arXiv subjects

Tolga Girici

Publications and source records attributed to Tolga Girici.

11 recordsLinked to original sources

arXiv2026

Realization of a Fully Connected Neural Layer Over-the-Air through Multi-hop Amplify-and-Forward Relays

We study the problem of implementing a fully-connected layer of a neural network using wireless over-the-air computing. We assume a multi hop system with a multi-antenna transmitter and receiver, along with a number of multi-hop amplify-and-forward relay devices in between. We formulate an optimization problem that optimizes the transmitter precoder, receiver combiner and amplify-and-forward gains, subject to relay device power constraint and transmitter power constraint. We propose an alternating optimization framework that optimizes the imitation accuracy. Simulation study results reveal that multi-hop relaying achieves an almost perfect classification accuracy when used in a neural network.

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arXiv2026

Pilot Allocation for Multi-Hop Over-the-Air Neural Inference under Imperfect CSI

A multi-hop amplify-and-forward (AF) relay network can emulate a fully connected (FC) neural network layer via over-the-air (OTA) computation. However, achieving high emulation accuracy requires accurate channel state information (CSI) across all links in the multi-hop network. In this work, we investigate the impact of CSI errors on classification performance. We propose five heuristic schemes for allocating the total channel training time (pilots) across hops and compare their effectiveness. Numerical results reveal a clear trade-off between channel training overhead and classification accuracy. In particular, with sufficient pilot power and balanced allocation of channel training resources, the system can achieve classification accuracy close to that of the digital baseline.

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arXiv2026

Wireless Physical Neural Networks (WPNNs): Opportunities and Challenges

Wireless communication systems exhibit structural and functional similarities to neural networks: signals propagate through cascaded elements, interact with the environment, and undergo transformations. Building upon this perspective, we introduce a unified paradigm, termed \textit{wireless physical neural networks (WPNNs)}, in which components of a wireless network, such as transceivers, relays, backscatter, and intelligent surfaces, are interpreted as computational layers within a learning architecture. By treating the wireless propagation environment and network elements as differentiable operators, new opportunities arise for joint communication-computation designs, where system optimization can be achieved through learning-based methods applied directly to the physical network. This approach may operate independently of, or in conjunction with, conventional digital neural layers, enabling hybrid communication learning pipelines. In the article, we outline representative architectures that embody this viewpoint and discuss the algorithmic and training considerations required to leverage the wireless medium as a computational resource. Through numerical examples, we highlight the potential performance gains in processing, adaptability, efficiency, and end-to-end optimization, demonstrating the promise of reconfiguring wireless systems as learning networks in next-generation communication frameworks.

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arXiv2025

Predictive Beamforming with Distributed MIMO

In vehicle-to-everything (V2X) applications, roadside units (RSUs) can be tasked with both sensing and communication functions to enable sensing-assisted communications. Recent studies have demonstrated that distance, angle, and velocity information obtained through sensing can be leveraged to reduce the overhead associated with communication beam tracking. In this work, we extend this concept to scenarios involving multiple distributed RSUs and distributed MIMO (multiple-input multiple-output) systems. We derive the state evolution model, formulate the extended Kalman-filter equations, and implement predictive beamforming for distributed MIMO. Simulation results indicate that, when compared with a co-located massive MIMO antenna array, distributed antennas lead to more uniform and robust sensing performance, coverage, and data rates, while the vehicular user is in motion.

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arXiv2022

Inter-Numerology Interference Pre-Equalization for 5G Mixed-Numerology Communications

This article proposes a pre-equalization method to remove inter-numerology interference (INI) that occurs in multi-numerology OFDM frame structures of fifth-generation New Radio (5G-NR) and beyond on the transmitter side. In the literature, guard bands, filters and interference cancellation methods are used to reduce the INI. In this work, we mathematically model how the INI is generated and how it can be removed completely for multi-numerology systems on the transmitter side.

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arXiv2018

Kalman Prediction Based Proportional Fair Resource Allocation for a Solar Powered Wireless Downlink

Optimization of a Wireless Sensor Network (WSN) downlink with an energy harvesting transmitter (base station) is considered. The base station (BS), which is attached to the central controller of the network, sends control information to the gateways of individual WSNs in the downlink. This paper specifically addresses the case where the BS is supplied with solar energy. Leveraging the daily periodicity inherent in solar energy harvesting, the schedule for delivery of maintenance messages from the BS to the nodes of a distributed network is optimized. Differences in channel gain from the BS to sensor nodes make it a challenge to provide service to each of them while efficiently spending the harvested energy. Based on PTF (Power-Time-Fair), a close-to-optimal solution for fair allocation of harvested energy in a wireless downlink proposed in previous work, we develop an online algorithm, PTF-On, that operates two algorithms in tandem: A prediction algorithm based on a Kalman filter that operates on solar irradiation measurements, and a modified version of PTF. PTF-On can predict the energy arrival profile throughout the day and schedule transmission to nodes to maximize total throughput in a proportionally fair way.

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arXiv2015

Optimal Energy Allocation Policies for a High Altitude Flying Wireless Access Point

Inspired by recent industrial efforts toward high altitude flying wireless access points powered by renewable energy, an online resource allocation problem for a mobile access point (AP) travelling at high altitude is formulated. The AP allocates its resources (available energy) to maximize the total utility (reward) provided to a sequentially observed set of users demanding service. The problem is formulated as a 0/1 dynamic knapsack problem with incremental capacity over a finite time horizon, the solution of which is quite open in the literature. We address the problem through deterministic and stochastic formulations. For the deterministic problem, several online approximations are proposed based on an instantaneous threshold that can adapt to short-time-scale dynamics. For the stochastic model, after showing the optimality of a threshold based solution on a dynamic programming (DP) formulation, an approximate threshold based policy is obtained. The performances of proposed policies are compared with that of the optimal solution obtained through DP.

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arXiv2012

Proportional Fair Resource Allocation on an Energy Harvesting Downlink - Part I: Structure

This paper considers the allocation of time slots in a frame, as well as power and rate to multiple receivers on an energy harvesting downlink. Energy arrival times that will occur within the frame are known at the beginning of the frame. The goal is to optimize throughput in a proportionally fair way, taking into account the inherent differences of channel quality among users. Analysis of structural characteristics of the problem reveals that it can be formulated as a biconvex optimization problem, and that it has multiple optima. Due to the biconvex nature of the problem, a Block Coordinate Descent (BCD) based optimization algorithm that converges to an optimal solution is presented. Numerical and simulation results show that the power-time allocations found by BCD achieve a good balance between total throughput and fairness.

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arXiv2012

Proportional Fair Resource Allocation on an Energy Harvesting Downlink - Part II: Algorithms

In this paper, the proportionally fair allocation of time slots in a frame, as well as power level to multiple receivers in an energy harvesting broadcast system, is considered. Energy harvest times in a frame are assumed to be known at the beginning of that frame. The goal is to solve an optimization problem designed to maximize a throughput-based utility function that provides proportional fairness among users. An optimal solution of the problem was obtained by using a Block Coordinate Descent (BCD) method in earlier work (presented in Part I of this study). However, finding the optimal allocation entails a computational complexity that increases sharply in terms of the number of users or slots. In this paper, certain structural characteristics of the optimal power-time allocation policy are derived. Building on those, two simple and computationally scalable heuristics, PTF and ProNTO are proposed. Simulation results suggest that PTF and ProNTO can closely track the performance of the BCD solution.

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arXiv2007

Fair Scheduling in OFDMA-based Wireless Systems with QoS Constraints

In this work we consider the problem of downlink resource allocation for proportional fairness of long term received rates of data users and quality of service for real time sessions in an OFDMA-based wireless system. The base station allocates available power and bandwidth to individual users based on long term average received rates, QoS based rate constraints and channel conditions. We solve the underlying constrained optimization problem and propose an algorithm that achieves the optimal allocation. Numerical evaluation results show that the proposed algorithm provides better QoS to voice and video sessions while providing more and fair rates to data users in comparison with existing schemes.

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arXiv2007

Practical Resource Allocation Algorithms for QoS in OFDMA-based Wireless Systems

In this work we propose an efficient resource allocation algorithm for OFDMA based wireless systems supporting heterogeneous traffic. The proposed algorithm provides proportionally fairness to data users and short term rate guarantees to real-time users. Based on the QoS requirements, buffer occupancy and channel conditions, we propose a scheme for rate requirement determination for delay constrained sessions. Then we formulate and solve the proportional fair rate allocation problem subject to those rate requirements and power/bandwidth constraints. Simulations results show that the proposed algorithm provides significant improvement with respect to the benchmark algorithm.

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