Search Results for author:
Found 10 papers, 1 papers with code
Joint Sequential Fronthaul Quantization and Hardware Complexity Reduction in Uplink Cell-Free Massive MIMO Networks
We show that 1) de-correlating the received signal vector at each AP from the corresponding vectors of the previous APs (inter-AP de-correlation) and 2) de-correlating the dimensions of the received signal vector at each AP (intra-AP de-correlation) before quantization helps to use the quantization bits at each AP more efficiently than directly quantizing the received signal vector without any pre-processing and consequently, improves the bit error rate (BER) and normalized mean square error (NMSE) of users signal estimation.
Sequential Processing in Cell-free Massive MIMO Uplink with Limited Memory Access Points
However, we show that in case of limited memory capacity at each AP, the memory capacity to store the received signal vectors at the final AP of this fronthaul becomes a limiting factor.
Zeroth-order Asynchronous Learning with Bounded Delays with a Use-case in Resource Allocation in Communication Networks
Distributed optimization has experienced a significant surge in interest due to its wide-ranging applications in distributed learning and adaptation.
A Deep Learning Based Resource Allocator for Communication Systems with Dynamic User Utility Demands
The optimization algorithm aims to optimize the on-off status of users in a time-sharing problem to satisfy their utility demands in expectation.
Distributed Adaptive Norm Estimation for Blind System Identification in Wireless Sensor Networks
Distributed signal-processing algorithms in (wireless) sensor networks often aim to decentralize processing tasks to reduce communication cost and computational complexity or avoid reliance on a single device (i. e., fusion center) for processing.
Sampling Rate Offset Estimation and Compensation for Distributed Adaptive Node-Specific Signal Estimation in Wireless Acoustic Sensor Networks
Sampling rate offsets (SROs) between devices in a heterogeneous wireless acoustic sensor network (WASN) can hinder the ability of distributed adaptive algorithms to perform as intended when they rely on coherent signal processing.
SPIRAL: A superlinearly convergent incremental proximal algorithm for nonconvex finite sum minimization
We introduce SPIRAL, a SuPerlinearly convergent Incremental pRoximal ALgorithm, for solving nonconvex regularized finite sum problems under a relative smoothness assumption.
Transfer functions of FXLMS-based Multi-channel Multi-tone Active Noise Equalizers
In this work, we show how to calculate these transfer functions with a double aim: to verify that at the frequencies of interest the values imposed by the equalizer settings are obtained, and to characterize the behavior of these transfer functions in the rest of the spectrum, as well as to get clues to predict the convergence behaviour of the algorithm.
GEVD-based Low-Rank Channel Covariance Matrix Estimation and MMSE Channel Estimation for Uplink Cellular Massive MIMO Systems
Therefore, a new channel covariance matrix estimator for low-rank channel covariance matrices is presented in this paper, using a generalized eigenvalue decomposition (GEVD) of two covariance matrices that can be estimated from the available uplink data.
Intersymbol and Intercarrier Interference in OFDM Systems: Unified Formulation and Analysis
A new equivalent channel matrix that is useful for calculating both the received signal and the intersymbol and intercarrier interference power is defined and characterized.
