arXiv subjects

Navid Reyhanian

Publications and source records attributed to Navid Reyhanian.

9 recordsLinked to original sources

arXiv2026

Mask-Compliant Clipping-Aware Precoding for Multi-User MIMO-OFDM Systems

Orthogonal frequency-division multiplexing (OFDM) is widely adopted in frequency-selective channels for its ability to simplify equalization, yet it also causes large signal peaks, out-of-band (OOB) emissions, and spectral leakage. We study downlink precoding/combining for multi-user multiple-input multiple-output (MU-MIMO)--OFDM systems by minimizing the sum of the users' mean-squared errors (MSEs) under per-subcarrier transmit-power limits, per-antenna OOB spectral-mask constraints, and per-antenna peak-amplitude (clipping) constraints, which confine the emitted spectrum and limit waveform peaks to reduce power-amplifier saturation, nonlinear distortion, and spectral regrowth. The resulting nonconvex problem is handled by a minimum mean-squared error (MMSE) based block coordinate descent (BCD), with a closed-form combiner update and an alternating direction method of multipliers (ADMM) algorithm with closed-form updates for the precoder subproblem. Simulations show clear gains over well-known benchmark schemes.

eess.SP↗

arXiv2026

Joint Spatial and Spectral Hybrid Precoding for Multi-User MIMO-OFDM Systems

Millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems operate over wide bandwidths and frequency-selective channels, making orthogonal frequency-division multiplexing (OFDM) a natural transmission scheme. In such systems, fully digital precoding is often impractical because the large antenna arrays require high hardware cost and power consumption, so hybrid precoding that combines digital and radio frequency (RF) processing is an attractive alternative. However, OFDM introduces high signal peaks that may cause clipping and generate out-of-band (OOB) emissions, while practical, nonideal phase shifters (PSs) at the RF precoder and user combiner suffer from phase errors. We study the problem of robust digital-RF precoding optimization for the downlink sum-rate maximization in multi-user (MU) MIMO-OFDM systems under maximum transmit power, clipping, and OOB emission mask constraints. The formulated maximization problem is nonconvex and difficult to solve. We propose a weighted minimum mean squared error (WMMSE) based block coordinate descent (BCD) method to iteratively optimize digital-RF precoders at the transmitter and digital-RF combiners at the users. Low-cost and scalable optimization approaches are proposed to efficiently solve the BCD subproblems. Extensive simulation results are conducted to demonstrate the efficiency of the proposed approaches and exhibit their superiority relative to well-known benchmarks.

eess.SP↗

arXiv2026

Symbol-Level Mask-Compliant Hybrid Precoding for Multi-User MIMO-OFDM Systems

Millimeter-wave (mmWave) technology is a crucial enabler for next-generation networks because it offers substantially greater available bandwidth. mmWave multiple-input multiple-output (MIMO) systems cannot rely solely on fully digital precoding due to hardware costs. As a result, hybrid precoding, which combines digital baseband processing with RF precoding, has emerged as a practical solution that balances performance and implementation complexity. As mmWave links typically operate over wideband, frequency-selective channels, orthogonal frequency-division multiplexing (OFDM) is commonly used to mitigate dispersive effects, yet OFDM introduces practical drawbacks, including out-of-band (OOB) emissions from abrupt spectral transitions among subcarriers and additional spectral leakage induced by windowing. Moreover, nonideal phase shifters (PS) in the RF transmit precoder and the user combiner impose inherent implementation limits that result in phase errors. We investigate robust joint digital--RF precoder design for minimizing the downlink sum mean-squared error (MSE) in hybrid multi-user (MU) MIMO--OFDM systems subject to maximum transmit-power, clipping, and OOB spectral-mask constraints. The resulting optimization is nonconvex and challenging to solve. To address this, we develop a minimum mean-squared error (MMSE) based block coordinate descent (BCD) algorithm that alternates between updating the transmitter-side digital--RF precoders and the user-side digital--RF combiners. For each BCD subproblem, we propose computationally efficient and scalable, closed-form solution strategies suitable for practical implementation. Extensive simulations validate the proposed methods and show clear performance improvements over established benchmark schemes.

eess.SP↗

arXiv2025

Precoding for Uplink RIS-Assisted Cell-Free MIMO-OFDM Systems with Hardware Impairments

This paper studies a reconfigurable intelligent surface (RIS)-assisted cell-free massive multiple-input multiple-output (CF-mMIMO) system with multiple RISs. Joint design of transmit precoding, RIS coefficients, and receive combining is investigated for uplink sum-rate maximization under in-phase and quadrature phase imbalance (IQI) at user equipments (UEs) and access points (APs). A weighted minimum mean squared error (WMMSE) based block coordinate descent (BCD) approach is proposed, where novel iterative methods are developed to efficiently solve the BCD subproblems. The efficiency of proposed approaches is demonstrated relative to heuristic methods via extensive simulations.

eess.SP↗

arXiv2021

Data-Driven Adaptive Network Slicing for Multi-Tenant Networks

Network slicing to support multi-tenancy plays a key role in improving the performance of 5G networks. In this paper, we propose a two time-scale framework for the reservation-based network slicing in the backhaul and Radio Access Network (RAN). In the proposed two time-scale scheme, a subset of network slices is activated via a novel sparse optimization framework in the long time-scale with the goal of maximizing the expected utilities of tenants while in the short time-scale the activated slices are reconfigured according to the time-varying user traffic and channel states. Specifically, using the statistics from users and channels and also considering the expected utility from serving users of a slice and the reconfiguration cost, we formulate a sparse optimization problem to update the configuration of a slice resources such that the maximum isolation of reserved resources is enforced. The formulated optimization problems for long and short time-scales are non-convex and difficult to solve. We use the $\ell_q$-norm, $0<q<1$, and group LASSO regularizations to iteratively find convex approximations of the optimization problems. We propose a Frank-Wolfe algorithm to iteratively solve approximated problems in long time-scales. To cope with the dynamical nature of traffic variations, we propose a fast, distributed algorithm to solve the approximated optimization problems in short time-scales. Simulation results demonstrate the performance of our approaches relative to optimal solutions and the existing state of the art method.

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arXiv2021

Online Stochastic Gradient Descent Learns Linear Dynamical Systems from A Single Trajectory

This work investigates the problem of estimating the weight matrices of a stable time-invariant linear dynamical system from a single sequence of noisy measurements. We show that if the unknown weight matrices describing the system are in Brunovsky canonical form, we can efficiently estimate the ground truth unknown matrices of the system from a linear system of equations formulated based on the transfer function of the system, using both online and offline stochastic gradient descent (SGD) methods. Specifically, by deriving concrete complexity bounds, we show that SGD converges linearly in expectation to any arbitrary small Frobenius norm distance from the ground truth weights. To the best of our knowledge, ours is the first work to establish linear convergence characteristics for online and offline gradient-based iterative methods for weight matrix estimation in linear dynamical systems from a single trajectory. Extensive numerical tests verify that the performance of the proposed methods is consistent with our theory, and show their superior performance relative to existing state of the art methods.

cs.LG↗

arXiv2021

Resource Reservation in Backhaul and Radio Access Network with Uncertain User Demands

Resource reservation is an essential step to enable wireless data networks to support a wide range of user demands. In this paper, we consider the problem of joint resource reservation in the backhaul and Radio Access Network (RAN) based on the statistics of user demands and channel states, and also network availability. The goal is to maximize the sum of expected traffic flow rates, subject to link and access point budget constraints, while minimizing the expected outage of downlinks. The formulated problem turns out to be non-convex and difficult to solve to global optimality. We propose an efficient Block Coordinate Descent (BCD) algorithm to approximately solve the problem. The proposed BCD algorithm optimizes the link capacity reservation in the backhaul using a novel multipath routing algorithm that decomposes the problem down to link-level and parallelizes the computation across backhaul links, while the reservation of transmission resources in RAN is carried out via a novel scalable and distributed algorithm based on Block Successive Upper-bound Minimization (BSUM). We prove that the proposed BCD algorithm converges to a Karush-Kuhn-Tucker solution. Simulation results verify the efficiency and the efficacy of our BCD approach against two heuristic algorithms.

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arXiv2019

A Linearly Convergent Doubly Stochastic Gauss-Seidel Algorithm for Solving Linear Equations and A Certain Class of Over-Parameterized Optimization Problems

Consider the classical problem of solving a general linear system of equations $Ax=b$. It is well known that the (successively over relaxed) Gauss-Seidel scheme and many of its variants may not converge when $A$ is neither diagonally dominant nor symmetric positive definite. Can we have a linearly convergent G-S type algorithm that works for {\it any} $A$? In this paper we answer this question affirmatively by proposing a doubly stochastic G-S algorithm that is provably linearly convergent (in the mean square error sense) for any feasible linear system of equations. The key in the algorithm design is to introduce a {\it nonuniform double stochastic} scheme for picking the equation and the variable in each update step as well as a stepsize rule. These techniques also generalize to certain iterative alternating projection algorithms for solving the linear feasibility problem $A x\le b$ with an arbitrary $A$, as well as high-dimensional minimization problems for training over-parameterized models in machine learning. Our results demonstrate that a carefully designed randomization scheme can make an otherwise divergent G-S algorithm converge.

math.OC↗

arXiv2014

On Capacity Regions of Two-Receiver Broadcast Packet Erasure Channels with Feedback and Memory

The two-receiver broadcast packet erasure channel with feedback and memory is studied. Memory is modeled using a finite-state Markov chain representing a channel state. Outer and inner bounds on the capacity region are derived when the channel state is strictly causally known at the transmitter. The bounds are both formulated in terms of feasibility problems and they are matching in all but one of the constraints. The results are extended to feedback with larger delay. Numerical results show that the bounds are close in many examples and the gains offered through feedback can be quite large. The presented outer bound meets the inner bound recently derived in \cite{Kuo_Wang2014} and hence describes the capacity region.

cs.IT↗