the error rate performance, especially for higher-order modulations
Robust Monotonic Optimization Framework for Multicell MISO Systems
The performance of multiuser systems is both difficult to measure fairly and
to optimize. Most resource allocation problems are non-convex and NP-hard, even
under simplifying assumptions such as perfect channel knowledge, homogeneous
channel properties among users, and simple power constraints. We establish a
general optimization framework that systematically solves these problems to
global optimality. The proposed branch-reduce-and-bound (BRB) algorithm handles
general multicell downlink systems with single-antenna users, multiantenna
transmitters, arbitrary quadratic power constraints, and robustness to channel
uncertainty. A robust fairness-profile optimization (RFO) problem is solved at
each iteration, which is a quasi-convex problem and a novel generalization of
max-min fairness. The BRB algorithm is computationally costly, but it shows
better convergence than the previously proposed outer polyblock approximation
algorithm. Our framework is suitable for computing benchmarks in general
multicell systems with or without channel uncertainty. We illustrate this by
deriving and evaluating a zero-forcing solution to the general problem.Comment: Published in IEEE Transactions on Signal Processing, 16 pages, 9
figures, 2 table
1-Bit Massive MIMO Transmission: Embracing Interference with Symbol-Level Precoding
The deployment of large-scale antenna arrays for cellular base stations
(BSs), termed as `Massive MIMO', has been a key enabler for meeting the
ever-increasing capacity requirement for 5G communication systems and beyond.
Despite their promising performance, fully-digital massive MIMO systems require
a vast amount of hardware components including radio frequency chains, power
amplifiers, digital-to-analog converters (DACs), etc., resulting in a huge
increase in terms of the total power consumption and hardware costs for
cellular BSs. Towards both spectrally-efficient and energy-efficient massive
MIMO deployment, a number of hardware limited architectures have been proposed,
including hybrid analog-digital structures, constant-envelope transmission, and
use of low-resolution DACs. In this paper, we overview the recent interest in
improving the error-rate performance of massive MIMO systems deployed with
1-bit DACs through precoding at the symbol level. This line of research goes
beyond traditional interference suppression or cancellation techniques by
managing interference on a symbol-by-symbol basis. This provides unique
opportunities for interference-aware precoding tailored for practical massive
MIMO systems. Firstly, we characterize constructive interference (CI) and
elaborate on how CI can benefit the 1-bit signal design by exploiting the
traditionally undesired multi-user interference as well as the interference
from imperfect hardware components. Subsequently, we overview several solutions
for 1-bit signal design to illustrate the gains achievable by exploiting CI.
Finally, we identify some challenges and future research directions for 1-bit
massive MIMO systems that are yet to be explored.Comment: This work has been submitted to the IEEE for possible publication.
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Linear Precoding with Low-Resolution DACs for Massive MU-MIMO-OFDM Downlink
We consider the downlink of a massive multiuser (MU) multiple-input
multiple-output (MIMO) system in which the base station (BS) is equipped with
low-resolution digital-to-analog converters (DACs). In contrast to most
existing results, we assume that the system operates over a frequency-selective
wideband channel and uses orthogonal frequency division multiplexing (OFDM) to
simplify equalization at the user equipments (UEs). Furthermore, we consider
the practically relevant case of oversampling DACs. We theoretically analyze
the uncoded bit error rate (BER) performance with linear precoders (e.g., zero
forcing) and quadrature phase-shift keying using Bussgang's theorem. We also
develop a lower bound on the information-theoretic sum-rate throughput
achievable with Gaussian inputs, which can be evaluated in closed form for the
case of 1-bit DACs. For the case of multi-bit DACs, we derive approximate, yet
accurate, expressions for the distortion caused by low-precision DACs, which
can be used to establish lower bounds on the corresponding sum-rate throughput.
Our results demonstrate that, for a massive MU-MIMO-OFDM system with a
128-antenna BS serving 16 UEs, only 3--4 DAC bits are required to achieve an
uncoded BER of 10^-4 with a negligible performance loss compared to the
infinite-resolution case at the cost of additional out-of-band emissions.
Furthermore, our results highlight the importance of taking into account the
inherent spatial and temporal correlations caused by low-precision DACs
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