arXiv subjects

Ender Ayanoglu

Publications and source records attributed to Ender Ayanoglu.

At least 19 recordsLinked to original sources

arXiv2026

RIS-Enabled Wireless Channel Equalization: Adaptive RIS Equalizer and Deep Reinforcement Learning

Reconfigurable Intelligent Surfaces (RISs) offer a promising means of reshaping the wireless propagation environment, yet practical methods for configuring large passive arrays to achieve reliable signal equalization remain limited. Equalization is essential in wideband links to counteract multipath-induced pulse distortion that otherwise degrades symbol recovery. This work investigates RIS-assisted pulse response equalization and signal boosting using both classical adaptive filtering and model-free deep reinforcement learning (DRL). We develop a steepest descent (SD) method that exploits cascaded BS-RIS-UE channel information to configure RIS coefficients for multipath mitigation and SNR enhancement, and we show that the tradeoffs between SD and DRL primarily arise from the extensive channel estimation required for accurate equalization with passive RIS hardware. Unlike traditional adaptive filtering, which updates delayed filter coefficients after signal reception, our approach uses the RIS positioned within the cascaded channel to perform equalization without delay elements, prior to reception at the UE. In this framework, the channel is estimated before equalization, forming the basis of what we term adaptive RIS equalization (ARISE). To overcome the reliance on channel estimation required for ARISE, we explore several DRL algorithms -- DDPG, TD3, and SAC -- that optimize RIS coefficients directly from the received pulse response without explicit channel estimation. Through extensive simulations across diverse channel conditions and RIS sizes, we show that SAC achieves fast, stable convergence and equalization performance comparable to ARISE while offering significantly lower implementation complexity. These results highlight the potential of DRL as a practical and scalable solution for real-time RIS control in future wireless systems.

cs.IT↗

arXiv2025

Experimental Analysis of Biasing Voltage Generation in Wave-Controlled RIS

Reconfigurable intelligent surfaces (RISs), an emerging technology proposed for inclusion in next generation wireless communication systems, are programmable surfaces that can adaptively reflect incident electromagnetic radiation in different desired directions. To reduce the complexity and physical profile of conventional RIS designs, a novel concept known as Wave-Controlled RIS has been proposed, in which standing waves along a transmission line are used to generate the required dc bias for reflective control. This paper shows the design of such a Wave-Controlled RIS and its biasing transmission line. The effectiveness of this approach in generating the correct dc bias from a single standing wave frequency is analyzed through both theoretical modeling and experimental validation, which uncovered a dependence on impedance matching not accounted for by the theory. Additionally, the potential for reflective control using only a single standing wave frequency on the biasing transmission line is explored, demonstrating the ability of single-beam steering toward angles near broadside.

eess.SP↗

arXiv2025

Discrete Beamforming Optimization for RISs with a Limited Phase Range and Amplitude Attenuation

This paper addresses the problem of maximizing the received power at a user equipment via reconfigurable intelligent surface (RIS) characterized by phase-dependent amplitude (PDA) and discrete phase shifts over a limited phase range. Given complex RIS coefficients, that is, discrete phase shifts and PDAs, we derive the necessary and sufficient conditions to achieve the optimal solution. To this end, we propose an optimal search algorithm that is proven to converge in linear time within at most NK steps, significantly outperforming the exhaustive search approach that would otherwise be needed for RISs with amplitude attenuation. Furthermore, we introduce a practical quantization framework for PDA-introduced RISs termed amplitude-introduced polar quantization (APQ), and extend it to a novel algorithm named extended amplitude-introduced polar quantization (EAPQ) that works with geometric projections. We derive closed-form expressions to assess how closely the performance of the proposed RIS configuration can approximate the ideal case with continuous phases and no attenuation. Our analysis reveals that increasing the number of discrete phases beyond K = 4 yields only marginal gains, regardless of attenuation levels, provided the RIS has a sufficiently wide phase range R. Furthermore, we also show and quantify that when the phase range R is limited, the performance is sensitive to attenuation for larger R, and sensitive to R when there is less attenuation. Finally, the proposed optimal algorithm provides a generic upper bound that could serve as a benchmark for discrete beamforming in RISs with amplitude constraints.

eess.SP↗

arXiv2025

AI-Driven Optimization of Wave-Controlled Reconfigurable Intelligent Surfaces

A promising type of Reconfigurable Intelligent Surface (RIS) employs tunable control of its varactors using biasing transmission lines below the RIS reflecting elements. Biasing standing waves (BSWs) are excited by a time-periodic signal and sampled at each RIS element to create a desired biasing voltage and control the reflection coefficients of the elements. A simple rectifier can be used to sample the voltages and capture the peaks of the BSWs over time. Like other types of RIS, attempting to model and accurately configure a wave-controlled RIS is extremely challenging due to factors such as device non-linearities, frequency dependence, element coupling, etc., and thus significant differences will arise between the actual and assumed performance. An alternative approach to solving this problem is data-driven: Using training data obtained by sampling the reflected radiation pattern of the RIS for a set of BSWs, a neural network (NN) is designed to create an input-output map between the BSW amplitudes and the resulting sampled radiation pattern. This is the approach discussed in this paper. In the proposed approach, the NN is optimized using a genetic algorithm (GA) to minimize the error between the predicted and measured radiation patterns. The BSW amplitudes are then designed via Simulated Annealing (SA) to optimize a signal-to-leakage-plus-noise ratio measure by iteratively forward-propagating the BSW amplitudes through the NN and using its output as feedback to determine convergence. The resulting optimal solutions are stored in a lookup table to be used both as settings to instantly configure the RIS and as a basis for determining more complex radiation patterns.

cs.ET↗

arXiv2024

Alternative Channel Charting Techniques in Cellular Wireless Communications

We investigate the use of conventional angle of arrival (AoA) algorithms the Bartlett's algorithm, the Minimum Variance Distortion Response (MVDR or Capon) algorithm, and the Minimum Norm algorithm for estimating the AoA $\theta$ together with our previously introduced algorithms linear regression (LR), inverse of the root sum squares of channel coefficients (ISQ), as well as a novel use of the MUSIC algorithm for estimating the distance from the base station, $\rho$ in the context of channel charting. We carry out evaluations in terms of the visual quality of the channel charts, the dimensionality reduction performance measures trustworthiness (TW) and connectivity (CT), as well as the execution time of the algorithms. We find that although the Bartlett's algorithm, MVDR, and Minimum Norm algorithms have sufficiently close performance to techniques we studied earlier, the Minimum Norm algorithm has significantly higher computational complexity than the other two. Previously, we found that the use of the MUSIC algorithm for estimation of both $\theta$ and $\rho$ has a very high performance. In this paper, we investigated and quantified the performance of the Bartlett algorithm in its use for estimating both $\theta$ and $\rho$, similar to the our previously introduced technique of using MUSIC for estimating both.

eess.SY↗

arXiv2024

Design and Operation Principles of a Wave-Controlled Reconfigurable Intelligent Surface

A Reflective Intelligent Surface (RIS) consists of many small reflective elements whose reflection properties can be adjusted to change the wireless propagation environment. Envisioned implementations require that each RIS element be connected to a controller, and as the number of RIS elements on a surface may be on the order of hundreds or more, the number of required electrical connectors creates a difficult wiring problem, especially at high frequencies where the physical space between the elements is limited. A potential solution to this problem was previously proposed by the authors in which "biasing transmission lines" carrying standing waves are sampled at each RIS location to produce the desired bias voltage for each RIS element. This solution has the potential to substantially reduce the complexity of the RIS control. This paper presents models for the RIS elements that account for mutual coupling and realistic varactor characteristics, as well as circuit models for sampling the transmission line to generate the RIS control signals. For the latter case, the paper investigates two techniques for conversion of the transmission line standing wave voltage to the varactor bias voltage, namely an envelope detector and a sample-and-hold circuit. The paper also develops a modal decomposition approach for generating standing waves that are able to generate beams and nulls in the resulting RIS radiation pattern that maximize either the Signal-to-Noise Ratio (SNR) or the Signal-to-Leakage-plus-Noise Ratio (SLNR). Extensive simulation results are provided for the two techniques, together with a discussion of computational complexity.

eess.SY↗

arXiv2024

Received Power Maximization Using Nonuniform Discrete Phase Shifts for RISs With a Limited Phase Range

To maximize the received power at a user equipment, the problem of optimizing a reconfigurable intelligent surface (RIS) with a limited phase range R < 2{\pi} and nonuniform discrete phase shifts with adjustable gains is addressed. Necessary and sufficient conditions to achieve this maximization are given. These conditions are employed in two algorithms to achieve the global optimum in linear time for R {\ge} {\pi} and R < {\pi}, where R is the limited RIS phase range. With a total number of N(2K + 1) complex vector additions, it is shown for R {\ge} {\pi} and R < {\pi} that the global optimality is achieved in NK or fewer and N(K + 1) or fewer steps, respectively, where N is the number of RIS elements and K is the number of discrete phase shifts which may be placed nonuniformly over the limited phase range R. In addition, we define two quantization algorithms that we call nonuniform polar quantization (NPQ) algorithm and extended nonuniform polar quantization (ENPQ) algorithm, where the latter is a novel quantization algorithm for RISs with a significant phase range restriction, i.e., R < {\pi}. With NPQ, we provide a closed-form solution for the approximation ratio with which an arbitrary set of nonuniform discrete phase shifts can approximate the continuous solution. We also show that with a phase range limitation, equal separation among the nonuniform discrete phase shifts maximizes the normalized performance. Furthermore, we show that the gain of using K {\ge} 3 with R < {\pi}/2 and K {\ge} 4 with R < {\pi} is only marginal. Finally, we prove that when R < 2{\pi}/3, ON/OFF selection for the RIS elements brings significant performance compared to the case when the RIS elements are strictly ON.

eess.SY↗

arXiv2023

Achieving Optimum Received Power with Elementwise Updates in the Least Number of Steps for Discrete-Phase RISs

The problem of optimizing discrete phases in a reconfigurable intelligent surface (RIS) to maximize the received power at a user equipment is addressed. Necessary and sufficient conditions to achieve this maximization are given. These conditions are employed in an algorithm to achieve the maximization. New versions of the algorithm are given that are proven to achieve convergence in N or fewer steps whether the direct link is completely blocked or not, where N is the number of the RIS elements, whereas previously published results achieve this in KN or 2N number of steps where K is the number of discrete phases. Thus, for a discrete-phase RIS, the techniques presented in this paper achieve the optimum received power in the smallest number of steps published in the literature. In addition, in each of those N steps, the techniques presented in this paper determine only one or a small number of phase shifts with a simple elementwise update rule, which result in a substantial reduction of computation time, as compared to the algorithms in the literature. As a secondary result, we define the uniform polar quantization (UPQ) algorithm which is an intuitive quantization algorithm that can approximate the continuous solution with an approximation ratio of sinc^2(1/K) and achieve low time-complexity, given perfect knowledge of the channel.

cs.IT↗

arXiv2023

Comments on "A Linear Time Algorithm for the Optimal Discrete IRS Beamforming"

The problem of optimizing discrete phases in a reconfigurable intelligent surface (RIS) to maximize the received power at a user equipment is addressed. Comments on [1] are provided. Updated necessary and sufficient conditions for its Lemma 1 are given. Consequently, an updated Algorithm 1 is provided with full specification. Simulation results with improved performance over the implementation of Algorithm 1 are provided. New versions of the algorithm are given that are proven to achieve convergence in N or fewer steps, where N is the number of the elements in the reconfigurable intelligent surface. This is in contrast with KN or 2N number of steps on the average specified for Algorithm 1 in [1], where K is the number of discrete phases. As a result, for a discrete-phase RIS, the techniques presented here achieve the optimum received power in the smallest number of steps published in the literature.

cs.IT↗

arXiv2022

Self-Supervised RF Signal Representation Learning for NextG Signal Classification with Deep Learning

Deep learning (DL) finds rich applications in the wireless domain to improve spectrum awareness. Typically, DL models are either randomly initialized following a statistical distribution or pretrained on tasks from other domains in the form of transfer learning without accounting for the unique characteristics of wireless signals. Self-supervised learning (SSL) enables the learning of useful representations from Radio Frequency (RF) signals themselves even when only limited training data samples with labels are available. We present a self-supervised RF signal representation learning method and apply it to the automatic modulation recognition (AMR) task by specifically formulating a set of transformations to capture the wireless signal characteristics. We show that the sample efficiency (the number of labeled samples needed to achieve a certain performance) of AMR can be significantly increased (almost an order of magnitude) by learning signal representations with SSL. This translates to substantial time and cost savings. Furthermore, SSL increases the model accuracy compared to the state-of-the-art DL methods and maintains high accuracy when limited training data is available.

cs.NI↗

arXiv2022

Model-Based Approaches to Channel Charting

We present new ways of producing a channel chart [1] employing model-based approaches. We estimate the angle of arrival theta and the distance rho between the base station and the user equipment by employing our algorithms, inverse of the root sum squares of channel coefficients (ISQ) algorithm, linear regression (LR) algorithm, and MUSIC/MUSIC (MM) algorithm. We compare these methods with the channel charting algorithms principal component analysis (PCA), Sammon's method (SM), and autoencoder (AE) [1]. We show that ISQ, LR, and MM surpass PCA, SM, and AE in performance. We also compare our algorithm MM with an algorithm from the literature that uses the MUSIC algorithm jointly on theta and rho. We call this algorithm the JM algorithm. JM performs very slightly better than MM but at a substantial increase in complexity. Finally, we introduce the rotate-and-sum (RS) algorithm which has about the same performance as the MM and JM algorithms. Unlike MUSIC, RS does not employ eigenvalue and eigenvector analysis. Thus, it is more suitable for direct register transfer logic (RTL) implementation.

cs.ET↗

arXiv2022

Gaussian Kernel Variance For an Adaptive Learning Method on Signals Over Graphs

This paper discusses a special kind of a simple yet possibly powerful algorithm, called single-kernel Gradraker (SKG), which is an adaptive learning method predicting unknown nodal values in a network using known nodal values and the network structure. We aim to find out how to configure the special kind of the model in applying the algorithm. To be more specific, we focus on SKG with a Gaussian kernel and specify how to find a suitable variance for the kernel. To do so, we introduce two variables with which we are able to set up requirements on the variance of the Gaussian kernel to achieve (near-) optimal performance and can better understand how SKG works. Our contribution is that we introduce two variables as analysis tools, illustrate how predictions will be affected under different Gaussian kernels, and provide an algorithm finding a suitable Gaussian kernel for SKG with knowledge about the training network. Simulation results on real datasets are provided.

eess.SP↗

arXiv2022

Machine Learning in NextG Networks via Generative Adversarial Networks

Generative Adversarial Networks (GANs) are Machine Learning (ML) algorithms that have the ability to address competitive resource allocation problems together with detection and mitigation of anomalous behavior. In this paper, we investigate their use in next-generation (NextG) communications within the context of cognitive networks to address i) spectrum sharing, ii) detecting anomalies, and iii) mitigating security attacks. GANs have the following advantages. First, they can learn and synthesize field data, which can be costly, time consuming, and nonrepeatable. Second, they enable pre-training classifiers by using semi-supervised data. Third, they facilitate increased resolution. Fourth, they enable the recovery of corrupted bits in the spectrum. The paper provides the basics of GANs, a comparative discussion on different kinds of GANs, performance measures for GANs in computer vision and image processing as well as wireless applications, a number of datasets for wireless applications, performance measures for general classifiers, a survey of the literature on GANs for i)-iii) above, and future research directions. As a use case of GAN for NextG communications, we show that a GAN can be effectively applied for anomaly detection in signal classification (e.g., user authentication) outperforming another state-of-the-art ML technique such as an autoencoder.

cs.LG↗

arXiv2022

Wave-Controlled Metasurface-Based Reconfigurable Intelligent Surfaces

Reconfigurable Intelligent Surfaces (RISs) are programmable metasurfaces that can adaptively steer received electromagnetic energy in desired directions by employing controllable phase shifting cells. Among other uses, an RIS can modify the propagation environment in order to provide wireless access to user locations that are not otherwise reachable by a base station. Alternatively, an RIS can steer the waves away from particular locations in space, to eliminate interference and allow for co-existence of the wireless network with other types of fixed wireless services (e.g., radars, unlicensed radio bands, etc.). The novel approach in this work is a wave-controlled architecture that properly accounts for the maximum possible change in the local reflection phase that can be achieved by adjacent RIS elements. It obviates the need for dense wiring and signal paths that would be required for individual control of every RIS element, and thus offers a substantial reduction in the required hardware. We specify this wave-controlled RIS architecture in detail and discuss signal processing and machine learning methods that exploit it in both point-to-point and multicell MIMO systems. Such implementations can lead to a dramatic improvement in next-generation wireless, radar, and navigation systems where RIS finds wide applications. They have the potential to improve the efficiency of spectrum utilization and coexistence by orders of magnitude.

eess.SP↗

arXiv2021

Diversity Analysis of Millimeter-Wave OFDM Massive MIMO Systems

We analyze the diversity gain for a distributed antenna subarray employing orthogonal frequency-division multiplexing (OFDM) in millimeter-wave (mm-Wave) massive multiple-input multiple-output (MIMO) systems. We show that the diversity gain depends on the number of transmitted data streams, the number of remote antenna units, and the number of propagation paths between RAUs. Furthermore, we show that by using bit-interleaved coded multiple beamforming (BICMB), one can achieve the maximum diversity gain in a distributed antenna subarray system. The assumption in both scenarios is that the number of the antennas at the transmitter and the receiver are large enough and channel state information (CSI) is known at the transmitter and the receiver.

cs.IT↗

arXiv2020

Bit-Interleaved Coded Multiple Beamforming with Perfect Coding in Massive MIMO Systems

This letter investigates bit-interleaved coded multiple beamforming (BICMB) with perfect coding in millimeter-wave (mm-wave) massive multiple-input multiple-output (MIMO) systems to achieve both maximum diversity gain and multiplexing gain. Using perfect coding with BICMB enables us to do this. We show that by using BICMB and perfect coding, the diversity gain becomes independent from the number of transmitted data streams and the number of antennas in each remote antenna unit (RAU) at the transmitter and the receiver. The assumption is that the perfect channel state information (CSI) is known at both the transmitter and the receiver and the number of antennas goes to infinity. This latter assumption can be relaxed by a large number of antennas in each RAU, similar to the case for all massive MIMO research. Simulation results show that when the perfect channel state information assumption is satisfied, the use of BICMB with perfect coding results in the diversity gain values predicted by the analysis.

cs.IT↗

arXiv2020

Millimeter Wave Massive MIMO Downlink Per-Group Communications with Hybrid Linear Precoding

We address the problem of analyzing and classifying in groups the downlink channel environment in a millimeter-wavelength cell, accounting for path loss, multipath fading, and User Equipment (UE) blocking, by employing a hybrid propagation and multipath fading model, thus using accurate inter-group interference modeling. The base station (BS) employs a large Uniform Planar Array (UPA) to facilitate massive Multiple-Input, Multiple-Output (MIMO) communications with high efficiency. UEs are equipped with a single antenna and are distributed uniformly within the cell. The key problem is analyzing and defining groups toward precoding. Because equitable type of throughput is desired between groups, Combined Frequency and Spatial Division and Multiplexing (CFSDM) prevails as necessary. We show that by employing three subcarrier frequencies, the UEs can be efficiently separated into high throughput groups, with each group employing Virtual Channel Model Beams (VCMB) based inner precoding, followed by efficient Multi-User Multiple-Input Multiple-Output (MU-MIMO) outer precoders. For each group, we study three different sub-grouping methods offering different advantages. We show that the improvement offered by Zero-Forcing Per-Group Precoding (ZF-PGP) over Zero-Forcing Precoding (ZFP) is very high.

cs.IT↗

arXiv2019

Bit-Interleaved Coded Multiple Beamforming in Millimeter-Wave Massive MIMO Systems

An analysis of the diversity gain for bit-interleaved coded multiple beamforming (BICMB) method in millimeter-wave (mm-Wave) massive multiple-input multiple-output (MIMO) systems is carried out for both the single-user and multi-user scenario. We show that the diversity gain is independent of the number of data streams and full spatial multiplexing order can be achieved in both scenarios. Also, we show that the diversity gain in the multi-user scenario is independent of the number of users in the system and only depends on the number of the remote antenna units (RAUs) at the transmitter side, when each user has only one RAU. The assumption here is that the channel state information (CSI) is known at both sides of the transmitter and the receiver and the number of antennas in each RAU goes to infinity. This latter assumption can be relaxed by a large number of antennas in each RAU, similar to the case for all massive MIMO research. Based on these assumptions, the diversity gain for the single-user scenario is $\frac{\left(\sum_{i,j}β_{ij}\right)^2}{\sum_{i,j}β_{ij}^2L_{ij}^{-1}}$ where $L_{ij}$ is the number of propagation paths and $β_{ij}$ is the large scale fading coefficient between the $i$th RAU in the transmitter and the $j$th RAU in the receiver. The diversity gain in the multi-user scenario for the $k$-th user is $\frac{M^2}{\sum_{j}L_{kj}^{-1}}$ where $M$ represents the number of RAUs at the transmitter. Simulation results show that when the perfect channel state information assumption is satisfied, the use of BICMB results in the diversity gain values predicted by the analysis.

cs.IT↗