MIMO communication systems: receiver design and diversity-multiplexing tradeoff analysis

This paper reports on investigations into the effects of spatial correlation on channel estimation and capacity of a multiple input multiple output (MIMO) system. Minimum mean square error (MMSE) method is applied for channel estimation under a correlated channel scenario. Simulation results for a 4 times 4 MIMO system with the Jakes model describing a channel between a base station and a mobile station demonstrate a trade-off between the effects of spatial correlation, channel estimation and capacity. For low values of signal to noise ratio (SNR), spatial correlation decreases channel estimation errors and improves capacity. For high values of SNR, spatial correlation has a less pronounced effect on improving channel estimation and thus the MIMO capacity is adversely affected by the decreased channel matrix rank.

This paper considers the design of a practical low-density parity check (LDPC)-coded multiple-input multiple-output (MIMO) system composed of M transmit and N receive antennas operating in a flat-fading environment where channel state information (CSI) is assumed to be unavailable both to the transmitter and the receiver. A soft iterative receiver structure is developed, which consists of three main blocks: a soft MIMO detector and two LDPC component soft decoders. We first propose at the component level several soft-input soft-output MIMO detectors whose performances are much better than the conventional minimal mean square error (MMSE)-based detectors. In particular, one optimal soft MIMO detector and two simplified suboptimal detectors are developed that do not require an explicit channel estimate and offer an effective tradeoff between complexity and performance. In addition, a modified expectation maximization (EM)-based MIMO detector is developed that completely removes positive feedback between input and output extrinsic information and provides much better performance compared with the direct EM-based detector that has strong correlations especially in fast-fading channels. At the structural level, the LDPC-coded MIMO receiver is constructed in an unconventional manner where the soft MIMO detector and LDPC variable node decoder form one super soft-decoding unit, and the LDPC check node decoder forms the other component of the iterative decoding scheme. By exploiting the proposed receiver structure, tractable extrinsic information transfer functions of the component soft decoders are obtained, which further lead to a simple and efficient LDPC code degree profile optimization algorithm with proven global optimality and guaranteed convergence from any initialization. Finally, numerical and simulation results are provided to confirm the advantages of the proposed design approach for the coded MIMO system.

This paper considers pilot-based channel estimation in large-scale multiple-input multiple-output (MIMO) communication systems, also known as “massive MIMO”. Unlike previous works on this topic, which mainly considered the impact of inter-cell disturbance due to pilot reuse (so-called pilot contamination), we are concerned with the computational complexity. The conventional minimum mean square error (MMSE) and minimum variance unbiased (MVU) channel estimators rely on inverting covariance matrices, which has cubic complexity in the multiplication of number of antennas at each side. Since this is extremely expensive when there are hundreds of antennas, we propose to approximate the inversion by an L-order matrix polynomial. A set of low-complexity Bayesian channel estimators, coined Polynomial ExpAnsion CHannel (PEACH) estimators, are introduced. The coefficients of the polynomials are optimized to yield small mean square error (MSE). We show numerically that near-optimal performance is achieved with low polynomial orders. In practice, the order L can be selected to balance between complexity and MSE. Interestingly, pilot contamination is beneficial to the PEACH estimators in the sense that smaller L can be used to achieve near-optimal MSEs.

A low-complexity algorithm based on variational Bayesian inference for MIMO channel estimation

This paper investigates the performance of IEEE 802.11ac physical layer (PHY) when the following multiple input multiple output (MIMO) detectors are implemented: Sphere Decoding (SD); QRM-Maximum Likelihood (QRM-MLD); List Minimum Mean Squared Error Successive Interference Cancellation (List MMSE-SIC); List Zero-Forcing SIC (List ZF-SIC); Lattice Reduction MMSE-SIC (LR MMSE-SIC) and List LR MMSE-SIC. The conclusions are founded on simulation results, validated using first order techniques, over TGac channel models. The IEEE 802.11ac simulator implements hard-decision Viterbi decoding and realistic synchronization and channel estimation algorithms. The List MMSE-SIC scheme presents power gains around 2 dB in relation to the List ZF-SIC MIMO detector. The QRM-MLD MIMO detector has a highly superior performance regarding to the List MMSE-SIC MIMO detector since this latter scheme cannot overcome the propagation errors that degrade the performance of MMSE-SIC MIMO detectors. However, for moderate list lengths of 4 and 8, the QRM-MLD MIMO detector presents severe power losses in relation to the optimal SD MIMO detector. The LR MMSE-SIC MIMO detector allows power losses of only 1–2 dB in reference to the optimal SD MIMO receiver. There are no noticeable power gains when List detection techniques in MMSE-SIC MIMO detectors are implemented since the diversity gains due to implementation of LR basis change overcome the error propagation in SIC MIMO detectors with original channel basis.

This paper describes investigations into the antenna mutual coupling (MC) effect on channel estimation and capacity of a multiple-input multiple-output (MIMO) wireless communication system. The presented investigations close the gap existing in the previous works which assessed the effect of mutual coupling on MIMO capacity under the assumption of availability of perfect channel state information (CSI) at the receiver. The new approach assumes that the perfect CSI is not available due to channel estimation errors. The investigations are carried out for different spacing between array antenna elements producing a varying effect of mutual coupling on the channel estimation and the resulting MIMO channel capacity.

Multiple Input Multiple Output (MIMO) system with orthogonal frequency division multiplexing (OFDM) is a promising physical layer candidate for the current and future wireless standards. MIMO increases system throughput while OFDM maximizes spectrum efficiency. To reduce the impacts of frequency selective fading and increase the capacity, MIMO requires precise channel state information (CSI). The presence of carrier frequency offset (CFO) introduces intercarrier interference (ICI) among the subcarriers which degrades the system performance. MIMO channel estimation is done under the assumption of perfect frequency synchronisation between the transmitter and receiver, or zero CFO. However, such an assumption is incorrect for time-varying and frequency-selective wireless channels, and the presence of CFO has an impact on channel estimation accuracy. In the existing methods, CFO and channel estimations are carried out independently using separate training sequences which reduces the spectral efficiency. As a result, the pilot aided interpolation approach is proposed in this paper for the joint estimate and compensation of CFO and channel effects using the same set of pilot tones. Theresults show that the proposed method offers precise CFO and channel estimation which improves the bit error rate performance and carrier to interference power ratio (CIR) of MIMO-OFDM system.

In recent years, multiple-input multiple-output (MIMO) systems, which use several transmit and receive antennas, have attracted much attention for high performance radio systems. In MIMO systems, the channel estimation is important to distinguish transmit signals from multiple transmit antennas. While the space-alternating generalized expectation-maximization (SAGE) algorithm is known to offer good channel estimation and data detection. We proposed earlier the minimum mean square error (MMSE)-based SAGE algorithm for MIMO systems where the MMSE estimation is used for channel estimation. We showed that the proposed MMSE-based SAGE algorithm can achieve the better bit error rate (BER) than the maximum likelihood (ML) detection with training symbols. The MMSE channel estimation needs the knowledge of the maximum Doppler frequency F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sub> for deriving the covariance matrix of the channel and the variance sigma <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of additive white Gaussian noise (AWGN). Additionally, the computation of the MMSE channel estimation requires O(L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) operations where L is the transmitted frame length. Thus, its computational complexity is high. In this paper, we propose a zero-forcing (ZF)-based SAGE algorithm for channel estimation and data detection in MIMO systems that does not need the knowledge of Fd and sigma <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . Since the computation of the proposed ZF-based SAGE algorithm requires O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) operations where N is the number of transmit antennas, its computational complexity is low. We show that the proposed ZF-based tracking SAGE algorithm with less computational complexity can achieve almost the same BER as that of the MMSE-based tracking SAGE algorithm

For channel estimation in the relay-assisted multiple-input multiple-output and orthogonal frequency division multiplexing indoor visible light communication (VLC) system affected by light source, there is a problem that the received signal cannot be separated by simple filtering. To solve this problem, a channel estimation by PARATUCK2-PARAFAC-based noise compensation which introduces tensors to construct the suitable model of VLC is proposed. First, the transmitting pilots for channel estimation and noise estimation are constructed separately with the Hermitian symmetry structure. Then the tensor-based transmission model of the indoor VLC system is established, and the signals for channel and noise estimations are received. Finally, an asymptotic estimation for separating signals from different sources and noise is proposed by PARATUCK2-PARAFAC. Simulation results show that the proposed algorithm provides a more effective scheme for channel estimation comparing with the state-of-the-art algorithms.

Recently, technological developments have enhanced, the use of Millimeter-wave (mm-Wave) Multiple Input Multiple Output (MIMO) system in various communication applications and wireless sensor networks as channel estimation efficiency can be immensely improved with the help of this technological developments in Millimeter-wave MIMO system and wireless sensor network as well. Moreover, they can improve quality of communication services to a great extent. However, cell interference in Millimeter-wave (mm-Wave) MIMO system can produce a massive impact on spectral efficiency. Therefore, a Routing Enabled Channel Estimation (RECE) technique is presented in this article to minimize interference between cells. The proposed Channel Estimation technique improves channel capacity as well as spectral efficiency. Moreover, Normalized Mean Square Error (NMSE) is minimized heavily using proposed RECE technique. Here, main aim of this article is to reduce cell interference and channel estimation inside a cell by using route selection, beam selection, and spatial frequency estimation. Here, different scenarios and parameters are considered to evaluate performance efficiency of proposed RECE technique in terms of spectral efficiency, NMSE and SNR and compared against varied traditional channel estimation techniques. Moreover, it is clearly evident from performance results that the proposed channel estimation technique performs better than the other two methods.

&lt;p&gt;Multiple-input multiple-output (MIMO) systems play a vital role in fourth generation wireless systems to provide advanced data rate. In this paper, a better performance and reduced complexity channel estimation method is proposed for MIMO systems based on matrix factorization. This technique is applied on training based least squares (LS) channel estimation using STBC for performance improvement. Simulation results indicate that the proposed method not only alleviates the performance of MIMO channel estimation but also significantly reduces the complexity caused by matrix inversion. The performance evaluations are validated through computer simulations using MATLAB in terms of bit error rate (BER) for modified LS with LS and MMSE channel estimation techniques. Simulation results show that the BER performance and complexity of the proposed method clearly outperforms the conventional LS channel estimation method.&lt;/p&gt;&lt;p&gt;&lt;em&gt;Journal of Advanced College of Engineering and Management, Vol. 1, 2015&lt;/em&gt;, pp. 11-24&lt;/p&gt;

A multiple-input multiple-output (MIMO) communication system combined with the orthogonal frequency partition multiplexing (OFDM) modulation technique can achieve reliable high data rate show over broadband wireless channels.Channel state information for both single-input single-output (SISO) and MIMO systems based on pilot aided arrangement is investigated in this paper.The estimate of channel at pilot frequencies with conventional Least Square (LS) and Minimum Mean Square (MMSE) estimation algorithms is carried out through Matlab simulation.For outdoor communicationscenarios, where wireless channels are sparse in nature, pathinterruptions of different transmit-receive antenna couples share a jointsparse decoration due to the spatial correlation of MIMO channels.By simultaneously exploiting those MIMOchannel characteristics, the proposed scheme performs better thanpresent state-of-the-art systems.Furthermore, by joint processingof signals associated with different antennas, the pilot overheadcan be reduced the performance of MIMO OFDM and SISO OFDM are assessed on the basis of Bit Error Rate (BER) and Mean Square Error (MSE) level.Further enhancement of presentation can be attained through maximum diversity Space Time Block Coding and Maximum Likelihood Detection at transmission and reception ends respectively.MMSE estimation has been shown to perform much better than LS but is more complex than LS for the MIMO system using pilot carriers.

Space-time techniques for multiple-input multiple-output (MIMO) systems potentially provide vast increases in capacity. In order to achieve the quoted capacity gains the MIMO channel impulse response (CIR) must be known or estimated. Thus far, existing MIMO channel estimation techniques have been limited to the narrowband case or cater specifically for coded space-time systems. In this paper, we present a novel training-based MIMO channel estimation scheme for an uncoded layered space-time system that operates in the wideband frequency-selective fading environment. The method uses a pilot matrix consisting of pilot symbols derived from the Paley-Hadamard matrix in order to jointly estimate the individual CIR of the MIMO channel. The orthogonal property of the pilot matrix is utilized to resolve both intersymbol interference and intercarrier interference of the multiple transmit and received signals. The Paley Toeplitz structure is also exploited in order to minimise the length of the pilot sequence for a given length of CIR and thus maximise the effective data throughput. Results are presented which demonstrate the accuracy of the proposed channel estimation scheme and its performance in a layered space-time system.

This paper presents a novel iterative detection and channel estimation scheme that combines the effort of superimposed training (ST) and pilot-aided training (PAT) for multiple-input multiple-output (MIMO) flat fading channels. The proposed method, hereafter known as joint mean removal ST and PAT (MRST-PAT), implements an iterative detection and channel estimation that achieves the performance of data-dependent ST (DDST) algorithm, with the difference that the data arithmetic cyclic mean is estimated and removed from data at the receiver’s end. It is demonstrated that this iterative and cooperative detection and channel estimator algorithm surpasses the effects of data detection identifiability condition that DDST has shown when higher orders of modulation are used. Theoretical performance of the MRST-PAT scheme is provided and corroborated by numerical simulations. In addition, the performance comparison between the proposed method and different MIMO channel estimation techniques is analyzed. The joint effort between ST and PAT shows that MRST-PAT is a solid candidate in communications systems for multiamplitude constellations in Rayleigh fading channels, while achieving high-throughput data rates with manageable complexity and bit-error rate (BER) as a figure of merit.

This chapter is attempted to provide a survey of the advanced concepts and related issues involved in Multiple Input Multiple Output (MIMO) systems. MIMO system technology has been considered as a really significant foundation on which to build the next and future generations of wireless networks. The chapter addresses advanced MIMO techniques such as polarization diversity and antenna selection. We gradually provide an overview of the MIMO features from basic to more advanced topics. The first sections of this chapter start by introducing the key aspects of theMIMO theory. TheMIMO systemmodel is first presented in a genericway. Then, we proceed to describe diversity schemes used in MIMO systems. MIMO technology could exploit several diversity techniques beyond the spatial diversity. These techniques essentially cover frequency diversity, time diversity and polarization diversity. We further provide the reader with a geometrically based models for MIMO systems. The virtue of this channel modeling is to adopt realisticmethods for modeling the spatio-temporal channel statistics from a physical wave-propagation viewpoint. Two classes for MIMO channel modeling will be described. These models involve the Geometry-based Stochastic ChannelModels (GSCM) and the Stochastic channel models. Besides the listedMIMO channel models already described, we derive and discuss capacity formulas for transmission over MIMO systems. The achieved MIMO capacities highlight the potential of spatial diversity for improving the spectral efficiency of MIMO channels. When Channel State Information (CSI) is available at both ends of the transmission link, the MIMO system capacity is optimally derived by using adaptive power allocation based on water-filling technique. The chapter continues by examining the combining techniques for multiple antenna systems. Combining techniques are motivated for MIMO systems since they enable the signal to noise ratio (SNR) maximization at the combiner output. The fundamental combing techniques are the Maximal Ratio Combining (MRC), the Selection Combining (SC) and the Equal Gain Combining(EGC). Once the combining techniques are analyzed, the reader is introduced to the beamforming processing as an optimal strategy for combining. The use of multiple antennas significantly improves the channel spectral efficiency. Nevertheless, this induces higher system complexity of the communication system and the communication system performance is effected due to correlation between antennas that need to be deployed at the same terminal. As such, the antenna selection algorithm for MIMO systems is presented. To elaborate on this point, we introduce Space time coding techniques for MIMO systems and we evaluate by simulation the performance of the communication system. Next, we emphasis on multi polarization techniques for MIMO systems. As a background, we presume that the reader has a thorough understanding of antenna theory. We recall the basic antenna theory and concepts that are used throughout the rest of the chapter. We rigorously introduce the 3D channel model over the Non-Line of Sight (NLOS) propagation channel for MIMO system with polarized antennas. We treat the depolarization phenomena and we study its effect on MIMO system capacity. The last section of the chapter provides a scenario for collaborative sensor nodes performing distributed MIMO system model which is devoted to sensor node localization in Wireless Sensor Networks. The localization algorithm is based on beamforming processing and was tested by simulation. Our chapter provides the reader by simulation examples for almost all the topics that have been treated for MIMO system development and key issues affecting achieved performance.