arXiv subjects

Cicek Cavdar

Publications and source records attributed to Cicek Cavdar.

At least 19 recordsLinked to original sources

arXiv2026

Fundamentals of Energy-Efficient Hardware Configurations for Wireless Links with Sleep Modes

In this paper, we examine the energy efficiency (EE) of a base station (BS) with multiple antennas. We use a state-of-the-art power consumption (PC) model that captures the passive and active parts of the transceiver circuitry, including the effects of radiated power, signal processing, and passive consumption. The paper treats the transmit power, bandwidth, and number of antennas as the optimization variables. We provide novel closed-form solutions for the optimal ratios of power per unit bandwidth and power per transmit antenna, and discover a new relationship in which the radiated power equals the total transceiver power at the EE-optimal operating point. A central finding is that the EE-optimal signal-to-noise ratio (SNR) collapses to a universal numerical constant of approximately 5.93 dB, independent of channel and hardware parameters. We present an algorithm that jointly optimizes the three design variables to achieve maximum EE under practical constraints, and provide analytical insight into whether maximum power or maximum bandwidth is optimal and how many antennas a BS should utilize. We further extend the optimization framework to incorporate quality-of-service (QoS) requirements and three advanced sleep modes of varying depth: absolute sleep, deep sleep, and idle mode. We characterize the optimal hardware configuration for each mode and determine when the rush-to-sleep strategy, which transmits briefly at the EE-optimal active configuration and sleeps the rest of the time, is optimal. Incorporating wake-up transition delays, we reveal how latency constraints and sleep-mode-specific transition times jointly dictate the optimal sleep mode for data packets with absolute deadlines. Together, these results indicate that energy-efficient operation requires treating transmission and sleep as a single coupled optimization.

cs.IT↗

arXiv2026

Green Cell-Free Massive MIMO for ISAC: Joint Cloud, Fronthaul and Radio Resource Allocation

In this paper, we develop a cross-layer end-to-end (E2E) resource orchestration framework for green CF-mMIMO ISAC systems with distributed multi-target detection. We propose a distributed sensing approach in which receive access points (RX-APs) compute local test statistics, which are aggregated at the cloud using weights based on sensing interference and channel quality. We derive the local maximum a posteriori ratio test (MAPRT) detectors under fully informed (FIS) and partially informed (PIS) operation, representing different levels of transmit-signal information at the RX-APs. We further characterize their processing and fronthaul requirements and derive a network power model incorporating radio transmission, AP and cloud processing, and fronthaul infrastructure. We formulate a mixed-integer non-convex problem that jointly optimizes transmit powers, AP modes, UE and sensing-area associations, RX-AP assignments, and active fronthaul and cloud resources subject to communication, sensing, power, processing-capacity, and fronthaul constraints. A two-stage iterative algorithm based on Big-M reformulation, convex--concave programming, penalty-based relaxation, and structured discrete recovery is developed. Numerical results show that the proposed E2E framework reduces total power by up to 50% compared with transmit-power-only optimization and by approximately 11-17% compared with joint radio optimization under full coordination, while maintaining detection probabilities above 0.95. The results also reveal a fundamental implementation trade-off: FIS provides lower detector-processing complexity and higher detection performance, whereas PIS substantially reduces fronthaul requirements.

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arXiv2026

Joint Access and Fronthaul Resource Allocation for Cell-Free Massive MIMO with Wireless Fronthaul

Wireless fronthaul is a key enabler of flexible and scalable cell-free massive MIMO systems, but its limited capacity poses significant challenges for maintaining high and uniform user performance. In this work, we analyze the performance of a cell-free massive MIMO network with wireless fronthaul under realistic low physical layer functional splits. We propose a joint access and fronthaul resource allocation algorithm that maximizes the minimum user equipment (UE) spectral efficiency while satisfying fronthaul load constraints. Our analysis reveals that power allocation over the wireless fronthaul follows a modified water-filling structure, where the water level is jointly determined by the access and fronthaul channel gains. Furthermore, we show that severe fronthaul limitations not only reduce UE rates but also introduce spatial performance disparities depending on the cloud location. Finally, we demonstrate that split option 8 is impractical under wireless fronthaul constraints, underscoring the importance of dynamic fronthaul bit allocation to reduce fronthaul load and enable efficient system operation.

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arXiv2026

Energy-Efficient Dual-Band Communication: How to Allocate Traffic to Sub-THz Carriers?

As 6G wireless networks transition toward sub-Terahertz (sub-THz) frequencies to satisfy extreme capacity demands, managing the trade-off between massive bandwidth and power consumption becomes a critical design challenge. In this paper, we investigate the fundamental energy efficiency (EE) limits of a dual-band base station site combining a coverage-oriented sub-6 GHz carrier with a capacity-oriented sub-THz carrier. By jointly optimizing hardware parameters and advanced sleep modes via activity factors, we identify four distinct operational regions that govern the EE-optimal behavior across the complete range of data rates. We derive closed-form analytical thresholds that dictate precisely when the sub-THz band should awaken from sleep and how to allocate traffic between the bands in that case. Our results demonstrate that utilizing the sub-THz band is EE-optimal when the power cost of the bandwidth-limited sub-6 GHz band surpasses the static power penalty of activating the sub-THz circuitry. Ultimately, this framework provides mathematically rigorous guidelines for power consumption minimization and sleep-mode management in future green networks.

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arXiv2026

Rethinking Energy Efficiency in Cell-Free Massive MIMO: The Role of Processing and Optical Fronthaul

Cell-free massive MIMO promises uniformly high performance by combining densely distributed radio units, coherent transmission, and centralized processing. Unlike earlier radio generations, it depends on dense fronthaul connectivity and a virtualized cloud-RAN architecture. In this setting, energy use is no longer driven primarily by active radio components; instead, fronthaul and processing play a dominant role, calling for a fresh perspective on what defines energy efficiency. This work introduces a modular power model that captures the interplay between radios, fronthaul, and cloud processing. The analysis highlights how design choices, such as functional splits and precoding strategies, shape both fronthaul data load and total power consumption. Centralized precoding provides stronger performance with less resource utilization, while flexible activation of radios and processing elements avoids unnecessary overhead. Overall, the energy efficiency of cell-free massive MIMO grows as antennas are more densely distributed across the coverage area, particularly when combined with end-to-end resource allocation.

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arXiv2026

Interplay between VAoI, Packet Error Rate, and Delay for Energy-Efficient Remote Monitoring

This letter studies energy optimization of short-packet transmission for event-triggered remote monitoring over finite-blocklength wireless links. A wireless sensor node generates updates only when the source state changes, and freshness is measured by the Version Age of Information (VAoI). We model the VAoI evolution as a Markov chain and show its coupling with the packet error rate, characterized by decoding error probability, and average delay. Then, we formulate a transmit-power allocation problem that minimizes the long-term average energy consumption under a VAoI constraint and solve it using a low-complexity search method. Numerical results show that the update arrival probability and blocklength strongly affect the energy--VAoI tradeoff, and that optimizing long-term energy consumption can substantially reduce energy compared with minimizing the energy per transmission.

cs.IT↗

arXiv2026

RIS-Assisted Survivable Backhaul Recovery in Small-Cell Systems

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.

cs.IT↗

arXiv2026

Multi-Connectivity for UAVs: A Measurement Study of Integrating Cellular, Aerial Mesh, and LEO Satellite Links

Future uncrewed aerial vehicle (UAV) systems increasingly combine heterogeneous communication technologies, such as low-latency aerial mesh, terrestrial cellular, and satellite links, to improve robustness and coverage. Multipath transport is a natural mechanism for aggregating these links, yet its ability to support real-time UAV services in highly heterogeneous environments remains insufficiently characterized. We present a measurement-driven study based on UAV flight experiments in an integrated network comprising UAV-to-UAV aerial mesh, private cellular, and low Earth orbit (LEO) satellite connectivity. Using Multipath TCP (MPTCP) as a representative lossless, in-order multipath transport framework, we find that aggregation can preserve end-to-end connectivity under severe link outages. However, large round-trip time (RTT) heterogeneity amplifies packet reordering, leading to substantial receiver-side buffering and bursty delivery. In addition, when the available links do not provide sufficient capacity for the offered load, pronounced sender-side buffering emerges. These effects cause real-time streaming to violate delay constraints, including cases where aggregate capacity is sufficient. To interpret these results, we formalize the distinction between connectivity continuity and service continuity and show empirically that maintaining connectivity is necessary but not sufficient for timely real-time delivery in multi-technology UAV networks. The findings motivate multipath designs that explicitly account for delay constraints, rather than optimizing for connectivity alone.

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arXiv2026

Energy Saving for Cell-Free Massive MIMO Networks: A Multi-Agent Deep Reinforcement Learning Approach

This paper focuses on energy savings in downlink operation of cell-free massive MIMO (CF mMIMO) networks under dynamic traffic conditions. We propose a multi-agent deep reinforcement learning (MADRL) algorithm that enables each access point (AP) to autonomously control antenna re-configuration and advanced sleep mode (ASM) selection. After the training process, the proposed framework operates in a fully distributed manner, eliminating the need for centralized control and allowing each AP to dynamically adjust to real-time traffic fluctuations. Simulation results show that the proposed algorithm reduces power consumption (PC) by 56.23% compared to systems without any energy-saving scheme and by 30.12% relative to a non-learning mechanism that only utilizes the lightest sleep mode, with only a slight increase in drop ratio. Moreover, compared to the widely used deep Q-network (DQN) algorithm, it achieves a similar PC level but with a significantly lower drop ratio.

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arXiv2026

Unlocking the Energy-Saving Potential in O-RAN Cell-Free Massive MIMO by Joint Orchestration of Radio, Wireless Fronthaul, and Cloud Resources

Network virtualization and cloudification in Open Radio Access Networks (O-RAN) enable joint orchestration of the processing and fronthaul resources, which are essential for realizing the energy-saving potential of cell-free massive MIMO networks. To harness this potential, we investigate cell-free massive MIMO deployed over an O-RAN architecture with a wireless fronthaul that removes the need for fiber deployment. We first model the end-to-end power consumption under wireless fronthaul. Then, we propose a joint orchestration framework for radio, fronthaul, and processing resources that minimizes end-to-end power consumption while satisfying user-equipment (UE) rate requirements and wireless-fronthaul constraints. Two algorithms are developed: a scenario-sampling/group-Lasso method for centralized precoding and a block-coordinate descent method for distributed precoding. Numerical results show that centralized precoding significantly outperforms distributed precoding. End-to-end resource orchestration provides up to 70% energy-savings compared to cloud-only orchestration and up to 15% compared to radio-only orchestration. Moreover, distributing the same total number of antennas across the coverage area, rather than concentrating them at a few radio units (RUs), substantially reduces network power consumption, demonstrating that cell-free massive MIMO can deliver both high performance and high energy efficiency in future mobile networks.

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arXiv2026

Scalable machine learning-based approaches for energy saving in densely deployed Open RAN

Densely deployed base stations are responsible for the majority of the energy consumed in Radio access network (RAN). While these deployments are crucial to deliver the required data rate in busy hours of the day, the network can save energy by switching some of them to sleep mode and maintain the coverage and quality of service with the other ones. Benefiting from the flexibility provided by the Open RAN in embedding machine learning (ML) in network operations, in this work we propose Deep Reinforcement Learning (DRL)-based energy saving solutions. Firstly we propose 3 different DRL-based methods in the form of xApps which control the Active/Sleep mode of up to 6 radio units (RUs) from Near Real time RAN Intelligent Controller (RIC). We also propose a further scalable federated DRL-based solution with an aggregator as an rApp in None Real time RIC and local agents as xApps. Our simulation results present the convergence of the proposed methods. We also compare the performance of our federated DRL across three layouts spanning 6--24 RUs and 500--1000\,m regions, including a composite multi-region scenario. The results show that our proposed federated TD3 algorithm achieves up to 43.75\% faster convergence, more than 50\% network energy saving and 37. 4\% lower training energy versus centralized baselines, while maintaining the quality of service and improving the robustness of the policy.

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arXiv2026

Incremental Learning-Based Open-Set Classification of Unknown UAVs via RF Signal Semantics

The proliferation of civilian and commercial unmanned aerial vehicles (UAVs) has heightened the demand for reliable radio frequency (RF)-based drone identification systems that can operate under dynamic and uncertain airspace conditions. Most existing RF-based recognition methods adopt a closed-set assumption, where all UAV types are known during training. Such an assumption becomes unrealistic in practical deployments, as new or unknown UAVs frequently emerge, leading to overconfident misclassifications and inefficient retraining cycles. To address these challenges, this paper proposes a unified incremental open-set learning framework for RF-based UAV recognition that enables both novel class discovery and incremental adaptation. The framework first performs open-set recognition to separate unknown signals from known classes in the semantic feature space, followed by an unsupervised clustering module that discovers new UAV categories by selecting between K-Means and Gaussian Mixture Models (GMM) based on composite validity scores. Subsequently, a lightweight incremental learning module integrates the newly discovered classes through a memory-bounded replay mechanism that mitigates catastrophic forgetting. Experiments on a real-world UAV RF dataset comprising 24 classes (18 known and 6 unknown) show effective open-set detection, promising clustering performance under the evaluated noise settings, and stable incremental adaptation with minimal storage cost, supporting the potential of the proposed framework for open-world UAV recognition.

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arXiv2026

NCR vs. Passive/Active RIS: How Much NCR Amplification is Required to Beat RIS?

This paper investigates the fundamental tradeoff between reconfigurable intelligent surfaces (RISs) and network-controlled repeaters (NCRs) in terms of achievable signal-to-noise ratio (SNR). Considering an uplink system with a multi-antenna base station (BS) and a single-antenna user equipment (UE), we derive closed-form SNR expressions for passive RIS-, active RIS-, and NCR-assisted communication under line-of-sight propagation between the BS-RIS/NCR and RIS/NCR-UE. Both narrowband and wideband transmissions are analyzed, with and without the presence of a direct BS--UE link. Our analysis reveals a key structural difference: while the SNR achieved with RISs grows unboundedly with the number of RIS elements, the SNR provided by an NCR is fundamentally limited by the UE--repeater channel due to noise amplification. Nevertheless, we show that NCRs can outperform both passive and active RISs when deployed close to the UE, provided that sufficient amplification is available. Numerical results based on realistic path loss models quantify the amplification levels required for NCRs to outperform RISs across different deployment geometries and system dimensions. These findings provide clear design guidelines for the practical integration of RISs and NCRs in future wireless networks.

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arXiv2026

Collective Grid: Privacy-Preserved Multi-Operator Energy Sharing Optimization via Federated Energy Prediction

Electricity consumption in mobile networks is increasing with the continued 5G expansion, rising data traffic, and more complex infrastructures. However, energy management is often handled independently by each mobile network operator (MNO), leading to limited coordination and missed opportunities for collective efficiency gains. To address this gap, we propose a privacy-preserving framework for automated energy infrastructure sharing among co-located MNOs. Our framework consists of three modules: (i) a federated learning-based privacy-preserving site energy consumption forecasting module, (ii) an orchestration module in which a mixed-integer linear program is solved to schedule energy purchases from the grid, utilization of renewable sources, and shared battery charging or discharging, based on real-time prices, forecasts, and battery state, and (iii) an energy source selection module which handles the selection of cost-effective power sources and storage actions based on predicted demand across MNOs for the next control window. Using data from operational networks, our experiments confirm that the proposed solution substantially reduces operational costs and outperforms non-sharing baselines, with gains that increase as network density rises in 5G-and-beyond deployments.

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arXiv2026

Chirp-Based Multi-Device Ambient Backscatter Communication and Sensing Enabled by OFDM-AFDM Symbiotic Radio

This paper presents a novel symbiotic radio system for integrated sensing and backscatter communication (ISABC) technique that enables signal-domain interference-free coexistence of the primary communication signal and the backscatter communication (BC) signal within the same spectrum. The proposed system design allows simultaneous backscatter devices (BDs) sensing and data transmission without mutual interference by exploiting waveform-domain orthogonality between orthogonal frequency division multiplexing (OFDM) and affine frequency domain multiplexing (AFDM) signals. Specifically, a chirp-based AFDM waveform is adopted due to its inherent processing gain, which enhances the detectability and reliability of the weak backscatter signal while simultaneously supporting high-resolution sensing. Unlike conventional methods that attempt to suppress direct-link interference (DLI), this approach embeds the backscatter transmission within the affine domain while maintaining reliable OFDM-based primary communication. Furthermore, by assigning distinct affine-domain shifts to each backscatter device, the proposed framework inherently suppresses inter-backscatter device interference (IBDI). Comprehensive simulation results demonstrate that the proposed coexistence scheme effectively mitigates interference without affecting the error rate of the primary link and improves the miss-detection probability performance of the BC, making it a promising candidate for future low-power and interferenceresilient systems.

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arXiv2026

EARL: Energy-Aware Adaptive Antenna Control with Reinforcement Learning in O-RAN Cell-Free Massive MIMO Networks

Cell-free massive multi-input multi-output (MIMO) promises uniform high performance across the network, but also brings a high energy cost due to joint transmission from distributed radio units (RUs) and centralized processing in the cloud. Leveraging the resource-sharing capabilities of Open Radio Access Network (O-RAN), we propose EARL, an energy-aware adaptive antenna control framework based on reinforcement learning. EARL dynamically configures antenna elements in RUs to minimize radio, optical fronthaul, and cloud processing power consumption while meeting user spectral efficiency demands. Numerical results show power savings of up to 81% and 50% over full-on and heuristic baselines, respectively. The RL-based approach operates within 220 ms, satisfying O-RAN's near-real-time limit, and a greedy refinement further halves power consumption at a 2 s runtime.

cs.IT↗

arXiv2026

Feasibility Study Regarding Self-sustainable Reconfigurable Intelligent Surfaces

Without requiring operational costs such as cabling and powering while maintaining reconfigurable phase-shift capability, self-sustainable reconfigurable intelligent surfaces (ssRISs) can be deployed in locations inaccessible to conventional relays or base stations, offering a novel approach to enhance wireless coverage. This study assesses the feasibility of ssRIS deployment by analyzing two harvest-and-reflect (HaR) schemes: element-splitting (ES) and time-splitting (TS). We examine how element requirements scale with key system parameters, transmit power, data rate demands, and outage constraints under both line-of-sight (LOS) and non-line-of-sight (NLOS) ssRIS-to-user equipment (UE) channels. Analytical and numerical results reveal distinct feasibility characteristics. The TS scheme demonstrates better channel hardening gain, maintaining stable element requirements across varying outage margins, making it advantageous for indoor deployments with favorable harvesting conditions and moderate data rates. However, TS exhibits an element requirement that exponentially scales to harvesting difficulty and data rate. Conversely, the ES scheme shows only linear growth with harvesting difficulty, providing better feasibility under challenging outdoor scenarios. These findings establish that TS excels in benign environments, prioritizing reliability, while ES is preferable for demanding conditions requiring operational robustness.

cs.IT↗

arXiv2025

Cell-free ISAC for Drone Detection Considering Coverage and Age of Sensing

The growing presence of unauthorized drones poses significant threats to public safety, underscoring the need for aerial surveillance solutions. This work proposes a cell-free integrated sensing and communication (ISAC) framework enabling drone detection within the existing communication network infrastructure, while maintaining communication services. The system exploits the spatial diversity and coordination of distributed access points (APs) in a cell-free massive MIMO architecture to detect aerial passive targets. To evaluate sensing performance, we introduce two key metrics: age of sensing (AoS), capturing the freshness of sensing information, and sensing coverage. The proposed AoS metric includes not only the transmission delays as in the existing models, but also the processing for sensing and networking delay, which are critical in dynamic environments like drone detection. We introduce an ambiguity parameter quantifying the similarity between the target-to-receiver channels for two hotspots and develop a novel network configuration strategy, including hotspot grouping, AP clustering, and sensing pilot assignment, leveraging simultaneous multi-point sensing to minimize AoS. Our results show that the best trade-off between AoS and sensing coverage is achieved when the number of hotspots sharing the same time/frequency resource matches the number of sensing pilots, indicating ambiguity as the primary factor limiting the sensing performance.

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