Maximum Likelihood Symbol Detection Research Articles

C-RAN-Type Cluster-Head-Driven UAV Relaying With Recursive Maximum Minimum Distance

In this letter, a Cloud Radio Access Network-type cluster-head-driven uplink model for multiple-antenna Unmanned Aerial Vehicles (UAV) relaying schemes is presented, which enables joint Maximum Likelihood symbol detection in the UAV cluster-head and the selection of UAV sources to communicate with each other aided by UAV-based relays. A relay selection technique, named Cluster-Head-Driven Best-Link (CHD-Best-Link), which employs cluster-head buffers and physical-layer network coding, is devised. A recursive maximum minimum distance relay selection strategy that exploits time-correlated channels and equips the CHD-Best-Link scheme is also developed. Simulations illustrate that CHD-Best-Link has superior average delay and bit error rate performances to that of previous schemes.

SBL-Based Joint Sparse Channel Estimation and Maximum Likelihood Symbol Detection in OSTBC MIMO-OFDM Systems

This paper presents sparse Bayesian learning (SBL)-based schemes for approximately sparse channel estimation in an orthogonal space-time block coded (OSTBC) multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) wireless system. The parameterized prior-based SBL framework is employed to present a pilot scheme for an ill-posed OSTBC MIMO-OFDM channel estimation scenario. Maximum likelihood symbol detection (MLSD) has been incorporated in the expectation-maximization framework for SBL-based channel estimation. This has led to the development of a novel scheme for joint approximately sparse channel estimation and symbol detection. The proposed scheme performs SBL-based channel estimation in the E-step followed by a modified ML decision metric-based symbol detection in the M-step. Bayesian Cramer–Rao bounds are obtained for the genie minimum mean-squared error estimators corresponding to the SBL schemes. Closed-form bit error probability expressions are derived for the MLSD in the presence of SBL-based channel estimation errors. Simulation results are presented towards the end to validate the theoretical bounds and illustrate the performance of the proposed techniques.

ML Symbol Detection for MIMO Systems in the Presence of Channel Estimation Errors

In wireless communication, the multiple-input multiple-output (MIMO) system is a well-known approach to improve the reliability as well as the data rate. In MIMO systems, channel state information (CSI) is typically required at the receiver to detect transmitted signals; however, in practical systems, the CSI is imperfect and contains errors, which affect the overall system performance. In this paper, we propose a novel maximum likelihood (ML) scheme for MIMO systems that is robust to the CSI errors. We apply an optimization method to estimate an instantaneous covariance matrix of the CSI errors in order to improve the detection performance. Furthermore, we propose the employment of the list sphere decoding (LSD) scheme to reduce the computational complexity, which is capable of efficiently finding a reduced set of the candidate symbol vectors for the computation of the covariance matrix of the CSI errors. An iterative detection scheme is also proposed to further improve the detection performance.

Iterative ML Symbol Detection without Timing and Phase Synchronization

This paper deals with maximum-likelihood (ML) detection of symbol sequence in the absence of synchronization information. A novel iterative scheme is proposed to obtain the ML estimates of the symbols without an estimation of synchronization parameters. Instead of the optimal sampling point recovery and explicit carrier phase compensation, the detection of symbols employs the direct calculation of the matched filter output, eliminating the need for a separate synchronizer. The detection problem is treated as ML estimation from incomplete data, which is solved by means of an iterative scheme based on the expectation-maximization algorithm. The proposed scheme is compared with conventional non-data-aided and iterative ML synchronizers. Accordingly, the simulation results indicate that the proposed detector enables improvements on both the bit error rate and convergence property.

Complexity Reduction in Joint Decoding of Block Coded Signals in Overloaded MIMO-OFDM System

This paper presents a low complexity joint decoding scheme of block coded signals in an overloaded multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system. In previous literature, a joint maximum likelihood decoding scheme of block coded signals has been evaluated through theoretical analysis. The diversity gain with block coding prevents the performance degradation induced by signal multiplexing. However, the computational complexity of the joint decoding scheme increases exponentially with the number of multiplexed signal streams. Thus, this paper proposes a two step joint decoding scheme for block coded signals. The first step of the proposed scheme calculates metrics to reduce the number of the candidate codewords using decoding based on joint maximum likelihood symbol detection. The second step of the proposed scheme carries out joint decoding on the reduced candidate codewords. It is shown that the proposed scheme reduces the complexity by about 1/174 for 4 signal stream transmission.

Hybrid Tree Search Algorithms for Detection in Spatial Multiplexing Systems

Hybrid tree search algorithms are described for maximum-likelihood symbol detection in spatial multiplexing (SM) systems. Essentially, the search tree is iteratively expanded in breadth-first (BF) manner until the probability that the current most likely path is correct exceeds the specified threshold, at which point, the depth-first (DF) stage is initiated to traverse the rest of the tree. In contrast with the sphere decoding (SD) algorithm, which starts off with the DF search, the proposed algorithms use the BF stage to enhance the accuracy of the initial DF search direction by exploiting the diversity inherent in the SM scheme. Simulation results demonstrate that, with a moderate increase in the memory requirement, the proposed algorithms achieve a significantly lower complexity than the SD algorithm in many scenarios.

ML Symbol Detection Based on the Shortest Path Algorithm for MIMO Systems

This paper presents a new maximum likelihood (ML) symbol detection algorithm for multiple-input multiple-output (MIMO) systems. To achieve the ML performance with low complexity, we search the integer points corresponding to symbol vectors in increasing order of the distance from the unconstrained least-squares solution. For each integer point, we test if it is the ML solution, and continue the integer point search until one of searched points is determined to be the ML solution. We present an efficient iterative search strategy, which is based on the shortest path algorithm for a graph. The simulation results show that the proposed algorithm has the lower complexity compared to the sphere decoding for channel matrices having low condition numbers. For further complexity reduction, we propose to use scaling, lattice-reduction, and regularization techniques. By applying these techniques, the computational complexity of proposed algorithm is reduced significantly when the channel matrix has a high condition number.

Parameter estimation and bit error rate of optimum receiver in a class‐A impulsive radio noise environment

AbstractSince most of the noise generated by electronic equipment using high‐frequency devices has statistical properties that are quite different from Gaussian noise, the bit error rate of conventional receivers is greatly degraded in a man‐made noise environment, because they are designed with the presumption of Gaussian noise. One of the methods of providing a better bit error rate in such a noise environment is the use of an optimum receiver. This optimum receiver attains a high bit error rate by using maximum likelihood symbol detection based on the statistical properties of the noise, and the statistical properties of the noise must be known a priori for optimum reception. In this paper, we use the Middleton class‐A impulsive radio noise model as the statistical model of the noise and study the parameter estimation and bit error rate of an optimum receiver in a class‐A impulsive radio noise environment. First, we evaluate the effect of errors in parameter estimation on the bit error rate of the optimum receiver in a class‐A impulsive radio noise environment, and show that to improve the performance of the optimum receiver in this noise environment, we should use parameters that express the statistics of the noise. Next we study the relationship between the number of noise samples used in estimation and the parameter estimation accuracy, and demonstrate that large numbers of noise samples are needed for highly accurate parameter estimation. However, it is shown that a quite good bit error rate can be obtained by the optimum receiver in the noise environment even if parameter estimation is based on a somewhat smaller number of noise samples (for example, about 700 samples for an impulsive index A = 0.05 and a Gaussian component ratio Γ′ = 0.05). © 2003 Wiley Periodicals, Inc. Electron Comm Jpn Pt 1, 86(8): 68–78, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecja.1173