Iterative Time-Varying Filter Algorithm Based on Discrete Linear Chirp Transform

Denoising of broadband non--stationary signals is a challenging problem in communication systems. In this paper, we introduce a time-varying filter algorithm based on the discrete linear chirp transform (DLCT), which provides local signal decomposition in terms of linear chirps. The method relies on the ability of the DLCT for providing a sparse representation to a wide class of broadband signals. The performance of the proposed algorithm is compared with the discrete fractional Fourier transform (DFrFT) filtering algorithm. Simulation results show that the DLCT algorithm provides better performance than the DFrFT algorithm and consequently achieves high quality filtering.Comment: 6 pages, conference pape

On the information freshness and tail latency trade-off in mobile networks

Abstract With the advent of emerging mission-critical applications, sampling information updates and scheduling mobile traffic in a timely manner are very challenging. In addition, maintaining fresh information and low latency communication is important to these applications. To that end, in this paper, we first derive closed-form expressions for the latency tail probability (LTP) and the average age of information (AoI) in M/G/1 systems, where shifted exponential service time is considered. Different from the majority of existing work in this domain, our analysis is derived assuming different update sizes with different priority levels. Next, we have developed novel policies for sampling and scheduling the information updates over the choice of one (or a set) of the parallel links, e.g., WiFi and LTE links. Then, a joint minimization of AoI and LTP is formulated and efficient algorithms are provided. Unlike queue-based policies, our scheduling approach over parallel links enjoys two key advantages. First, the scheduling decision is independent of the queue length and is thus less complex. Second, it can differentiate the updates of the apps to further prioritize the very-timely sensitive information (e.g control signals) over other messages that can tolerate more delay (e.g., status updates). Our evaluation results show significant improvements of the proposed approaches as compared to the state-of-the-art algorithms

A low-complexity detection algorithm for uplink massive MIMO systems based on alternating minimization

Abstract In this letter, we propose an algorithm based on the alternating minimization technique to solve the uplink massive multiple-input multiple-output (MIMO) detection problem. The proposed algorithm is specifically designed to avoid any matrix inversion and any computations of the Gram matrix at the receiver. The algorithm provides a lower complexity compared to the conventional minimum mean square error detection technique, especially when the total number of user equipment antennas (across all users) is close to the number of base station antennas. The idea is that the algorithm re-formulates the maximum-likelihood detection problem as a sum of convex functions based on decomposing the received vector into multiple vectors. Each vector represents the contribution of one of the transmitted symbols in the received vector. Alternating minimization is used to solve the new formulated problem in an iterative manner with a closed-form solution update in every iteration. Simulation results demonstrate the efficacy of the proposed algorithm in the uplink massive MIMO setting for both coded and uncoded cases

A proximal Jacobian ADMM approach for fast massive MIMO signal detection in low-latency communications

Abstract One of the 5G promises is to provide Ultra Reliable Low Latency Communications (URLLC) which targets an end to end communication latency that is <; 1ms. The very low latency requirement of URLLC entails a lot of work in all networking layers. In this paper, we focus on the physical layer, and in particular, we propose a novel formulation of the massive MIMO uplink detection problem. We introduce an objective function that is a sum of strictly convex and separable functions based on decomposing the received vector into multiple vectors. Each vector represents the contribution of one of the transmitted symbols in the received vector. Proximal Jacobian Alternating Direction Method of Multipliers (PJADMM) is used to solve the new formulated problem in an iterative manner where at every iteration all variables are updated in parallel and in a closed form expression. The proposed algorithm provides a lower complexity and much faster processing time compared to the conventional MMSE detection technique and other iterative-based techniques, especially when the number of single antenna users is close to the number of base station (BS) antennas. This improvement is obtained without any matrix inversion. Simulation results demonstrate the efficacy of the proposed algorithm in reducing detection processing time in the multi-user uplink massive MIMO setting

Proceedings of First Conference for Engineering Sciences and Technology: Vol. 1

This volume contains contributed articles of Track 1, Track 2 &amp; Track 3, presented in the conference CEST-2018, organized by&nbsp;Faculty of Engineering Garaboulli, and Faculty of Engineering, Al-khoms, Elmergib University (Libya) on 25-27 September 2018. Track 1: Communication and Information Technology Track 2: Electrical and Electronics Engineering Track 3: Oil and Chemical Engineering Other articles of Track 4, 5 &amp; 6 have been published in volume 2 of the proceedings at this lin