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This article studies identification problems of multiple linear regression models, which may be described a class of multi-input multi-output systems (i.e. multivariable systems). Based on the coupling identification concept, a novel coupled-least-squares (C-LS) parameter identification algorithm is introduced for the purpose of avoiding the matrix inversion in the multivariable recursive least-squares (RLS) algorithm for estimating the parameters of the multiple linear regression models. The analysis indicates that the C-LS algorithm does not involve the matrix inversion and requires less computationally efforts than the multivariable RLS algorithm, and that the parameter estimates given by the C-LS algorithm converge to their true values. Simulation results confirm the presented convergence theorems.

The joint sparsity of uplink channels in massive multi‐input–multi‐output (MIMO) systems is explored and a block sparse model is proposed for joint channel estimation. The block coherence of this model is analysed. It is indicated that as the number of antennas at the base station grows to be infinity, the block coherence will be zero. Then a block optimised orthogonal matching pursuit (BOOMP) algorithm is proposed. Simulation results verify the analysis and show that the joint estimation using the BOOMP algorithm can significantly improve the channel estimation performance.

This study presents a novel neural network (NN)-based non-linear adaptive control strategy for the global stability of multi-input–multi-output state-control homogeneous bilinear system (BLS) at the equilibrium position. Although this class of non-linear system is neither piecewise nor feedback linearisable, conditionally stabilisable control system design can be utilised to generate multiple state transitions and corresponding control gains. The collected data was used to train a NN to obtain an optimal gain estimator. Then the optimal gain estimator was integrated into real-time control system operation to adaptively compute control gains, ensuring that the controller is continuously adjustable to changing behaviour of the system. The proposed design was shown, through an illustrative example, to overcome the stability limitations of traditional controllers for the investigated class of BLS. Furthermore, discussions about the utility of the traditional control and learning system integration, as well as stability analysis of the proposed scheme were presented.

An expression of the minimum mean square error (MMSE) of the linear MMSE channel estimation is given in the case of a non-invertible channel covariance matrix, as in single-input single-output (SISO) OFDM system. A matrix expression, already proposed for a multi-input multi-output OFDM system in a previous article, is not valid in SISO. A new proof is then proposed, by deriving a scalar expression of the MMSE, which leads to solve an optimisation problem. Furthermore, we show that the proposed solution is the global minimum. Simulations validate the proposed development.

This study presents an orthogonal design of space-time block code (STBC) that is single symbol decodable and enables full diversity and full rate transmission in a quasi-static channel environment. In the proposed scheme, full diversity is achieved by use of constellation rotation and co-ordination interpolation as is the case for the co-ordinate interleaved orthogonal design, whereas the transmission matrix of the proposed code consists of orthogonal columns with all non-zero elements, alleviating the concern about high peak-to-average power ratio. In particular, the proposed code offers lower complexity of channel estimation, and exhibits lower symbol error rate than the conventional quasi-orthogonal STBC when an maximum-likelihood decision-directed channel estimator is assumed. The advantages of the proposed code over the conventional codes are demonstrated through computer simulations. In addition, the selection of the angle of rotation for the proposed design is also presented, regarding the impact of channel estimation error.

Dynamic pilot allocation (DPA) for discrete Fourier transform (DFT)-based channel estimation in multi-input–multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems with spatial multiplexing can significantly improve the bit error rate performance compared with systems with uniform pilot allocation. However, the exhaustive search for optimum pilot allocation leads to very high complexity. The authors devise a multi-input–multi-output iterative pilot search (MIPS) algorithm applied with different MIMO-OFDM receivers (linear, successive interference cancellation (SIC) and maximum likelihood (ML)), which significantly reduces the complexity of DPA. Exact derivations are also given based on the receivers. They also propose a novel stacked vector quantisation technique to reduce feedback burdens for DPA in MIMO-OFDM system. Simulation results illustrate that the proposed MIPS algorithm with limited feedback can improve the performance of MIMO-OFDM systems.

In this study, a novel phase offset-based partial transmit sequence (PTS) scheme is proposed to reduce the peak-to-average power ratio (PAPR) in Alamouti coded multi-input–multi-output orthogonal frequency division multiplexing systems, and its key idea is that different phase rotation sequences are multiplied by their corresponding phase offsets at the transmitter. Moreover, a minimum Euclidean distance decoder is proposed to recover the phase rotation sequences at the receiver. The theoretical analysis and simulation results show that the proposed PTS scheme could offer good performances of both the bit error rate and the PAPR reduction without transmitting the side information, resulting in the increase of the data rate.

A simple and accurate statistical model (SASM) for a high-power amplifier (HPA) with memory in multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems is proposed. This model is statistically equivalent to the more complex generalised memory polynomial (GMP) model, which is one of the most accurate models. The HPA distortion is modelled using stationary white Gaussian noise sources that pass through finite impulse response (FIR) filters. The filter coefficients and the variance of noise sources are derived analytically from the GMP model parameters by spectral decomposition of the GMP noise power spectrum. The number of FIR filters is equal to the memory length of the GMP model. The HPA effect on the OFDM signal, considered as frequency-dependent attenuation coefficients, is modelled using another FIR filter. These FIR filters with small lengths make the model computationally inexpensive and mathematically tractable. The main characteristics of this model are identical bit error rate (BER) and error vector magnitude of subcarriers with those in the GMP model. The proposed model is the simplest representation of a HPA with memory effects. An excellent agreement between the simulation results of the SASM and GMP model is observed. This verifies the accuracy and efficiency of the proposed method.

This study proposes a novel user-centric cyber-physical framework to achieve distributed demand response (DDR) in a distribution system where local schedulers (LS) present at individual buses program both local and non-local loads to reduce the maximum load within a sparse communication setting. A COnjoint Methodology for communication and controller DESign (COMDES) has been proposed to design the topology of the communication network while considering various cyber and physical properties of the smart grid like bandwidth, load profile and voltage controller stability margin. The designed communication network provides a unified pathway for exchanging multiple types of data among different points in the grid to consummate the advanced ideas of DDR and multi-input multi-output voltage control concurrently. Concepts of Lyapunov stability and linear matrix inequalities are employed to design optimisation frameworks for tuning the values of voltage controllers which, in turn, play a vital role in pooling the loads to be scheduled by the individual LS for execution. Established algorithms like earliest deadline first, least laxity first and dynamic rate priority are used to generate switching schedules for redistributed loads and achieve peak shaving. IEEE 4 bus and 14 bus models have been used to demonstrate the efficacy of the proposed framework.

The design of quantised dynamic output feedback for decentralised ℋ∞ control systems is considered with multi-input and multi-output. It is assumed that a decentralised dynamic output feedback has been designed for a decentralised continuous-time LTI system, so that the closed-loop system is stable and a desired ℋ∞ disturbance attenuation level is achieved, and that each channel’s measurement outputs are quantised before they are passed to the local controller. We propose a local-output-dependent strategy for updating the quantisers’ parameters, so that the overall closed-loop system is asymptotically stable and achieves the same ℋ∞ disturbance attenuation level. Both the pre-designed controllers and the quantisers’ parameters are constructed in a decentralised manner, depending on local measurement outputs.