43 research outputs found
Capacity Bounds for One-Bit MIMO Gaussian Channels with Analog Combining
The use of 1-bit analog-to-digital converters (ADCs) is seen as a promising
approach to significantly reduce the power consumption and hardware cost of
multiple-input multiple-output (MIMO) receivers. However, the nonlinear
distortion due to 1-bit quantization fundamentally changes the optimal
communication strategy and also imposes a capacity penalty to the system. In
this paper, the capacity of a Gaussian MIMO channel in which the antenna
outputs are processed by an analog linear combiner and then quantized by a set
of zero threshold ADCs is studied. A new capacity upper bound for the zero
threshold case is established that is tighter than the bounds available in the
literature. In addition, we propose an achievability scheme which configures
the analog combiner to create parallel Gaussian channels with phase
quantization at the output. Under this class of analog combiners, an algorithm
is presented that identifies the analog combiner and input distribution that
maximize the achievable rate. Numerical results are provided showing that the
rate of the achievability scheme is tight in the low signal-to-noise ratio
(SNR) regime. Finally, a new 1-bit MIMO receiver architecture which employs
analog temporal and spatial processing is proposed. The proposed receiver
attains the capacity in the high SNR regime.Comment: 30 pages, 9 figures, Submitted to IEEE Transactions on Communication
Millimeter Wave Systems for Wireless Cellular Communications
This thesis considers channel estimation and multiuser (MU) data transmission
for massive MIMO systems with fully digital/hybrid structures in mmWave
channels. It contains three main contributions. In this thesis, we first
propose a tone-based linear search algorithm to facilitate the estimation of
angle-of-arrivals of the strongest components as well as scattering components
of the users at the base station (BS) with fully digital structure. Our results
show that the proposed maximum-ratio transmission (MRT) based on the strongest
components can achieve a higher data rate than that of the conventional MRT,
under the same mean squared errors (MSE). Second, we develop a low-complexity
channel estimation and beamformer/precoder design scheme for hybrid mmWave
systems. In addition, the proposed scheme applies to both non-sparse and sparse
mmWave channel environments. We then leverage the proposed scheme to
investigate the downlink achievable rate performance. The results show that the
proposed scheme obtains a considerable achievable rate of fully digital
systems. Taking into account the effect of various types of errors, we
investigate the achievable rate performance degradation of the considered
scheme. Third, we extend our proposed scheme to a multi-cell MU mmWave MIMO
network. We derive the closed-form approximation of the normalized MSE of
channel estimation under pilot contamination over Rician fading channels.
Furthermore, we derive a tight closed-form approximation and the scaling law of
the average achievable rate. Our results unveil that channel estimation errors,
the intra-cell interference, and the inter-cell interference caused by pilot
contamination over Rician fading channels can be efficiently mitigated by
simply increasing the number of antennas equipped at the desired BS.Comment: Thesi
Cell-Free IoT Networks with SWIPT: Performance Analysis and Power Control
In this paper, the performance of simultaneous wireless information and power transfer (SWIPT) in downlink (DL) Internet-of-things (IoT) networks relying on cell-free massive multiple-input multiple-output (CF-mMIMO) technique is investigated. In such a network, the access points (APs) beam the radio-frequency (RF) energy toward IoT sensors during the DL wireless power transfer phase. Tight closed-form expressions for DL harvested energy (HE) and achievable rate with conjugate beamforming (CB) and normalized CB (NCB) are respectively derived, which enable us to analyze the behaviors of CB and NCB schemes in terms of both HE and achievable rate. Apart from this, to guarantee sensor fairness with respect to the HE and achievable rate, a max-min power control strategy based on the accelerated projected gradient (APG) method is proposed. Specifically, the proposed APG-based power control is able to determine the optimal solution in closed form and is more memory-efficient than the convex-solver-based counterpart. These analytical results as well as the effectiveness of the proposed power control policy are verified by experimental simulations
Compressive Sensing-Based Grant-Free Massive Access for 6G Massive Communication
The advent of the sixth-generation (6G) of wireless communications has given
rise to the necessity to connect vast quantities of heterogeneous wireless
devices, which requires advanced system capabilities far beyond existing
network architectures. In particular, such massive communication has been
recognized as a prime driver that can empower the 6G vision of future
ubiquitous connectivity, supporting Internet of Human-Machine-Things for which
massive access is critical. This paper surveys the most recent advances toward
massive access in both academic and industry communities, focusing primarily on
the promising compressive sensing-based grant-free massive access paradigm. We
first specify the limitations of existing random access schemes and reveal that
the practical implementation of massive communication relies on a dramatically
different random access paradigm from the current ones mainly designed for
human-centric communications. Then, a compressive sensing-based grant-free
massive access roadmap is presented, where the evolutions from single-antenna
to large-scale antenna array-based base stations, from single-station to
cooperative massive multiple-input multiple-output systems, and from unsourced
to sourced random access scenarios are detailed. Finally, we discuss the key
challenges and open issues to shed light on the potential future research
directions of grant-free massive access.Comment: Accepted by IEEE IoT Journa
An overview of transmission theory and techniques of large-scale antenna systems for 5G wireless communications
To meet the future demand for huge traffic volume of wireless data service, the research on the fifth generation (5G) mobile communication systems has been undertaken in recent years. It is expected that the spectral and energy efficiencies in 5G mobile communication systems should be ten-fold higher than the ones in the fourth generation (4G) mobile communication systems. Therefore, it is important to further exploit the potential of spatial multiplexing of multiple antennas. In the last twenty years, multiple-input multiple-output (MIMO) antenna techniques have been considered as the key techniques to increase the capacity of wireless communication systems. When a large-scale antenna array (which is also called massive MIMO) is equipped in a base-station, or a large number of distributed antennas (which is also called large-scale distributed MIMO) are deployed, the spectral and energy efficiencies can be further improved by using spatial domain multiple access. This paper provides an overview of massive MIMO and large-scale distributed MIMO systems, including spectral efficiency analysis, channel state information (CSI) acquisition, wireless transmission technology, and resource allocation
Channel Estimation in Multi-user Massive MIMO Systems by Expectation Propagation based Algorithms
Massive multiple input multiple output (MIMO) technology uses large antenna arrays with tens or hundreds of antennas at the base station (BS) to achieve high spectral efficiency, high diversity, and high capacity. These benefits, however, rely on obtaining accurate channel state information (CSI) at the receiver for both uplink and downlink channels. Traditionally, pilot sequences are transmitted and used at the receiver to estimate the CSI. Since the length of the pilot sequences scale with the number of transmit antennas, for massive MIMO systems downlink channel estimation requires long pilot sequences resulting in reduced spectral efficiency and the so-called pilot contamination due to sharing of the pilots in adjacent cells.
In this dissertation we first review the problem of channel estimation in massive MIMO systems. Next, we study the problem of semi-blind channel estimation in the uplink in the case of spatially correlated time-varying channels. The proposed method uses the transmitted data symbols as virtual pilots to enhance channel estimation. An expectation propagation (EP) algorithm is developed to iteratively approximate the joint a posterior distribution of the unknown channel matrix and the transmitted data symbols with a distribution from an exponential family. The distribution is then used for direct estimation of the channel matrix and detection of the data symbols. A modified version of Kalman filtering algorithm referred to as KF-M emerges from our EP derivation and it is used to initialize our algorithm. Simulation results demonstrate that channel estimation error and the symbol error rate of the proposed algorithm improve with the increase in the number of BS antennas or the number of data symbols in the transmitted frame. Moreover, the proposed algorithms can mitigate the effects of pilot contamination as well as time-variations of the channel.
Next, we study the problem of downlink channel estimation in multi-user massive MIMO systems. Our approach is based on Bayesian compressive sensing in which the clustered sparse structure of the channel in the angular domain is exploited to reduce the pilot overhead. To capture the clustered structure, we employ a conditionally independent identically distributed Bernoulli-Gaussian prior on the sparse vector representing the channel, and a Markov prior on its support vector. An EP algorithm is developed to approximate the intractable joint distribution on the sparse vector and its support with a distribution from an exponential family. This distribution is then used for direct estimation of the channel. The EP algorithm requires the model parameters which are unknown. We estimate these parameters using the expectation maximization (EM) algorithm. Simulation results show that the proposed combination of EM and EP referred to as EM-EP algorithm outperforms several recently-proposed algorithms in the literature