1,389 research outputs found
Waveforms for the Massive MIMO Downlink: Amplifier Efficiency, Distortion and Performance
In massive MIMO, most precoders result in downlink signals that suffer from
high PAR, independently of modulation order and whether single-carrier or OFDM
transmission is used. The high PAR lowers the power efficiency of the base
station amplifiers. To increase power efficiency, low-PAR precoders have been
proposed. In this article, we compare different transmission schemes for
massive MIMO in terms of the power consumed by the amplifiers. It is found that
(i) OFDM and single-carrier transmission have the same performance over a
hardened massive MIMO channel and (ii) when the higher amplifier power
efficiency of low-PAR precoding is taken into account, conventional and low-PAR
precoders lead to approximately the same power consumption. Since downlink
signals with low PAR allow for simpler and cheaper hardware, than signals with
high PAR, therefore, the results suggest that low-PAR precoding with either
single-carrier or OFDM transmission should be used in a massive MIMO base
station
Massive MU-MIMO-OFDM Downlink with One-Bit DACs and Linear Precoding
Massive multiuser (MU) multiple-input multiple- output (MIMO) is foreseen to
be a key technology in future wireless communication systems. In this paper, we
analyze the downlink performance of an orthogonal frequency division
multiplexing (OFDM)-based massive MU-MIMO system in which the base station (BS)
is equipped with 1-bit digital-to-analog converters (DACs). Using Bussgang's
theorem, we characterize the performance achievable with linear precoders (such
as maximal-ratio transmission and zero forcing) in terms of bit error rate
(BER). Our analysis accounts for the possibility of oversampling the
time-domain transmit signal before the DACs. We further develop a lower bound
on the information-theoretic sum-rate throughput achievable with Gaussian
inputs.
Our results suggest that the performance achievable with 1-bit DACs in a
massive MU-MIMO-OFDM downlink are satisfactory provided that the number of BS
antennas is sufficiently large
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
Multiuser MIMO-OFDM for Next-Generation Wireless Systems
This overview portrays the 40-year evolution of orthogonal frequency division multiplexing (OFDM) research. The amelioration of powerful multicarrier OFDM arrangements with multiple-input multiple-output (MIMO) systems has numerous benefits, which are detailed in this treatise. We continue by highlighting the limitations of conventional detection and channel estimation techniques designed for multiuser MIMO OFDM systems in the so-called rank-deficient scenarios, where the number of users supported or the number of transmit antennas employed exceeds the number of receiver antennas. This is often encountered in practice, unless we limit the number of users granted access in the base stationβs or radio portβs coverage area. Following a historical perspective on the associated design problems and their state-of-the-art solutions, the second half of this treatise details a range of classic multiuser detectors (MUDs) designed for MIMO-OFDM systems and characterizes their achievable performance. A further section aims for identifying novel cutting-edge genetic algorithm (GA)-aided detector solutions, which have found numerous applications in wireless communications in recent years. In an effort to stimulate the cross pollination of ideas across the machine learning, optimization, signal processing, and wireless communications research communities, we will review the broadly applicable principles of various GA-assisted optimization techniques, which were recently proposed also for employment inmultiuser MIMO OFDM. In order to stimulate new research, we demonstrate that the family of GA-aided MUDs is capable of achieving a near-optimum performance at the cost of a significantly lower computational complexity than that imposed by their optimum maximum-likelihood (ML) MUD aided counterparts. The paper is concluded by outlining a range of future research options that may find their way into next-generation wireless systems
Resource allocation for transmit hybrid beamforming in decoupled millimeter wave multiuser-MIMO downlink
This paper presents a study on joint radio resource allocation and hybrid precoding in multicarrier massive multiple-input multiple-output communications for 5G cellular networks. In this paper, we present the resource allocation algorithm to maximize the proportional fairness (PF) spectral efficiency under the per subchannel power and the beamforming rank constraints. Two heuristic algorithms are designed. The proportional fairness hybrid beamforming algorithm provides the transmit precoder with a proportional fair spectral efficiency among users for the desired number of radio-frequency (RF) chains. Then, we transform the number of RF chains or rank constrained optimization problem into convex semidefinite programming (SDP) problem, which can be solved by standard techniques. Inspired by the formulated convex SDP problem, a low-complexity, two-step, PF-relaxed optimization algorithm has been provided for the formulated convex optimization problem. Simulation results show that the proposed suboptimal solution to the relaxed optimization problem is near-optimal for the signal-to-noise ratio SNR <= 10 dB and has a performance gap not greater than 2.33 b/s/Hz within the SNR range 0-25 dB. It also outperforms the maximum throughput and PF-based hybrid beamforming schemes for sum spectral efficiency, individual spectral efficiency, and fairness index
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