ROBUST TRAINING SEQUENCE DESIGN FOR SPATIALLY CORRELATED MIMO CHANNEL ESTIMATION (original) (raw)
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Special Issue: Multiple-Input Multiple-Output (MIMO) Communications
Wireless Communications and Mobile Computing, 2004
The idea of using multiple transmit and receive antennas in wireless communication systems is one of the most important breakthroughs in communication theory during the last decade. Popularly referred to as MIMO technology, this concept can greatly improve data throughput and link performance in wireless networks. In principle, a MIMO system can operate in, or anywhere between, one of the two possible modes. If the transmitter knows the channel, then one can use spatial beamforming techniques to steer RF energy in the direction of the receiver. On the other hand, if the transmitter does not know the channel, one can use space-time coding which effectively distributes the transmitted power uniformly in all directions, and in addition augments the data with structure that can be used to combat from fading dips. Sometimes, space-time coding methods are grouped into two categories: those that focus on throughput improvement (e.g. Bell-Labs layered space-time architecture, BLAST), and those that solely aim at improving link performance (including, most notably, orthogonal block coding and transmit diversity schemes); however, this classification is simplistic and many of the currently best known schemes do not fall under any of these two groups.
Wireless Personal Communications, 2007
Transmit antenna selection in spatially multiplexed multiple-input multiple-output (MIMO) systems is a low complexity low-rate feedback technique, which involves transmission of a reduced number of streams from the maximum possible to improve the error rate performance of linear receivers. It has been shown to be effective in enhancing the performance of single-user interference-free point-to-point MIMO systems. However, performance of transmit antenna selection techniques in interference-limited environments and over frequency selective channels is less well understood. In this paper, we investigate the performance of transmit antenna selection in spatially multiplexed MIMO systems in the presence of cochannel interference. We propose a transmission technique for the downlink of a cellular MIMO system that employs transmit antenna selection to minimize the effect of co-channel interference from surrounding cells. Several transmit antenna selection algorithms are proposed and their performance is evaluated in both frequency flat and frequency selective channels. Various antenna selection algorithms proposed in the literature for single user MIMO links are extended to a cellular
Optimal Superimposed Training Design for Spatially Correlated Fading MIMO Channels
IEEE Transactions on Wireless Communications, 2008
The problem of channel estimation for spatially correlated fading multiple-input multiple-output (MIMO) systems is considered. Based on the channel's second order statistic, the minimum mean-square error (MMSE) channel estimator that works with the superimposed training signal is first developed. The problem of designing the optimal superimposed signal is then addressed and solved with an iterative optimization algorithm. Results show that under the constraint of equal training power and bandwidth efficiency, our optimal design of the superimposed training signal leads to a significant reduction in channel estimation error when compared to the conventional design of time-multiplexing training, especially for slowly timevarying channels with a large coherence time. The issue of power allocation between the information-bearing and training signals for detection enhancement is also investigated. Simulation results demonstrate excellent bit-error-rate performance of orthogonal space-time block codes with our proposed channel estimation. has been pursuing the Ph.D. degree with the School of Electrical Engineering and Telecommunications, University of New South Wales. His research interests are MIMO and MIMO-OFDM wireless communications, including coding and signal processing techniques, and applications of convex optimization for training signal and precoder design under correlated channels and colored noise.
Exploiting Spatial and Frequency Diversity in Spatially Correlated MU-MIMO Downlink Channels
Journal of Computer Networks and Communications, 2012
The effect of self-interference due to the increase of spatial correlation in a MIMO channel has become one of the limiting factors towards the implementation of future network downlink transmissions. This paper aims to reduce the effect of self-interference in a downlink multiuser-(MU-) MIMO transmission by exploiting the available spatial and frequency diversity. The subcarrier allocation scheme can exploit the frequency diversity to determine the self-interference from the ESINR metric, while the spatial diversity can be exploited by introducing the partial feedback scheme, which offers knowledge of the channel condition to the base station and further reduces the effect before the allocation process takes place. The results have shown that the proposed downlink transmission scheme offers robust bit error rate (BER) performance, even when simulated in a fully correlated channel, without imposing higher feedback requirements on the base controller.
Spatial Multiplexing in Modern Mimo Systems
2016
Digital communication using multiple-input-multiple-output (MIMO) has been regarded as one of the most significant technical breakthrough modern communications. Beside, several different open loop MIMO systems include, Spatial Multiplexing (SM) to provide diversity gain and increase the reliability of wireless links. Under suitable channel fading conditions, having both multiple transmit and multiple receive antennas (i.e., a MIMO channel) provides an additional spatial dimension for communication and yields a degree-of-freedom gain. These additional degrees of freedom can be exploited by spatially multiplexing several data streams onto the MIMO channel, and lead to an increase in the capacity: the capacity of such a MIMO channel with n transmit and receive antennas is proportional to n. Index Terms-Diversity, spatial multiplexing(SM or SMX) , , MIMO, SIC, ML, ZF..
Overview and recent challenges of MIMO systems
The primary objective of this article is to provide an overview of techniques for multiple-input multiple-output, (MIMO) wireless communication systems. Information theoretic background of the szgnificant capacity enhancement supported by MIMO radio network ,configurations is first explained. Current' activities towards the utilization of MIMO concepts in the third generation systems as well,as recent challenges in signal processing for single carrier sig-. naling-based MIMO communication systems are then introduced. '
Training-based MIMO channel estimation: a study of estimator tradeoffs and optimal training signals
IEEE Transactions on Signal Processing, 2006
In this paper, we study the performance of multipleinput multiple-output channel estimation methods using training sequences. We consider the popular linear least squares (LS) and minimum mean-square-error (MMSE) approaches and propose new scaled LS (SLS) and relaxed MMSE techniques which require less knowledge of the channel second-order statistics and/or have better performance than the conventional LS and MMSE channel estimators. The optimal choice of training signals is investigated for the aforementioned techniques. In the case of multiple LS channel estimates, the best linear unbiased estimation (BLUE) scheme for their linear combining is developed and studied. Index Terms-Multiple-input multiple-output (MIMO) channel estimation, optimal training signals. I. INTRODUCTION B ECAUSE of the growing demand for high data rates in wireless communication systems, array-based transceivers and space diversity methods have recently become an intensive area of research [1]-[7]. It has been shown both analytically and using field tests that in rich scattering environments, multipleinput multiple-output (MIMO) techniques can greatly increase the capacity of wireless systems [2], [3], [6]. However, to use the advantages that MIMO systems can offer, an accurate channel state information (CSI) is required at the transmitter and/or receiver. For example, the performance of transmit beamforming is entirely determined by the accuracy of the CSI at the transmitter. If space-time coding is used, then the availability of an accurate CSI at the receiver is crucial for the performance of space-time decoders. Therefore, an accurate channel estimation plays a key role in MIMO communications [8]-[10]. One of the most popular and widely used approaches to the MIMO channel estimation is to employ pilot signals (also referred to as training sequences) and then to estimate the channel based on the received data and the knowledge of training symbols.
MIMO channels: optimizing throughput and reducing outage by increasing multiplexing gain
TELKOMNIKA Telecommunication Computing Electronics and Control, 2020
The two main aims of deploying multiple input multiple out (MIMO) are to achieve spatial diversity (improves channel reliability) and spatial multiplexing (increase data throughput). Achieving both in a given system is impossible for now, and a trade-off has to be reached as they may be conflicting objectives. The basic concept of multiplexing: divide (multiplex) transmit a data stream several branches and transmit via several (independent) channels. In this paper, we focused mainly on achieving spatial multiplexing by modeling the channel using the diagonal Bell Labs space time scheme (D-BLAST) and the vertical Bell Labs space time architecture (V-BLAST) Matlab simulations results were a lso given to further compare the advantages of spatial multiplexing. Keywords: Diversity MIMO Multiplexing Reliability Spatial Throughput This is an open access article under the CC BY-SA license. 1. INTRODUCTION The need for and data rates and a high quality of service (QoS). Over the years, the ubiquity offered by wireless communication has made it the more preferred means over wired; hence, there has been an increase in research on how to improve the modulation schemes used over the air interface. Multiple input multiple output (MIMO) offers desirable properties that meet most of the requirement stated above. By using multiple output multiple input (MIMO) systems, diversity gain mitigates fading, increases coverage and improves QoS. Multiplexing gain increases capacity and spectral efficiency with no additional power or bandwidth expenditure [1]. The core idea under the MIMO systems is the ability to turn multi-path propagation, which is typically an obstacle in conventional wireless communication, into a benefit for users [2]. With MIMO, the capacity of a communication system increases linearly with the number of antennas, thereby achieving an increase in spectral efficiency, without requiring more resources in terms of bandwidth and power [3-5]. From Figure 1 shows that MIMO technology has two main objectives which it aims to achieve: high spatial multiplexing gain and high spatial diversity. To attain spatial multiplexing, the system is made to carry multiple data stream over one frequency, simultaneously-form multiple independent links (on same channel) between transmitter and receiver to communicate at higher data rates. In low SNR environment, spatial diversity techniques are applied to mitigate fading and the performance gain is typically expressed as diversity gain (in dB) [6]; for higher SNR facilitates the use of spatial multiplexing (SM), i.e., the transmission of parallel data streams, and information theoretic capacity in bits per second per Hertz (bits/s/Hz) is the performance measure of choice [7]. Spatial diversity works on the principle of transmission