3,522 research outputs found
Near-optimal pilot allocation in sparse channel estimation for massive MIMO OFDM systems
Inspired by the success in sparse signal recovery, compressive sensing has already been applied for the pilot-based channel estimation in massive multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. However, little attention has been paid to the pilot design in the massive MIMO system. To obtain the near-optimal pilot placement, two efficient schemes based on the block coherence (BC) of the measurement matrix are introduced. The first scheme searches the pilot pattern with the minimum BC value through the simultaneous perturbation stochastic approximation (SPSA) method. The second scheme combines the BC with probability model and then utilizes the cross-entropy optimization (CEO) method to solve the pilot allocation problem. Simulation results show that both of the methods outperform the equispaced search method, exhausted search method and random search method in terms of mean square error (MSE) of the channel estimate. Moreover, it is demonstrated that SPSA converges much faster than the other methods thus are more efficient, while CEO could provide more accurate channel estimation performance
Beamspace Aware Adaptive Channel Estimation for Single-Carrier Time-varying Massive MIMO Channels
In this paper, the problem of sequential beam construction and adaptive
channel estimation based on reduced rank (RR) Kalman filtering for
frequency-selective massive multiple-input multiple-output (MIMO) systems
employing single-carrier (SC) in time division duplex (TDD) mode are
considered. In two-stage beamforming, a new algorithm for statistical
pre-beamformer design is proposed for spatially correlated time-varying
wideband MIMO channels under the assumption that the channel is a stationary
Gauss-Markov random process. The proposed algorithm yields a nearly optimal
pre-beamformer whose beam pattern is designed sequentially with low complexity
by taking the user-grouping into account, and exploiting the properties of
Kalman filtering and associated prediction error covariance matrices. The
resulting design, based on the second order statistical properties of the
channel, generates beamspace on which the RR Kalman estimator can be realized
as accurately as possible. It is observed that the adaptive channel estimation
technique together with the proposed sequential beamspace construction shows
remarkable robustness to the pilot interference. This comes with significant
reduction in both pilot overhead and dimension of the pre-beamformer lowering
both hardware complexity and power consumption.Comment: 7 pages, 3 figures, accepted by IEEE ICC 2017 Wireless Communications
Symposiu
Energy-Efficient Low-Complexity Algorithm in 5G Massive MIMO Systems
Energy efficiency (EE) is a critical design when taking into account
circuit power consumption (CPC) in fifth-generation cellular networks. These
problems arise because of the increasing number of antennas in massive
multiple-input multiple-output (MIMO) systems, attributable to inter-cell
interference for channel state information. Apart from that, a higher number
of radio frequency (RF) chains at the base station and active users consume
more power due to the processing activities in digital-to-analogue converters
and power amplifiers. Therefore, antenna selection, user selection, optimal
transmission power, and pilot reuse power are important aspects in improving
energy efficiency in massive MIMO systems. This work aims to investigate
joint antenna selection, optimal transmit power and joint user selection based
on deriving the closed-form of the maximal EE, with complete knowledge
of large-scale fading with maximum ratio transmission. It also accounts for
channel estimation and eliminating pilot contamination as antennasM→∞.
This formulates the optimization problem of joint optimal antenna selection,
transmits power allocation and joint user selection to mitigate inter-cellinterference
in downlink multi-cell massiveMIMO systems under minimized
reuse of pilot sequences based on a novel iterative low-complexity algorithm
(LCA) for Newton’s methods and Lagrange multipliers. To analyze the precise
power consumption, a novel power consumption scheme is proposed for
each individual antenna, based on the transmit power amplifier and CPC.
Simulation results demonstrate that the maximal EE was achieved using the
iterative LCA based on reasonable maximum transmit power, in the case the
noise power is less than the received power pilot. The maximum EE was
achieved with the desired maximum transmit power threshold by minimizing pilot reuse, in the case the transmit power allocation ρd = 40 dBm, and the
optimal EE=71.232 Mb/j
On iterative low-complexity algorithm for optimal antenna selection and joint transmit power allocation under impact pilot contamination in downlink 5g massive MIMO systems
Massive multiple-input-multiple-output (MIMO) technology has been proven to be a
viable strategy for enhancing energy efficiency (EE) and achievable high data rates,
which is the key to the design of the fifth-generation wireless cellular networks. The
major challenge in massive MIMO systems is pilot contamination arising from large
numbers of pilot reuse sequences due to non-orthogonal pilot sequences between
different cells. Massive MIMO systems are affected by pilot contamination,
which influences the data rate of the system. In this thesis, highly interfering UEs
in adjacent cells were identified based on estimates of large-scale fading and then
included in the joint channel processing to achieve the desired tradeoff between the
effectiveness and the efficiency of channel estimation in order to increase the data
rate. The BS correlates the training signal with the established pilot reuse sequences
of every UE to obtain a high-quality channel estimation. The channel quality of the
users was enhanced by allocating orthogonal pilot reuse sequences to the center
user and the edge user according to different levels of pilot contamination based
on the large-scale fading that mitigated pilot contamination. Meanwhile, an
increase in the number of antenna arrays at BSs resulted in greater power
consumption due to the increased number of radio-frequency (RF) chains, which
could not be neglected and became a technical challenge. Achievable high data rate in
massive MIMO, depended on quality of channel and analyze the circuit power
consumption under power constraint for a limited number of RF chains for antennas
selection. The full knowledge of channel state information (CSI) and the
configuration channel selection, which used to prevent the major training that is
incurred in the channel estimation for all receiving antenna. The optimal antenna
could be chosen based on the transmitted power by selecting the preceding channel
estimation. Moreover, reducing the transmitted power from the BS depended on
selecting the optimal number of RF chains for choosing the best performing active
antenna selection. To evaluate the energy-efficient massive MIMO, we focused iv
not only on the joint antenna selection, optimal transmit power, and circuit
power consumption to balance the radiated EE but also on adjusting the length
of the pilot sequences to improve EE. The proposed Low–complexity iterative
algorithm for antenna selection and transmission power helped to choose an
accurate number of active RF chains to reduce circuit power consumption, and
minimize the reuse of pilot sequences to improve channel estimation. The
optimization of the antenna selection and optimal transmission power with impact of
pilot reuse sequences were achieved, by applying Newton’s method and the Lagrange
multiplier. This enabled the use of pilot reuse sequences and minimized the total
transmit power based on the proportional number of antenna selection and reduced
the number of RF chains at the receiver through efforts to allocate every RF chain.
From the simulation results, the channel quality of the users was enhanced by
allocating orthogonal pilot reuse sequences. From Fig.4.3, in chapter 4, the
maximal value of data rates = (17.4, 16.9,16.3) bits/s/Hz, when the optimal transmit
pilot reuse was = (14, 17, 20), with accounting channel estimation, when the
number of antennas was . The proposed low- complexity iterative algorithm
achieved the best maximal EE according Fig. 6.5 in chapter 6, which was 95 Mbits/j,
resulted from the large number of antennas at the BS, when the transmit power was
and transmit antennas was = 100 and user was = 20. In conclusion,
the proposed low-complexity iterative algorithm can be used to maximize the EE
based on the maximum transmit power , where the noise power is less
than the power of the received pilot sequence
Analysis of Wireless Networks With Massive Connectivity
Recent years have witnessed unprecedented growth in wireless networks in terms of both data traffic and number of connected devices. How to support this fast increasing demand for high data traffic and connectivity is a key consideration in the design of future wireless communication systems. With this motivation, in this thesis, we focus on the analysis of wireless networks with massive connectivity.
In the first part of the thesis, we seek to improve the energy efficiency (EE) of single-cell massive multiple-input multiple-output (MIMO) networks with joint antenna selection and user scheduling. We propose a two-step iterative procedure to maximize the EE. In each iteration, bisection search and random selection are used first to determine a subset of antennas with the users selected before, and then identify the EE-optimal subset of users with the selected antennas via cross entropy algorithm. Subsequently, we focus on the joint uplink and downlink EE maximization, under a limitation on the number of available radio frequency (RF) chains. With the Jensen\u27s inequality and the power consumption model, the original problem is converted into a combinatorial optimization problem. Utilizing the learning-based stochastic gradient descent framework and the rare event simulation method, we propose an efficient learning-based stochastic gradient descent algorithm to solve the corresponding combinatorial optimization problem.
In the second part of the thesis, we focus on the joint activity detection and channel estimation in cell-free massive MIMO systems with massive connectivity. At first, we conduct an asymptotic analysis of single measurement vector (SMV) based minimum mean square error (MMSE) estimation in cell-free massive MIMO systems with massive connectivity. We establish a decoupling principle of SMV based MMSE estimation for sparse signal vectors with independent and non-identically distributed (i.n.i.d.) non-zero components. Subsequently, using the decoupling principle, likelihood ratio test and the optimal fusion rule, we obtain detection rules for the activity of users based on the received pilot signals at only one access point (AP), and also based on the cooperation of the received pilot signals from the entire set of APs for centralized and distributed detection. Moreover, we study the achievable uplink rates with zero-forcing (ZF) detector at the central processing unit (CPU) of the cell-free massive MIMO systems.
In the third part, we focus on the performance analysis of intelligent reflecting surface (IRS) assisted wireless networks. Initially, we investigate the MMSE channel estimation for IRS assisted wireless communication systems. Then, we study the sparse activity detection problem in IRS assisted wireless networks. Specifically, employing the generalized approximate message passing (GAMP) algorithm, we obtain the MMSE estimates of the equivalent effective channel coefficients from the base station (BS) to all users, and transform the received pilot signals into additive Gaussian noise corrupted versions of the equivalent effective channel coefficients. Likelihood ratio test is used to acquire decisions on the activity of each user based on the Gaussian noise corrupted equivalent effective channel coefficients, and the optimal fusion rule is used to obtain the final decisions on the activity of all users based on the previous decisions on the activity of each user and the corresponding reliabilities. Finally, we conduct an asymptotic analysis of maximizing the weighted sum rate by joint beamforming and power allocation under transmit power and quality-of-service (QoS) constraints in IRS assisted wireless networks
A Deep-Learning Based Framework for Joint Downlink Precoding and CSI Sparsification
Optimal pilot design to acquire channel state information (CSI) is of critical importance for FDD downlink massive MIMO systems, and is still an open problem. To tackle this issue, in this paper we propose a two-stage precoding approach based on reduced CSI (rCSI-TSP) design framework and an efficient algorithm, whose core is to obtain an optimal precoder while also sparsifying physical CSI (pCSI), so as to save on CSI estimation. The advantages of the rCSI-TSP framework are three-fold. First, the framework enables to simultaneously extract and exploit statistical and instantaneous CSI. Second, it guarantees the most needed rCSI can be obtained and thus avoids performance loss due to heuristic pilot design. Third, we tailor an efficient online deep-learning based method for the TSP framework, which paves the way for practical applications. As an example, we apply the framework to the multi-user symbol-level precoding (SLP) and verify performance improvements
Joint Pilot Design and Uplink Power Allocation in Multi-Cell Massive MIMO Systems
This paper considers pilot design to mitigate pilot contamination and provide
good service for everyone in multi-cell Massive multiple input multiple output
(MIMO) systems. Instead of modeling the pilot design as a combinatorial
assignment problem, as in prior works, we express the pilot signals using a
pilot basis and treat the associated power coefficients as continuous
optimization variables. We compute a lower bound on the uplink capacity for
Rayleigh fading channels with maximum ratio detection that applies with
arbitrary pilot signals. We further formulate the max-min fairness problem
under power budget constraints, with the pilot signals and data powers as
optimization variables. Because this optimization problem is non-deterministic
polynomial-time hard due to signomial constraints, we then propose an algorithm
to obtain a local optimum with polynomial complexity. Our framework serves as a
benchmark for pilot design in scenarios with either ideal or non-ideal
hardware. Numerical results manifest that the proposed optimization algorithms
are close to the optimal solution obtained by exhaustive search for different
pilot assignments and the new pilot structure and optimization bring large
gains over the state-of-the-art suboptimal pilot design.Comment: 16 pages, 8 figures. Accepted to publish at IEEE Transactions on
Wireless Communication
On Low-Resolution ADCs in Practical 5G Millimeter-Wave Massive MIMO Systems
Nowadays, millimeter-wave (mmWave) massive multiple-input multiple-output
(MIMO) systems is a favorable candidate for the fifth generation (5G) cellular
systems. However, a key challenge is the high power consumption imposed by its
numerous radio frequency (RF) chains, which may be mitigated by opting for
low-resolution analog-to-digital converters (ADCs), whilst tolerating a
moderate performance loss. In this article, we discuss several important issues
based on the most recent research on mmWave massive MIMO systems relying on
low-resolution ADCs. We discuss the key transceiver design challenges including
channel estimation, signal detector, channel information feedback and transmit
precoding. Furthermore, we introduce a mixed-ADC architecture as an alternative
technique of improving the overall system performance. Finally, the associated
challenges and potential implementations of the practical 5G mmWave massive
MIMO system {with ADC quantizers} are discussed.Comment: to appear in IEEE Communications Magazin
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