Özlem Tuğfe Demir

Zhenyu Li, Özlem Tuğfe Demir, Emil Björnson et al.

The increasing densification of small-cell networks substantially expands cable-based backhaul infrastructure, creating heightened vulnerability to cable link failures. This paper proposes a reconfigurable intelligent surface (RIS)-assisted backup framework that exploits a key insight: during backhaul cable failures, base station (BS) radio components remain functional, enabling wireless backhaul traffic redistribution. Our framework maintains network connectivity by redistributing disconnected BS backhaul traffic to neighboring BSs through RIS-assisted wireless links. To maximize survivability across varying traffic conditions, we formulate a joint optimization problem that maximizes total resolvable backhaul traffic by jointly deciding BS selection, RIS phase shifts, and precoding vectors. The inherent non-convexity arising from coupling and quadratic fractional term is addressed through an alternating optimization algorithm that iteratively solves tractable convex subproblems via quadratic transformation. Comprehensive numerical evaluations demonstrate that the proposed RIS-enhanced framework significantly improves survivability from 58% to 72% under challenging high-intensity hotspot traffic conditions. Moreover, RIS provides the greatest gains for antenna-constrained systems by extending coverage to access more spare capacity of the distant BSs as well as enhancing the signal strength. Consequently, high survivability is achieved even with only two antennas per BS under moderate traffic intensity.

Özlem Tuğfe Demir, Salih Gümüşbuğa

Cell-free massive MIMO has matured into a key candidate technology for 6G and beyond, owing to its ability to provide nearly uniform service quality to many user equipments (UEs) over the same time-frequency resources. Unlike conventional cellular massive MIMO, the core idea is to distribute a large number of low-cost access points (APs) across the network and enable joint coherent transmission and reception. While early works largely assumed ideal hardware, hardware impairments become inevitable when APs are implemented with low-cost components. In this context, this paper investigates the adverse impact of low-resolution digital-to-analog converters (DACs) on the downlink performance of cell-free massive MIMO-OFDM systems. In contrast to prior studies that mainly quantify spectral-efficiency degradation under low-resolution DACs, we consider the design of quantized constant-envelope (CE) precoding, which additionally enables the use of highly power-efficient amplifiers. To the best of our knowledge, this is the first work on quantized CE precoding for cell-free massive MIMO-OFDM. Beyond adapting the classical maximum-antenna-power method, we propose a novel power-control strategy across APs that mitigates the detrimental effects of severely quantized transmitters by reducing the contribution of harmful APs. Simulation results demonstrate that the proposed power-control mechanism significantly improves the uncoded bit error rate performance.

Özlem Tuğfe Demir

Modular extremely large-scale MIMO (XL-MIMO) architectures combined with wireless fronthaul provide a scalable alternative to monolithic arrays, but their performance is sensitive to hardware impairments and resource allocation strategies. In this paper, we consider a distributed two-phase processing framework for modular XL-MIMO systems employing amplify-and-forward wireless fronthaul under practical hardware constraints. We jointly model access-side and fronthaul-side distortions and formulate a weighted minimum mean-square error (WMMSE)-based optimization problem that maximizes the uplink sum spectral efficiency (SE) by jointly adjusting UE transmit powers and fronthaul amplification levels. The resulting algorithm alternates between distortion-aware receiver design and convex power-control updates. Numerical results demonstrate that the proposed joint optimization significantly improves spectral efficiency compared to fixed transmission strategies, particularly when the CPU has a moderate number of antennas, while also quantifying the relative impact of access and fronthaul impairments.

Özlem Tuğfe Demir, Alva Kosasih

This paper investigates the joint optimization of power allocation and antenna activation in sparse extremely large aperture array systems operating under power amplifier non-linearities. We first derive an analytical expression for the achievable spectral efficiency (SE) of point-to-point MIMO channels affected by non-linear distortions using the Bussgang decomposition. To address the combinatorial and non-convex nature of the energy-efficiency (EE) maximization problem, we employ an unsupervised deep neural network (DNN) that learns the non-linear mapping between the channel state information and the optimal EE operating point. The DNN jointly predicts distortion-aware power allocation, total transmit power scaling, and modular sub-array activation based on singular-value and geometric channel features. Numerical results demonstrate that the proposed DNN-based arrays achieve significant EE gains over the conventional sparse arrays.

Doğa Evgür, Ozan Alp Topal, Özlem Tuğfe Demir

This paper investigates the uplink capacity of single-input single-output (SISO) systems assisted by a swarm of network-controlled repeaters (NCRs). We develop a rigorous wideband formulation based on OFDM signaling. Starting from the continuous-time passband model, we derive the capacity expression for the repeater-assisted OFDM channel, accounting for amplified noise contributions from multiple repeaters. Numerical results demonstrate that NCRs can substantially enhance system capacity even with simple activation strategies, and that activating only the closest repeater yields nearly the same performance as activating all repeaters, thereby offering significant energy-saving opportunities. These findings highlight the potential of NCR swarms as a cost-effective and scalable solution for coverage extension and capacity enhancement in wideband wireless networks.

Özlem Tuğfe Demir, Emil Björnson

Network-controlled repeaters (NCRs) are a low-cost means to extend coverage and strengthen macro diversity in wireless networks. They operate in real time by amplifying and re-transmitting the incoming signal with only hardware-level delays, without requiring any channel state information (CSI) at the repeater itself. However, their power amplifiers (PAs) generate non-linear distortion that is jointly forwarded with the desired signal and can undermine multiuser performance unless the distortion statistics are exploited. This paper develops a distortion-aware (DA) uplink framework for repeater-assisted massive MIMO (RA-MIMO) under PA non-linearities. We adopt a memoryless third-order polynomial model for the repeater PA and characterize the achievable spectral efficiency (SE) using the Bussgang decomposition. Closed-form expressions are derived for the Bussgang gain matrix and the distortion covariance. We also design a DA combining vector that maximizes the effective signal-to-interference-plus-distortion ratio.

Tianzhang Cai, Qichen Wang, Shuai Zhang et al.

We develop a multi-agent reinforcement learning (MARL) algorithm to minimize the total energy consumption of multiple massive MIMO (multiple-input multiple-output) base stations (BSs) in a multi-cell network while preserving the overall quality-of-service (QoS) by making decisions on the multi-level advanced sleep modes (ASMs) and antenna switching of these BSs. The problem is modeled as a decentralized partially observable Markov decision process (DEC-POMDP) to enable collaboration between individual BSs, which is necessary to tackle inter-cell interference. A multi-agent proximal policy optimization (MAPPO) algorithm is designed to learn a collaborative BS control policy. To enhance its scalability, a modified version called MAPPO-neighbor policy is further proposed. Simulation results demonstrate that the trained MAPPO agent achieves better performance compared to baseline policies. Specifically, compared to the auto sleep mode 1 (symbol-level sleeping) algorithm, the MAPPO-neighbor policy reduces power consumption by approximately 8.7% during low-traffic hours and improves energy efficiency by approximately 19% during high-traffic hours, respectively.