A Low Complexity Pilot Scheduling Algorithm for Massive MIMO

Pilot contamination is a fundamental bottleneck in massive multiple-input multiple-output (MIMO) cellular networks. In this letter, we aim to design a pilot scheduling method to reduce the effect of pilot contamination in multi-user multi-cell massive MIMO systems. Mathematically, the pilot scheduling problem can be formulated as a permutation-based optimization problem. However, finding the optimal solution requires an exhaustive search and is computationally prohibitive. Therefore, we propose a low-complexity near-optimal algorithm developed from the cross-entropy optimization framework to solve this problem. Simulation results reveal that our algorithm not only significantly outperforms the existing pilot-scheduling schemes but also achieves excellent performance with low complexity

Maximizing the network outage rate for fast fluid antenna multiple access systems

Using reconfigurable fluid antennas, it is possible to have a software-controlled position-tuneable antenna to realize spatial diversity and multiplexing gains that are previously only possible using multiple antennas. Recent results illustrated that fast fluid antenna multiple access (f-FAMA) which always tunes the antenna to the position for maximum signal-to-interference ratio (SIR) on a symbol-by-symbol basis, could support hundreds of users on the same radio channel, all by a single fluid antenna at each user without complex coordination and optimization. The network outage rate, nevertheless, depends on the SIR threshold chosen for each user. Motivated by this, this paper adopts a first-order approximation to obtain the outage probability expression from which a closed-form solution is derived for optimizing the SIR threshold in maximizing the network outage rate. Moreover, a closed-form expression is provided to estimate the number of users in the f-FAMA network in which the outage rate begins to plateau. Numerical results show that the proposed SIR threshold achieves near-maximal outage rate performance

Closed-form expressions for spatial correlation parameters for performance analysis of fluid antenna systems

The emerging fluid antenna technology enables a high-density positionswitchable antenna in a small space to obtain enormous performance gains for wireless communications. To understand the theoretical performance of fluid antenna systems, it is important to account for the strong spatial correlation over the different positions (referred to as ‘ports’). Previous works used a classical, generalised correlation model to characterise the channel correlation among the ports but were limited by the lack of degree of freedom of the model to imitate the correlation structures in an actual antenna. In this letter, it is proposed to use a common correlation parameter and to choose it by setting the correlation coefficient of any two ports to be the same as the average correlation coefficient of an actual fluid antenna taking up a linear space. A closedform expression for the spatial correlation parameter is first derived assuming that the number of ports is large, and it is illustrated that the correlation parameter depends only on the size of the fluid antenna but not the port density. Simpler expressions are then obtained for small and large sizes of fluid antenna. The resulting model is finally used to study the performance of fluid antenna systems. Simulation results based on the proposed model are provided to confirm the promising performance of fluid antenna in single and multiuser environments

Robust Hybrid Beamforming Design for Multi-RIS Assisted MIMO System with Imperfect CSI

Reconfigurable intelligent surface (RIS) has been developed as a promising approach to enhance the performance of fifth-generation (5G) systems through intelligently reconfiguring the reflection elements. However, RIS-assisted beamforming design highly depends on the channel state information (CSI) and RIS’s location, which could have a significant impact on system performance. In this paper, the robust beamforming design is investigated for a RIS-assisted multiuser millimeter wave system with imperfect CSI, where the weighted sum-rate maximization problem (WSM) is formulated to jointly optimize transmit beamforming of the BS, RIS placement and reflect beamforming of the RIS. The considered WSM maximization problem includes CSI error, phase shifts matrices, transmit beamforming as well as RIS placement variables, which results in a complicated nonconvex problem. To handle this problem, the original problem is divided into a series of subproblems, where the location of RIS, transmit/reflect beamforming and CSI error are optimized iteratively. Then, a multiobjective evolutionary algorithm is introduced to gradient projection-based alternating optimization, which can alleviate the performance loss caused by the effect of imperfect CSI. Simulation results reveal that the proposed scheme can potentially enhance the performance of existing wireless communication, especially considering a desirable trade-off among beamforming gain, user priority and error factor

Questioning Classic Patient Classification Techniques in Gait Rehabilitation: Insights from Wearable Haptic Technology

Classifying stroke survivors based on their walking abilities is an important part of the gait rehabilitation process. It can act as powerful indicator of function and prognosis in both the early days after a stroke and long after a survivor receives rehabilitation. This classification often relies solely on walking speed; a quick and easy measure, with only a stopwatch needed. However, walking speed may not be the most accurate way of judging individual’s walking ability. Advances in technology mean we are now in a position where ubiquitous and wearable technologies can be used to elicit much richer measures to characterise gait. In this paper we present a case study from one of our studies, where within a homogenous group of stroke survivors (based on walking speed classification) important differences in individual results and the way they responded to rhythmic haptic cueing were identified during the piloting of a novel gait rehabilitation technique

Endogenous repair by the activation of cell survival signalling cascades during the early stages of rat parkinsonism.

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On Sparse Vector Recovery Performance in Structurally Orthogonal Matrices via LASSO

In this paper, we consider the compressed sensing problem of reconstructing a sparse signal from an undersampled set of noisy linear measurements. The regularized least squares or least absolute shrinkage and selection operator (LASSO) formulation is used for signal estimation. The measurement matrix is assumed to be constructed by concatenating several randomly orthogonal bases, which we refer to as structurally orthogonal matrices. Such measurement matrix is highly relevant to large-scale compressive sensing applications because it facilitates rapid computation and parallel processing. Using the replica method in statistical physics, we derive the mean-squared-error (MSE) formula of reconstruction over the structurally orthogonal matrix in the large-system regime. Extensive numerical experiments are provided to verify the analytical result. We then consider the analytical result to investigate the MSE behaviors of the LASSO over the structurally orthogonal matrix, with an emphasis on performance comparisons with matrices with independent and identically distributed (i.i.d.) Gaussian entries. We find that structurally orthogonal matrices are at least as good as their i.i.d. Gaussian counterparts. Thus, the use of structurally orthogonal matrices is attractive in practical applications

Spectral and Energy Efficiency of Uplink D2D Underlaid Massive MIMO Cellular Networks

CCBY One of key 5G scenarios is that device-to-device (D2D) and massive multiple-input multiple-output (MIMO) will be co-existed. However, interference in the uplink D2D underlaid massive MIMO cellular networks needs to be coordinated, due to the vast cellular and D2D transmissions. To this end, this paper introduces a spatially dynamic power control solution for mitigating the cellular-to-D2D and D2D-to-cellular interference. In particular, the proposed D2D power control policy is rather flexible including the special cases of no D2D links or using maximum transmit power. Under the considered power control, an analytical approach is developed to evaluate the spectral efficiency (SE) and energy efficiency (EE) in such networks. Thus, the exact expressions of SE for a cellular user or D2D transmitter are derived, which quantify the impacts of key system parameters such as massive MIMO antennas and D2D density. Moreover, the D2D scale properties are obtained, which provide the sufficient conditions for achieving the anticipated SE. Numerical results corroborate our analysis and show that the proposed power control solution can efficiently mitigate interference between the cellular and D2D tier. The results demonstrate that there exists the optimal D2D density for maximizing the area SE of D2D tier. In addition, the achievable EE of a cellular user can be comparable to that of a D2D user

Intelligent Omni Surface-Assisted Self-Interference Cancellation for Full-Duplex MISO System

The full-duplex (FD) communication can achieve higher spectrum efficiency than conventional half-duplex (HD) communication; however, self-interference (SI) is the key hurdle. This paper is the first work to propose the intelligent omni surface (IOS)-assisted FD multi-input single-output (MISO) FD communication systems to mitigate SI, which solves the frequency-selectivity issue. In particular, two types of IOS are proposed, energy splitting (ES)-IOS and mode switching (MS)-IOS. We aim to maximize data rate and minimize SI power by optimizing the beamforming vectors, amplitudes and phase shifts for the ES-IOS and the mode selection and phase shifts for the MS-IOS. However, the formulated problems are non-convex and challenging to tackle directly. Thus, we design alternative optimization algorithms to solve the problems iteratively. Specifically, the quadratic constraint quadratic programming (QCQP) is employed for the beamforming optimizations, amplitudes and phase shifts optimizations for the ES-IOS and phase shifts optimizations for the MS-IOS. Nevertheless, the binary variables of the MS-IOS render the mode selection optimization intractable, and then we resort to semidefinite relaxation (SDR) and Gaussian randomization procedures to solve it. Simulation results validate the proposed algorithms’ efficacy and show the effectiveness of both the IOSs in mitigating SI compared to the case without an IOS

Robust solutions to constrained optimization problems by LSTM networks

Many technical issues for communications and computer infrastructures, including resource sharing, network management and distributed analytics, can be formulated as optimization problems. Gradient-based iterative algorithms have been widely utilized to solve these problems. Much research focuses on improving the iteration convergence. However, when system parameters change, it requires a new solution from the iterative methods. Therefore, it is helpful to develop machine-learning solution frameworks that can quickly produce solutions over a range of system parameters. We propose here a learning approach to solve non-convex, constrained optimization problems. Two coupled Long Short Term Memory (LSTM) networks are used to find the optimal solution. The advantages of this new framework include: (1) near optimal solution for a given problem instance can be obtained in very few iterations (time steps) during the inference process, (2) the learning approach allows selections of various hyper-parameters to achieve desirable tradeoffs between the training time and the solution quality, and (3) the coupled-LSTM networks can be trained using system parameters with distributions different from those used during inference to generate solutions, thus enhancing the robustness of the learning technique. Numerical experiments using a dataset from Alibaba reveal that the relative discrepancy between the generated solution and the optimum is less than 1% and 0.1% after 2 and 12 iterations, respectively