@article{stylianopoulos_bayraktar_gonzalez-prelcic_alexandropoulos_2023, title={Autoregressive Attention Neural Networks for Non-Line-of-Sight User Tracking with Dynamic Metasurface Antennas}, DOI={10.1109/CAMSAP58249.2023.10403512}, abstractNote={User localization and tracking in the upcoming generation of wireless networks have the potential to be revolutionized by technologies such as Dynamic Metasurface Antennas (DMAs). Commonly proposed algorithmic approaches rely on assumptions about relatively dominant Line-of-Sight (LoS) paths or may require pilot transmission sequences whose length is comparable to the number of DMA elements, thus leading to limited effectiveness and considerable measurement overheads in blocked LoS and dynamic multipath environments. Therefore, this paper proposes a two-stage machine-learning-based approach for user tracking, specifically designed for non-LoS multipath settings. A newly proposed Attention-based neural network is first trained to map noisy channel responses to potential user positions regardless of user-mobility patterns. This architecture constitutes a modification of the prominent Vision Transformer, specifically modified for extracting information from high-dimensional frequency response signals. As a second stage, its predictions for the past user positions are passed through a learnable autoregressive model to exploit the time-correlated information and obtain the final position predictions; thus the problems of localization and tracking are decomposed. The channel estimation procedure leverages a DMA architecture with partially-connected Radio Frequency Chains (RFCs), which results to reduced numbers of pilot signals. The numerical evaluation over an outdoor ray-tracing scenario illustrates that despite LoS blockage, this methodology is capable of achieving high position accuracy across various multipath settings.}, journal={2023 IEEE 9TH INTERNATIONAL WORKSHOP ON COMPUTATIONAL ADVANCES IN MULTI-SENSOR ADAPTIVE PROCESSING, CAMSAP}, author={Stylianopoulos, Kyriakos and Bayraktar, Murat and Gonzalez-Prelcic, Nuria and Alexandropoulos, George C.}, year={2023}, pages={391–395} }
 @article{bayraktar_rusu_gonzalez-prelcic_chen_2023, title={SELF-INTERFERENCE AWARE CODEBOOK DESIGN FOR FULL-DUPLEX JOINT SENSING AND COMMUNICATION SYSTEMS AT MMWAVE}, DOI={10.1109/CAMSAP58249.2023.10403422}, abstractNote={This paper proposes an analog beam codebook for full-duplex joint sensing and communication (JSAC) systems at millimeter wave (mmWave) bands. The codebook design is formulated as the minimization of the self-interference (SI) caused by full-duplex operation, while also considering constraints associated to the beam gains and the implementation based on phase shifters. It is shown that the proposed codebook is suitable for joint initial access and target detection in mmWave communication systems that leverage a full-duplex circuit. The simulation results reveal that the proposed design outperforms the benchmarks in terms of communication and sensing metrics, while operating with a practical analog beamformer.}, journal={2023 IEEE 9TH INTERNATIONAL WORKSHOP ON COMPUTATIONAL ADVANCES IN MULTI-SENSOR ADAPTIVE PROCESSING, CAMSAP}, author={Bayraktar, Murat and Rusu, Cristian and Gonzalez-Prelcic, Nuria and Chen, Hao}, year={2023}, pages={231–235} }
 @article{bayraktar_guvensen_2022, title={Adaptation of Code-Domain NOMA to SC-FDE Based Overloaded mmWave Hybrid Massive MIMO}, volume={26}, ISSN={["1558-2558"]}, DOI={10.1109/LCOMM.2021.3135379}, abstractNote={In this letter, we provide a practical framework to resolve whether code-domain NOMA (CD-NOMA) is beneficial when integrated with massive MIMO systems. In order to realize this integration, first, we develop a novel code-beamspace wideband signal model for uplink CD-NOMA in mmWave hybrid massive MIMO systems employing single-carrier (SC) transmission. Then, we apply a state-of-the-art SC frequency domain equalization (SC-FDE) based iterative receiver where the number of radio frequency (RF) chains is limited. Simulation results verify the effectiveness of the proposed architecture in overloaded scenarios. Furthermore, we show that CD-NOMA can enhance the performance of mmWave hybrid beamforming based massive MIMO systems by effectively decreasing the correlation between closely separated user channels in joint code-beamspace.}, number={3}, journal={IEEE COMMUNICATIONS LETTERS}, author={Bayraktar, Murat and Guvensen, Gokhan M.}, year={2022}, month={Mar}, pages={667–671} }
 @article{bayraktar_rusu_gonzalez-prelcic_zhang_2022, title={Beam Codebook Design for Joint Initial Access and Localization in mmWave Networks}, ISSN={["1058-6393"]}, DOI={10.1109/IEEECONF56349.2022.10052101}, abstractNote={Wireless networks are incorporating higher frequency bands and higher bandwidths by exploiting MIMO technology with large arrays. These large arrays and bandwidths enable high resolution estimates of the angles and delays associated to the different multipath components of the MIMO channel. Given the sparse nature of the millimeter wave (mmWave) channel, sparse recovery algorithms can extract the path parameters with reasonable accuracy. Moreover, channel sparsity also facilitates the association of these multipath components to the geometry of the environment, providing sufficient information to determine the user position. In this paper, we address the problem of designing the set of training precoders and combiners that, while providing a high accuracy channel and position estimate, also result in a reduced training overhead with respect to standardized beam training strategies. As performance metric, we consider the mutual coherence between the training hybrid precoders/combiners and the overcomplete dictionary used to represent the channel. The proposed scheme significantly reduces overhead and outperforms previous designs in terms of the accuracy of the channel estimate, which results in a higher localization accuracy and a higher spectral efficiency.}, journal={2022 56TH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS, AND COMPUTERS}, author={Bayraktar, Murat and Rusu, Cristian and Gonzalez-Prelcic, Nuria and Zhang, Charlie Jianzhong}, year={2022}, pages={919–924} }
 @article{bayraktar_palacios_gonzalez-prelcic_zhang_2022, title={Multidimensional Orthogonal Matching Pursuit-based RIS-aided Joint Localization and Channel Estimation at mmWave}, ISSN={["2325-3789"]}, DOI={10.1109/SPAWC51304.2022.9833999}, abstractNote={RIS-aided millimeter wave wireless systems benefit from robustness to blockage and enhanced coverage. In this paper, we study the ability of RIS to also provide enhanced localization capabilities as a by-product of communication. We consider sparse reconstruction algorithms to obtain high resolution channel estimates that are mapped to position information. In RIS-aided mmWave systems, the complexity of sparse recovery becomes a bottleneck, given the large number of elements of the RIS and the large communication arrays. We propose to exploit a multidimensional orthogonal matching pursuit strategy for compressive channel estimation in a RIS-aided millimeter wave system. We show how this algorithm, based on computing the projections on a set of independent dictionaries instead of a single large dictionary, enables high accuracy channel estimation at reduced complexity. We also combine this strategy with a localization approach which does not rely on the absolute time of arrival of the LoS path. Localization results in a realistic 3D indoor scenario show that RIS-aided wireless system can also benefit from a significant improvement in localization accuracy.}, journal={2022 IEEE 23RD INTERNATIONAL WORKSHOP ON SIGNAL PROCESSING ADVANCES IN WIRELESS COMMUNICATION (SPAWC)}, author={Bayraktar, Murat and Palacios, Joan and Gonzalez-Prelcic, Nuria and Zhang, Charlie Jianzhong}, year={2022} }