References of "Elbir, Ahmet M."
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See detailA family of deep learning architectures for channel estimation and hybrid beamforming in multi-carrier mm-wave massive MIMO.
Elbir, Ahmet M.; Mishra, Kumar Vijay; Mysore Rama Rao, Bhavani Shankar UL et al

in IEEE Transactions on Cognitive Communications and Networking (2021)

Hybrid analog and digital beamforming transceivers are instrumental in addressing the challenge of expensive hardware and high training overheads in the next generation millimeter-wave (mm-Wave) massive ... [more ▼]

Hybrid analog and digital beamforming transceivers are instrumental in addressing the challenge of expensive hardware and high training overheads in the next generation millimeter-wave (mm-Wave) massive MIMO (multiple-input multiple-output) systems. However, lack of fully digital beamforming in hybrid architectures and short coherence times at mm-Wave impose additional constraints on the channel estimation. Prior works on addressing these challenges have focused largely on narrowband channels wherein optimization-based or greedy algorithms were employed to derive hybrid beamformers. In this paper, we introduce a deep learning (DL) approach for channel estimation and hybrid beamforming for frequency-selective, wideband mm-Wave systems. In particular, we consider a massive MIMO Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system and propose three different DL frameworks comprising convolutional neural networks (CNNs), which accept the raw data of received signal as input and yield channel estimates and the hybrid beamformers at the output. We also introduce both offline and online prediction schemes. Numerical experiments demonstrate that, compared to the current state-of-the-art optimization and DL methods, our approach provides higher spectral efficiency, lesser computational cost and fewer number of pilot signals, and higher tolerance against the deviations in the received pilot data, corrupted channel matrix, and propagation environment. [less ▲]

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See detailHybrid Beamforming for Terahertz Joint Ultra-Massive MIMO Radar-Communications
Elbir, Ahmet M.; Mishra, Kumar Vjiay; Chatzinotas, Symeon UL

in IEEE Journal of Selected Topics in Signal Processing (2021), 15(6), 1468-1483

In this paper, we investigate the hybrid beamforming problem in joint radar-communications at terahertz (THz) bands. In order to address the extremely high attenuation at THz, ultra-massive multiple-input ... [more ▼]

In this paper, we investigate the hybrid beamforming problem in joint radar-communications at terahertz (THz) bands. In order to address the extremely high attenuation at THz, ultra-massive multiple-input multiple-output (UM-MIMO) antenna systems have been proposed for THz communications to compensate propagation losses. Further, we propose a new group-of-subarrays (GoSA) UM-MIMO structure to reduce the hardware cost. We formulate the GoSA beamformer design as an optimization problem to provide a trade-off between the unconstrained communications beamformers and the desired radar beamformers. Numerical experiments demonstrate that the proposed approach outperforms the conventional approaches in terms of spectral efficiency and hardware costs. [less ▲]

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See detailFederated Learning for Physical Layer Design
Elbir, Ahmet M.; Papazafeiropoulos, Anastasios; Chatzinotas, Symeon UL

in IEEE Communications Magazine (2021)

Model-free techniques, such as machine learning (ML), have recently attracted much interest towards the physical layer design, e.g., symbol detection, channel estimation, and beamforming. Most of these ML ... [more ▼]

Model-free techniques, such as machine learning (ML), have recently attracted much interest towards the physical layer design, e.g., symbol detection, channel estimation, and beamforming. Most of these ML techniques employ centralized learning (CL) schemes and assume the availability of datasets at a parameter server (PS), demanding the transmission of data from edge devices, such as mobile phones, to the PS. Exploiting the data generated at the edge, federated learning (FL) has been proposed recently as a distributed learning scheme, in which each device computes the model parameters and sends them to the PS for model aggregation while the datasets are kept intact at the edge. Thus, FL is more communication-efficient and privacy-preserving than CL and applicable to the wireless communication scenarios, wherein the data are generated at the edge devices. This article presents the recent advances in FL-based training for physical layer design problems. Compared to CL, the effectiveness of FL is presented in terms of communication overhead with a slight performance loss in the learning accuracy. The design challenges, such as model, data, and hardware complexity, are also discussed in detail along with possible solutions. [less ▲]

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See detailDeep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems
Elbir, Ahmet M.; Papazafeiropoulos, Anastasios; Kourtessis, Pandelis et al

in IEEE Wireless Communications Letters (2020), 9(Sept. 2020), 1447-1451

This letter presents the first work introducing a deep learning (DL) framework for channel estimation in large intelligent surface (LIS) assisted massive MIMO (multiple-input multiple-output) systems. A ... [more ▼]

This letter presents the first work introducing a deep learning (DL) framework for channel estimation in large intelligent surface (LIS) assisted massive MIMO (multiple-input multiple-output) systems. A twin convolutional neural network (CNN) architecture is designed and it is fed with the received pilot signals to estimate both direct and cascaded channels. In a multi-user scenario, each user has access to the CNN to estimate its own channel. The performance of the proposed DL approach is evaluated and compared with state-of-the-art DL-based techniques and its superior performance is demonstrated. [less ▲]

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