Projected parallel interference cancellation multiuser detector for asynchronous upstream OCDMA-PON

OCDMA is a promising candidate for next generation passive optical networks (NG-PON). OCDMA-PON can potentially provide all customers with a Gb/s-class BW upstream with inherent flexibility. Unfortunately OCDMA suffers from multiple access interference (MAI) and various detection noises. To mitigate MAI, we propose a novel parallel interference cancellation technique for incoherent DS-OCDMA that better exploits the non-negativity of the solution. The proposed PIC scheme incorporates this additional information into its structure. The proposed PIC detector is shown to outperform the conventional correlator detector, the decorrelator detector, the linear parallel interference Cancellation (LPIC) detector and even the linear minimum mean square error (LMMSE) detector.

We generalize the multiuser (CDMA spread spectrum) communication systems to the fading environments. We first extend Verdu's (1986) conventional optimum receiver to Rayleigh-fading environments and then evaluate its performance. Having no knowledge of the received power at the receiving end, we therefore need an estimator to efficiently estimate the received signal strength of each user in fading environments. A linear minimum mean square error (LMMSE) unbiased estimator is proposed to attain this goal. By using the minimum mean square error (MMSE) Bayesian estimation, we further propose the LMMSE bit estimator for efficient demodulation. Its performance is close to the optimum multiuser receiver but with a much simpler polynomial complexity. To further reduce the complexity, we extend the LMMSE estimator to the sequential LMMSE estimator. In sequential estimation, we do not need to implement the matched filter banks and to perform the matrix inversion when estimating. In addition, it converges after approximately 2k iterations, where k is the number of users. With this fast convergence property and the simple structure, the sequential LMMSE estimator provides an attractive alternative to the implementation of a multiuser system.

In a code-division multiple-access system, multiuser detection (MUD) can exploit the information of signals from other interfering users to increase system capacity. However, the optimum MUD can be characterized as an NP-hard combinatorial optimization problem such that the computational complexity increases exponentially with the number of users. In this paper, we apply a new evolutionary algorithm, called particle swarm optimization (PSO), to develop a suboptimal MUD strategy. The decorrelating detector (DD) or linear minimum mean square error (LMMSE) detector is used as the first stage to initialize the PSO-based MUD. Then, the PSO algorithm is applied to detect the received data bit by optimizing an objective function incorporating the linear system of the DD or LMMSE detector. Simulation results show that the performance of our proposed decorrelating PSO and LMMSE-PSO MUD are promising and outperform the decorrelating and LMMSE MUD with a slight increase in computation.

Ever since the adoption of the single carrier frequency division multiple access (SC-FDMA) scheme for the uplink in the Long Term Evolution (LTE) standard, there has been much interest in improved receiver algorithms for the same. Successive interference cancelation (SIC) based turbo receivers (Turbo SIC) have been proposed to tackle the multi-stream interference (MSI) for transmission modes such as spatial multiplexing (SM) and multiuser (MU) multiple input multiple output (MIMO). In addition, decision feedback equalizers (DFE) have been discussed as an alternative to the simple linear minimum mean square error (LMMSE) based receivers to suppress intersymbol interference (ISI) caused by the frequency selective multi-path channel. This paper proposes a DFE receiver variant based on parallel interference cancelation (PIC) with maximum likelihood detection (MLD) and ordered SIC (OSIC) principles to suppress ISI and MSI. Simulations show that the proposed receiver performs better than the existing SIC and DFE based receivers and achieves the matched filter bound (MFB) for both coded and uncoded systems.

Photon-counting detectors are greatly improving the resolution and image quality in computed tomography (CT) technology. The drawback is, however, that the reconstruction becomes more challenging. This is because there is a considerable increment of the processing data due to the multiple energy bins and materials in the reconstruction analysis, as well as improved resolution. Yet efficient material decomposition and reconstruction methods tend to generate noisy images that do not completely satisfy the expected image quality. Therefore, there is a need for efficient denoising of the resulting material images. We present a new and fast denoiser that is based on a linear minimum mean square error (LMMSE) estimator. The LMMSE is very fast to compute, but not commonly used for CT image denoising, probably due to its inability to adapt the amount of denoising to different parts of the image and the difficulty to derive accurate statistical properties from the CT data. To overcome these problems we propose a model-based deep learning strategy, that is, a deep neural network that preserves an LMMSE structure (model-based), providing more robustness unseen data, as well as good interpretability to the result. In this way, the solution adapts to the anatomy in every point of the image and noise properties at that particular location. In order to asses the performance of the new method, we compare it to both to a conventional LMMSE estimator and to a “black-box” CNN in a simulation study with anthropomorphic phantoms.

LMMSE and MAP estimators for reduction of multiplicative noise in the nonsubsampled contourlet domain

The author proposed soft decision feedback equalisation for orthogonal frequency division multiplexing (OFDM) transmission over doubly selective channels which is used for turbo equalisation. The doubly selective channel is approximated using the basis expansion model (BEM). The decision feedback equaliser (DFE) is obtained and updated for every sub-carrier which results in a per-tone DFE. On the basis of the Gaussian approximation, expressions for the a priori, the a posteriori and the extrinsic information have been derived for quadrature phase shift keying (QPSK) modulation. For turbo equalisation, where iterative joint channel equalisation and channel decoding is executed, in the first iteration, estimates are based on the feed-forward filter which are fed to the channel decoder. These estimates are not reliable and may incur error propagation and poor turbo equalisation performance. For more reliable estimates in the first iteration, the DFE is initialised as a linear minimum mean square error (LMMSE) equaliser. The per-tone LMMSE soft-output equaliser is also derived, and expressions for the a priori, a posteriori and extrinsic information are obtained viewing the LMMSE equaliser as a special case of the DFE. The extrinsic information in both cases was derived based on the Gaussian approximation. Numerical results for bit-error rate performance assuming the BEM channel model and Jakes' channel model are shown to confirm our findings.

A decision-directed (DD) channel estimation based on iterative linear minimum mean square error (LMMSE) is proposed for orthogonal frequency division multiplexing systems. Existing DD channel estimation is well known to have the problem of error propagation because of symbol-by-symbol detection. The proposed algorithm can estimate the correction term of current channel state information (CSI) according to the error vector of previous CSI by applying the orthogonality principle, and corrects the current CSI with this correction term. Analysis and simulation results have shown that this method has no error propagation problem. The performance of the proposed algorithm is much better than the conventional DD channel estimation, and close to the optimal LMMSE estimator, but with much less computational complexity compared with the optimal LMMSE estimator.

A method based on measurement correlation (MC) and linear minimum mean square error (LMMSE) for multichannel surface electromyography (sEMG) signal decomposition was developed in this study. This MC-LMMSE method gradually and iteratively increases the correlation between an optimized vector and a reconstructed matrix that is correlated with the measurement matrix. The performance of the proposed MC-LMMSE method was evaluated with both simulated and experimental sEMG signals. Simulation results show that the MC-LMMSE method can successfully reconstruct up to 53 innervation pulse trains with a true positive rate greater than 95%. The performance of the MC-LMMSE method was also evaluated using experimental sEMG signals collected with a 64-channel electrode array from the first dorsal interosseous muscles of three subjects at different contraction levels. A maximum of 16 motor units were successfully extracted from these multichannel experimental sEMG signals. The performance of the MC-LMMSE method was further evaluated with multichannel experimental sEMG data by using the “two sources” method. The large population of common MUs extracted from the two independent subgroups of sEMG signals demonstrates the reliability of the MC-LMMSE method in multichannel sEMG decomposition.

Channel estimation (CE) is a challenging problem and one of the key technologies in Orthogonal Frequency Division Multiplexing (OFDM) systems. The Linear Minimum Mean Square Error (LMMSE) proposed for OFDM systems has excellent Mean Square Error (MSE) performance. However, the conventional method requires the statistical knowledge of the channel information in advance and the complicated inverse operation of large dimension matrices. In this case, an enhanced fast LMMSE channel estimation using Fast Fourier Transform (FFT) operation and the most significant taps (MST) algorithm for OFDM systems is proposed. This method has the advantage of low computational complexity and no requirement on the detailed channel information. In addition, simulation results demonstrate that the proposed algorithm outperforms other LMMSE methods in terms of bit error rate (BER) without the loss of MSE performance.

We derive the Linear Minimum Mean Square Error (LMMSE) receiver for Wyner's model of a linear cellular array. By exploiting the special structure of the channel model, we obtain explicit Mean Square Error (MSE) expressions and information capacity formulae for both finite-sized as well as infinite-sized linear cellular arrays. We show that the performance of an individual user becomes insensitive to the array size for large array sizes and that the proportion of users achieving almost the same performance converges to one, as the array size tends to infinity. The effects of intercell interference and noise on the performance of each user are also investigated.

We consider a wirelessly powered Internet of Things (IoT) based on cell-free massive MIMO with energy harvesting. In such a system, during the downlink phase, the sensors harvest radio-frequency (RF) energy emitted by the distributed access points (APs). During the uplink phase, sensors transmit data to the APs using the harvested energy. We assume that single antenna sensors send uplink pilots in order to allow APs to detect active users and estimate their channel coefficients. We assume that each AP is equipped with N$\geq$1 antennas and uses the linear minimum mean square error (LMMSE) channel estimation. Through an asymptotic analysis, we derive closedform approximations for the variance of the LMMSE channel coefficient estimates, the amount of harvested energy, and the achievable rates for the uplink data transmission. We next use these expressions to jointly optimize the charging duration and uplink and downlink power control coefficients to minimize the total transmit energy consumptions of APs and sensors, which is crucially important for IoT sensors. Simulation results verify the accuracy of the obtained expressions, and shows that significant gains in energy efficiency can be achieved by the proposed optimization algorithms.

An energy-efficient distributed detection method for wireless sensor networks is considered in this paper. It is assumed that a group of sensors observe the same quantity in independent additive observation noises with possibly different variances. We assume that the links between the nodes and the fusion center are subject to Rayleigh fading wireless channels. At the fusion center, incorporating the effect of fading in the detection process, a decision fusion rule based on the linear minimum mean square error(LMMSE) is investigated. In this paper, two types of constraints are considered: 1) transmission power constraints at the nodes, and 2) the communication channel between the nodes and the decision center. we propose a optimal distributed estimation algorithm in energy-constrained wireless sensor networks. By numerical examples, we illustrate the performance of the proposed serial detection scheme.

Visible light communication (VLC) has been proposed as a promising way for next generation wireless communication networks to mitigate the scarcity of the radio frequency (RF) spectrum, and has consequently attracted much attention. This paper introduces a single-input single-output (SISO) VLC system under the joint effects of statistical random channel and signal-dependent shot noise (SDSN). Moreover, it estimates the channel of the considered system using maximum likelihood (ML), least square (LS), linear minimum mean square error (LMMSE), maximum posteriori probability (MAP) and minimum mean square error (MMSE) estimators. Furthermore, a Bayesian Cramér-Rao lower bound (BCRLB) is derived for the proposed system and it is compared to the mean square error (MSE) of the proposed estimators. The problem of unknown SDSN factor, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\zeta ^{2}$</tex-math></inline-formula> , at the receiver side is discussed and two solutions are investigated. The receiver of a VLC system under SDSN and random channel gain <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$h$</tex-math></inline-formula> is designed and its BER is studied. Finally, Monte Carlo simulation results of the proposed estimators, which show the dramatic effect of the SDSN on the considered system, are provided. In particular, the presence of noise variance, as well as the SDSN factor, causes an increase in the MSE of the system, while increasing the power reinforces the system performance.

With the increasing rate of wireless devices and high bandwidth operations, wireless networking and communications are becoming over crowded. To cope with such crowded and messy situation, massive MIMO is designed to work with hundreds of low-cost serving antennas at a time as well as improve the spectral efficiency at the same time. Time Division Duplexing (TDD) has been used for gaining beamforming which is a major part of massive MIMO, to gain its best improvement to transmit and receive pilot sequences. All the benefits are only possible if the channel state information or channel estimation is gained properly. Least Square (LS), Minimum Mean Square Error (MMSE) and Linear Minimum Mean Square Error (LMMSE) are the estimators commonly used for estimating channel matrices. We have optimized these methods using genetic algorithm to minimize the mean squared error and finding the best channel matrix from existing algorithms with less computational complexity. Our simulation result has shown that the use of Genetic Algorithm (GA) worked well on existing algorithms in a Rayleigh slow fading channel and existence of Additive White Gaussian Noise. We found that the GA optimized LS is better than existing algorithms. GA provides optimal result in some few iterations in terms of Mean Square Error (MSE) with respect to SNRand computational complexity.