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

Precoding Design for PAPR Reduction in Mixed-Numerology MU-MIMO OFDM Systems

Orthogonal frequency division multiplexing (OFDM) is widely adopted in modern wireless systems for high spectral efficiency, yet its high peak-to-average power ratio (PAPR) limits power amplifier efficiency. Mixed-numerology transmission, which enables flexible subcarrier spacing for diverse services, further aggravates the PAPR problem. Although PAPR-aware precoding has been investigated in single-numerology multi-user multiple-input multiple-output (MU-MIMO) OFDM systems, the PAPR suppression in mixed-numerology MU-MIMO OFDM systems has not been thoroughly addressed. In this letter, we propose a low-complexity precoding method to jointly suppress per-antenna PAPR, inter-numerology interference (INI), and multi-user interference (MUI) in mixed-numerology MU-MIMO OFDM systems. By decomposing the complex precoding design problem into low-dimensional subproblems, we develop an efficient algorithm with closed-form or semi-closed-form updates. Simulation results demonstrate that the proposed algorithm effectively reduces transmit power while satisfying per-antenna PAPR, INI, and MUI constraints, highlighting its application potential for MU-MIMO data-channel transmission with available channel state information (CSI).

Compressed-Sensing-Based Sparse Channel Estimation for Frequency-Selective MIMO-OFDM Systems Under Reduced Pilot Observations

Accurate channel estimation in frequency-selective multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems requires balancing pilot overhead, reconstruction accuracy, and computational cost. This paper presents a reproducible compressed sensing benchmark for sparse delay-domain channel estimation with reduced pilot observations. Its novelty is not the invention of Orthogonal Matching Pursuit (OMP), Compressive Sampling Matching Pursuit (CoSaMP), or Subspace Pursuit (SP), but the construction of a transparent and auditable evaluation protocol in which all estimators operate on the same channel realizations, sensing matrices, pilot budgets, signal-to-noise ratios (SNRs), stopping rules, and Monte Carlo trials. The framework explicitly defines the underdetermined observation model, the per-link 4 × 4 MIMO interpretation, the minimum-norm least-squares (LS) baseline, the identity-prior linear minimum mean-square error (LMMSE) baseline, the oracle-known sparsity assumption, uncertainty reporting, runtime protocol, and the mapping from delay-domain estimates to link- and subcarrier-domain quantities. OMP, CoSaMP, SP, LS, and LMMSE are evaluated for a 128-element delay dictionary, five active taps, sampling ratios from 0.10 to 0.70, SNRs from 0 to 30 dB, and 80 independent trials per operating point. The results show that sparse recovery exploits the assumed delay-domain sparsity more effectively than non-sparse baselines in the underdetermined regime, while pilot density remains a dominant factor in support identification and reconstruction error. The accompanying Python human–machine interface (HMI) produces confidence-aware metrics and publication-ready figures, enabling exact repetition of the benchmark and controlled extension to more realistic channel models. The conclusions are limited to simulation-based algorithmic evidence and define a direct pathway toward standardized-channel, software-defined radio (SDR), and measured radio-frequency (RF) validation.

Constellation Selection and Power Allocation for Multi-Cell OFDM-ISAC: Managing Inter-Cell Interference and Sensing Sidelobes

A modulation- and receive-filter-aware framework for the sensing-interference management in multi-cell OFDM-ISAC systems is developed and closed-form signal-to-interference-plus-noise ratio (SINR) expressions for each range--Doppler bin under matched filtering (MF) and reciprocal filtering (RF).

Beam Selection for Delay-Doppler Visibility in Multi-Target MIMO-OFDM Sensing

This paper studies leakage-aware beam selection for multi-target multiple-input multiple-output (MIMO)-orthogonal frequency-division multiplexing (OFDM) sensing. We focus on ensuring that each hypothesized target remains detectable at its own delay-Doppler (DD) bin despite leakage from other targets. For this, we derive a visibility metric that separates the desired focused power from pairwise leakage caused by the finite-grid OFDM ambiguity kernel, receive angular coupling, and transmit codebook gains. Then, we formulate a max-min beam-selection and power-allocation problem in which the sensing signal over the fixed OFDM time-frequency grid is transmitted by a limited number of selected beams. The resulting problem is solved via a bisection-based mixed-integer successive convex approximation framework. A time-sharing formulation is also presented as a globally solvable benchmark. Numerical results show that the proposed pairwise leakage-aware design achieves higher worst-target visibility than the time-sharing, global leakage-suppression, illumination-based, and random beam-selection baselines, especially when targets are close in angle or DD.

Precoding for full-duplex MU-MIMO systems with Lagrange dual ascent and convex approximation

QoS-Aware Joint Subcarrier and Power Allocation for OFDM-ISAC V2X Systems

This paper investigates joint subcarrier and power allocation for a multi-user Orthogonal Frequency Division Multiplexing (OFDM)-based Integrated Sensing and Communication (ISAC) system in Vehicle-to-Everything (V2X) environments. The goal is to maximize a weighted sum of the capped effective radar Signal-to-Noise Ratio (SNR) and aggregate communication rate, under per-user constraints on minimum effective radar SNR, communication rate, and range resolution. The problem is formulated as a Mixed-Integer Nonlinear Programming (MINLP) model. To address its non-convexity, we develop a Block Coordinate Descent–based Joint Resource Allocation (BCD-JRA) algorithm that alternates between nonlinear power allocation and mixed-integer subcarrier assignment and is used as a benchmark in our study. To support real-time V2X operation, we further propose a low-complexity two-stage heuristic, termed Phased Constraint Satisfaction and Greedy Allocation (PSGA). PSGA first allocates the minimum resources needed to satisfy the Quality of Service (QoS) constraints, and then greedily assigns remaining resources based on marginal utility gains while accounting for effective radar SNR capping. The simulation results show that PSGA attains utility close to the BCD-JRA benchmark with millisecond-level latency and satisfies all QoS constraints in the reported experiments.