New normalized constant modulus algorithms with relaxation

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A new family of blind adaptive equalization algorithms

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Residual echo signal in critically sampled subband acoustic echo cancellers based on IIR and FIR filter banks

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Energy Efficiency Optimization for Mutual-Coupling-Aware Wireless Communication System based on RIS-enhanced SWIPT

The widespread deployment of the Internet of Things (IoT) is promoting interest in simultaneous wireless information and power transfer (SWIPT), the performance of which can be further improved by employing a reconfigurable intelligent surface (RIS). In this paper, we propose a novel RIS-enhanced SWIPT system built on an electromagnetic-compliant framework. The mutual-coupling effects in the whole system are presented explicitly. Moreover, the reconfigurability of RIS is no longer expressed by the reflection-coefficient matrix but by the impedances of the tunable circuit. For comparison, both the no-coupling and the coupling-awareness cases are discussed. In particular, the energy efficiency (EE) is maximized by cooperatively optimizing the impedance parameters of the RIS elements as well as the active beamforming vectors at the base station (BS). For the coupling-awareness case, the considered problem is split into several sub-problems and solved alternatively due to its nonconvexity. Firstly, it is transformed into a more solvable form by applying the Neuman series approximation, which can be resolved iteratively. Then an alternative optimization (AO) framework and semi-definite relaxation (SDR), successive convex approximation (SCA), and Dinkelbach’s algorithm are applied to solve each sub-problem decomposed from it. Owning to the similarity between the two cases, the no-coupling one can be viewed as a reduced form of the coupling case and thus solved through a similar approach. Numerical results reveal the influence of mutual-coupling effects on the EE, especially in the RIS with closely spaced elements. In addition, physical beam designs are presented to demonstrate how the RIS assists SWIPT through various reflecting states in different conditions

Adaptive Partial Update Channel Shortening in Impulsive Noise Environments

This is a conference paper [© IEEE]. It is also available at: http://ieeexplore.ieee.org/ Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.Partial updating is an effective method for reducing computational complexity in adaptive filter implementations. In this paper adaptive partial update channel shortening algorithms in impulsive noise environments are proposed. These algorithms are based on updating a portion of the coefficients at each time sample instead of the entire set of coefficients. These algorithms have low computational complexity whilst retaining essentially identical performance to the sum-absolute autocorrelation minimization (SAAM) algorithm due to Nawaz and chambers. Simulation studies show the ability of the deterministic partial update SAAM (DPUSAAM) algorithm and the Random Partial Update SAAM (RPUSAAM)algorithm to achieve channel shortening and hence an acceptable level of bitrate within a multicarrier system

Full-rate and full-diversity extended orthogonal space-time block coding in cooperative relay networks with imperfect synchronization

This is a conference paper [© IEEE]. It is also available at: http://ieeexplore.ieee.org/ Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.In this paper we present a novel extended orthogonal space-time block coding (EO-STBC) scheme for three and four relay nodes to use in asynchronous cooperative relay networks. This approach attains full-rate and full-diversity in that each hop attains unity rate and all four uncorrelated paths are utilized. Robustness against the effects of random delays at the relay nodes is enhanced through the use of a low-rate feedback channel. A new low complexity phase feedback scheme has been proposed which can retain the advantage of the perfect feedback scheme with substantial reduction in the feedback overhead. Orthogonal frequency division multiplexing (OFDM) with cyclic prefix (CP) is used at the source node to combat the timing errors at the relay nodes, which operate in a simple amplify-and-forward (AF) mode. Simulations show that our new scheme outperforms the previous schemes and uses a very simple symbol-wise maximum-likelihood (ML) decoder

A novel adaptive leakage factor scheme for enhancement of a variable tap-length learning algorithm

In this paper a new adaptive leakage factor variable tap-length learning algorithm is proposed. Through analysis the converged difference between the segmented mean square error (MSE) of a filter formed from a number of the initial coefficients of an adaptive filter, and the MSE of the full adaptive filter, is confirmed as a function of the tap-length of the adaptive filter to be monotonically non-increasing. This analysis also provides a systematic way to select the key parameters in the fractional tap-length (FT) learning algorithm, first proposed by Gong and Cowan, to ensure convergence to permit calculation of the true tap-length of the unknown system and motivates the need for adaptation in the leakage factor during learning. A new strategy for adaptation of the leakage factor is therefore developed to satisfy these requirements with both small and large initial tap-length. Simulation results are presented which confirm the advantages of the proposed scheme over the original FT scheme

Sequential blind source separation based exclusively on second-order statistics developed for a class of periodic signals

A sequential algorithm for the blind separation of a class of periodic source signals is introduced in this paper. The algorithm is based only on second-order statistical information and exploits the assumption that the source signals have distinct periods. Separation is performed by sequentially converging to a solution which in effect diagonalizes the output covariance matrix constructed at a lag corresponding to the fundamental period of the source we select, the one with the smallest period. Simulation results for synthetic signals and real electrocardiogram recordings show that the proposed algorithm has the ability to restore statistical independence, and its performance is comparable to that of the equivariant adaptive source separation (EASI) algorithm, a benchmark high-order statistics-based sequential algorithm with similar computational complexity. The proposed algorithm is also shown to mitigate the limitation that the EASI algorithm can separate at most one Gaussian distributed source. Furthermore, the steady-state performance of the proposed algorithm is compared with that of EASI and the block-based second-order blind identification (SOBI) method

Energy Minimization in D2D-Assisted Cache-Enabled Internet of Things: A Deep Reinforcement Learning Approach

Mobile edge caching (MEC) and device-to-device (D2D) communications are two potential technologies to resolve traffic overload problems in the Internet of Things. Previous works usually investigate them separately with MEC for traffic offloading and D2D for information transmission. In this article, a joint framework consisting of MEC and cache-enabled D2D communications is proposed to minimize the energy cost of systematic traffic transmission, where file popularity and user preference are the critical criteria for small base stations (SBSs) and user devices, respectively. Under this framework, we propose a novel caching strategy, where the Markov decision process is applied to model the requesting behaviors. A novel scheme based on reinforcement learning (RL) is proposed to reveal the popularity of files as well as users' preference. In particular, a Q-learning algorithm and a deep Q-network algorithm are, respectively, applied to user devices and the SBS due to different complexities of status. To save the energy cost of systematic traffic transmission, users acquire partial traffic through D2D communications based on the cached contents and user distribution. Taking the memory limits, D2D available files, and status changing into consideration, the proposed RL algorithm enables user devices and the SBS to prefetch the optimal files while learning, which can reduce the energy cost significantly. Simulation results demonstrate the superior energy saving performance of the proposed RL-based algorithm over other existing methods under various conditions