A time-evolving RCE model is developed that characterizes the joint effects of residual phase mismatches, residual carrier frequency offsets, and oscillator phase noise, and a Gauss--Legendre quadrature-based weighted minimum mean square error (WMMSE) optimization framework is developed.
Abstract
This paper investigates robust downlink transmission to tolerate calibration aging in time-division duplex cell-free massive multiple-input multiple-output (CF-mMIMO) systems with residual calibration errors (RCEs). Unlike existing studies that typically treat RCEs as static impairments, we develop a time-evolving RCE model that characterizes the joint effects of residual phase mismatches, residual carrier frequency offsets, and oscillator phase noise. Based on this model, two practical processing architectures are considered: instantaneous calibrated-channel-based robust beamforming (BF) and statistical beamformed-channel-based robust power allocation (PA). For both architectures, tractable achievable rate lower bounds are derived, which explicitly reveal the impact of calibration aging on coherent combining, BF-gain uncertainty, and inter-user interference. Using these lower bounds as design metrics, we formulate an effective weighted sum-rate (EWSR) maximization problem over the data transmission interval, so that the resulting BF and PA designs can account for the temporal evolution of RCEs rather than a single calibrated instant. To efficiently solve the resulting problems, a Gauss--Legendre quadrature-based weighted minimum mean square error (WMMSE) optimization framework is developed, where both robust BF and PA are updated in an access point (AP)-block manner with closed-form solutions under per-AP power constraints. Simulation results demonstrate that: i) the proposed algorithms exhibit stable convergence; ii) the proposed robust BF and PA schemes achieve higher EWSR by explicitly accounting for calibration aging than their non-robust counterparts; and iii) robust BF achieves higher spectral efficiency (SE), whereas robust PA provides a more favorable tradeoff between SE and implementation cost in terms of computational complexity and fronthaul overhead.
In this article, we consider received-power maximization in RIS-assisted wireless links using only limited received signal strength (RSS) feedback, avoiding explicit estimation of the high-dimensional cascaded channel. We propose stochastic and deterministic algorithms for passive and active beamforming. In the deterministic method, each RIS element requires only three fixed-phase RSS measurements to recover the phase, followed by tree-structured quantization. We prove a non-asymptotic received-power guarantee for the fixed-beamformer single-user RIS sweep under cascaded Rayleigh fading and extend the bound to finite-resolution RIS phase shifters. Simulations over Rayleigh and temporally correlated channels show that the proposed methods approach full-CSI alternating optimization. We extend the channel-estimation-free framework to multi-user RIS-assisted transmission by proposing a unified single-bit consensus method for both RIS phase adaptation and transmit beamforming. In this mechanism, all users vote on each shared perturbation using only local SINR comparisons, allowing the update to account for both desired-signal improvement and interference generated to other users. The weighted consensus allows for fairness among users based on their priority.
In integrated sensing and communication (ISAC) systems, stringent sensing performance constraints can severely limit the power available for communication. Hybrid reconfigurable intelligent surfaces (HRISs) with capabilities of both passive reflection and active signal amplification can significantly improve communication performance in the power-limited regime. This motivates us to analyze and optimize the performance of an HRIS-aided multiple-input-multiple-output (mMIMO) ISAC system. We first estimate the effective uplink/downlink channels using the minimum mean square error method. We then derive closed-form expressions for the communication sum-rate and sensing Cram\'er-Rao lower bound (CRLB). It is shown that under the equal power allocation strategy, the CRLB remains independent of the HRIS coefficients. Then, we formulate a joint optimization problem of power allocation and HRIS beamforming to maximize the communication sum-rate while ensuring specified sensing CRLB constraints. To solve the formulated non-convex problem, we propose an alternating optimization algorithm based on fractional programming and successive convex approximation. Extensive simulations validate our analysis and proposed algorithm, showing significant improvements in both communication and sensing performances enabled by the HRIS. For example, an HRIS with only $4$ active elements offers $97.30\%$ improvement in the communication sum-rate, while ensuring a sensing CRLB constraint of $-30$ dB.
Smriti Uniyal, Tian-Yu Fang, M. di Renzo et al.· 0 citations
Stable downlink precoding in time-division duplex (TDD) multi-user multi-input multi-output (MU-MIMO) systems critically depends on the reliability of channel state information (CSI) at the transmitter. In mobile environments, Doppler-induced channel variation, estimation noise, and pilot contamination distort the channel structure, often leading to ill-conditioned matrices and unstable linear precoding. This paper proposes a Doppler-aware CSI reliability framework in which the condition number of sounding reference signal (SRS)-based channel estimates is used as a per-snapshot indicator of spatial robustness. Instead of relying solely on instantaneous CSI, the proposed approach selects the better-conditioned CSI between current and previously estimated channel realizations prior to downlink transmission, thereby improving robustness under mobility. System-level simulations compliant with 5G New Radio specifications are conducted under fixed, increasing, and decreasing Doppler scenarios using clustered delay line (CDL) channel models. Results show consistent gains in throughput and block error rate (BLER), approaching the performance of the perfect channel state information at transmitter (CSIT). Among the considered schemes, regularized zero forcing (RZF) precoding exhibits higher robustness to CSI degradation, while block diagonalization (BD) is more sensitive to Doppler variations. Overall, the proposed method provides a lightweight and standard-compatible solution for improving MU-MIMO precoding reliability in time-varying channels.
L. Bharti, Adarsh Ravi, Hamza Bouchebbah et al.· International Conference on...· 0 citations
The proposed framework does not optimize only computational speed, but also clarifies the trade-off among execution time, SINR, spectral efficiency, and fairness under dynamic uplink CF-mMIMO conditions, indicating that this architecture serves as an adaptable platform to evaluate dynamic uplink power distribution across CF-mMIMO networks.
Hussein A. Jasim, M. F. A. Rasid, F. Hashim et al.· Engineer· 0 citations
In time-division duplex (TDD) massive multiple-input multiple-output (MIMO) systems, users send pilot sequences for channel state estimation in a fixed time interval, which leads to pilot redundancy if users undergo longer coherence time than the prescribed channel estimation interval, and consequently reduces the net sum spectral efficiency of the systems. In this paper, we propose an adaptive channel estimation scheme for TDD massive MIMO systems, where base stations can reuse the aged channel state information (CSI) by exploiting the temporal correlation inherent in channel aging. We introduce a Fisher transformation method for temporal correlation estimation to determine the CSI estimation interval. We also derive the closed-form expressions of the spectral efficiency with channel aging effect and design a threshold to control the channel aging error introduced by CSI reuse. Numerical results demonstrate that our proposed adaptive scheme yields significant performance gains in spectral efficiency across various communication scenarios.
Zhouyi Qian, Shaowei Wang· IEEE Transactions on Wireles...· 0 citations
Integrated sensing and communication (ISAC) under a cell-free (CF) architecture enables seamless connectivity and sensing coverage by allowing multiple distributed access points (APs) to jointly serve users and detect targets, thereby mitigating cell-edge effects and enhancing spatial diversity. However, wideband CF-ISAC also suffers from frequency-selective fading and strong inter-AP interference. To address these challenges, we investigate a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted ISAC framework, which extends full-space coverage and mitigates multiplicative fading and blockage effects. A joint optimization strategy is developed to maximize the weighted ISAC joint rate by jointly optimizing bandwidth and power allocation, receive beamforming, and active STAR-RIS beamforming. To tackle the non-convexity caused by variable coupling and intricate constraints, an efficient alternating optimization algorithm is developed. The original problem is decomposed into several subproblems: first, a closed-form solution for receive beamforming is derived; next, the resource allocation semi-analytical solutions are obtained via Karush-Kuhn-Tucker (KKT) conditions. Subsequently, the active STAR-RIS coefficients are optimized by capitalizing on fractional programming and majorization-minimization (MM) techniques. Finally, simulation results reveal that the proposed scheme achieves a 20.34% weighted ISAC joint-rate gain over the passive scheme, validating its effectiveness in wideband CF-ISAC systems.
Xintong Zhou, Feng Ke, Xiu-Yin Zhang et al.· IEEE Transactions on Communi...· 0 citations
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