Browsing by Author "Duman, Tolga Mete"

Now showing 1 - 12 of 12

Open Access
A slotted pilot-based unsourced random access scheme with a multiple-antenna receiver
(Institute of Electrical and Electronics Engineers, 2023-08-30) Özateş, Mert; Kazemi, Mohammad; Duman, Tolga Mete
We consider unsourced random access over fading channels with a massive number of antennas at the base station, and propose a simple, yet energy-efficient solution by dividing the transmission frame into slots. We utilize non-orthogonal pilot sequences followed by a polar codeword for transmission in each slot. At the receiver side, we first detect the transmitted pilot sequences by employing a generalized orthogonal matching pursuit algorithm and utilize a linear minimum mean square error solution to estimate the channel vectors. We then perform an iterative decoding based on maximal ratio combining, single-user polar decoding, and successive interference cancellation with re-estimation of the channel vectors to recover the data bits. We also analyze the performance of the proposed scheme using normal approximations and provide a detailed complexity analysis. Numerical examples demonstrate that the proposed scheme either outperforms the existing schemes in the literature or has a competitive performance with a lower complexity. Furthermore, it is suitable for fast-fading scenarios due to its excellent performance in the short blocklength regime.
Open Access
An energy-efficient feedback-aided irregular repetition slotted ALOHA scheme and its asymptotic performance analysis
(Institute of Electrical and Electronics Engineers, 2023-05-12) Haghighat, Javad; Duman, Tolga Mete
We present a decentralized feedback-aided Irregular Repetition Slotted ALOHA (IRSA) scheme that improves energy efficiency. The scheme divides the IRSA MAC frame into several sub-frames, performs tentative decoding after each sub-frame, and uses limited feedback for users to detect whether their packet has been decoded at the receiver. Once a user detects that its packet is decoded, it stops transmitting its remaining replicas, resulting in a decrease in the expected number of transmitted packet replicas and an increase in energy efficiency. For analysis, we employ a graph-based representation of the successive interference cancellation decoding of IRSA. We prove several results for a fixed graph, and extend our analysis to a randomly selected graph to derive the efficiency of the proposed scheme. Numerical results show that the proposed feedback-aided IRSA solution outperforms standard IRSA and performs similarly to the best known Coded Slotted ALOHA (CSA) schemes. Also, the proposed scheme achieves efficiencies significantly larger than the threshold of 0.5 which is an upper bound for standard IRSA.
Open Access
Analysis of Coded Slotted ALOHA with energy harvesting nodes for Perfect and Imperfect Packet Recovery Scenarios
(Institute of Electrical and Electronics Engineers, 2023-03-06) Haghighat, Javad; Duman, Tolga Mete
We analyze the performance of Coded Slotted ALOHA (CSA) protocols in scenarios where users are equipped with limited batteries that are recharged through Energy Harvesting (EH). First, we assume a Perfect Packet Recovery Scenario (PPRS) for which the received packets are decoded with no errors when there is no interference. We introduce Battery Outage Probability (BOP) as an extra performance metric; and, we derive the optimal EH-CSA transmission policies, which offer the maximum attainable traffic load while maintaining an asymptotically negligible Packet Loss Ratio (PLR), under specific rate and BOP constraints. We extend our study to Imperfect Packet Recovery Scenario (IPRS) where impairments at the physical layer, including channel estimation and channel decoding errors, will distort messages being passed through the iterative Successive Interference Cancellation (SIC) process. The distorted messages being passed through the SIC process potentially lead to error propagation. In order to track the error propagation process, we define the concept of Accumulated Noise plus Interference Power (ANIP), and analytically track the evolution of its probability distribution. We employ our results to evaluate the bit error rates for different transmission policies for the case of IPRS. We also demonstrate the advantages of the optimal transmission policies through numerical examples for both PPRS and IPRS. Our results show that the optimal EH-CSA policies outperform the policies optimized for standard CSA without EH considerations, and the schemes that are optimal for PPRS are not necessarily optimal for the IPRS case. Furthermore, the EH-CSA optimal policies strictly outperform standard CRDSA when the system is required to support higher traffic loads.
Open Access
Channel estimation and symbol demodulation for OFDM systems over rapidly varying multipath channels with hybrid deep neural networks
(Institute of Electrical and Electronics Engineers, 2023-05-01) Gümüş, Mücahit; Duman, Tolga Mete
We consider orthogonal frequency division multiplexing over rapidly time-varying multipath channels, for which performance of standard channel estimation and equalization techniques degrades dramatically due to inter-carrier interference (ICI). We focus on improving the overall system performance by designing deep neural network (DNN) architectures for both channel estimation and data demodulation. To accomplish this, we employ the basis expansion model to track the channel tap variations, and exploit convolutional neural networks’ learning abilities of local correlations together with a coarse least square solution for a robust and accurate channel estimation procedure. For data demodulation, we use a recurrent neural network for improved performance and robustness as single tap frequency-domain equalizers perform poorly, and more sophisticated equalization techniques such as band-limited linear minimum mean squared error equalizers are vulnerable to model mismatch and channel estimation errors. Numerical examples illustrate that the proposed DNN architectures outperform the traditional algorithms. Specifically, the bit error rate results for a wide range of Doppler values reveal that the proposed DNN-based equalizer is robust, and it mitigates the ICI effectively, offering an excellent demodulation performance. We further note that the DNN-based channel estimator offers an improved performance with a reduced computational complexity.
Open Access
Energy efficiency analysis of a feedback-aided IRSA scheme
(Institute of Electrical and Electronics Engineers, 2022-08-03) Haghighat, Javad; Duman, Tolga Mete
Irregular Repetition Slotted ALOHA (IRSA) achieves load thresholds very close to 1 at the expense of reduced energy efficiency compared to its competitor, Coded Slotted ALOHA (CSA). The efficiency is related to the expected number of transmitted replicas, and is upper-bounded by 0.5 in the case of IRSA. In this paper, we present a feedback-aided IRSA scheme, analyze its efficiency, and show that utilizing a very limited feedback will offer considerable improvements. Remarkably, the feedback-aided scheme enables IRSA to achieve efficiencies greater than 0.5, and in some cases, perform very close to the more complex CSA schemes.
Open Access
Federated learning with over-the-air aggregation over time-varying channels
(Institute of Electrical and Electronics Engineers, 2023-01-17) Tegin, Büşra; Duman, Tolga Mete
We study federated learning (FL) with over-the-air aggregation over time-varying wireless channels. Independent workers compute local gradients based on their local datasets and send them to a parameter server (PS) through a time-varying multipath fading multiple access channel via orthogonal frequency-division multiplexing (OFDM). We assume that the workers do not have channel state information, hence the PS employs multiple antennas to alleviate the fading effects. Wireless channel variations result in inter-carrier interference, which has a detrimental effect on the performance of OFDM systems, especially when the channel is rapidly varying. We examine the effects of the channel time variations on the convergence of the FL with over-the-air aggregation, and show that the resulting undesired interference terms have only limited destructive effects, which do not prevent the convergence of the learning algorithm. We also validate our results via extensive simulations, which corroborate the theoretical expectations.
Open Access
Reliable extraction of semantic information and rate of innovation estimation for graph signals
(Institute of Electrical and Electronics Engineers , 2022-12-19) Kalfa, Mert ; Yetim, Sadık Yağız ; Atalik, Arda ; Gök, Mehmetcan; Ge, Y.; Li, R.; Tong, W.; Duman, Tolga Mete; Arıkan, Orhan
Semantic signal processing and communications are poised to play a central part in developing the next generation of sensor devices and networks. A crucial component of a semantic system is the extraction of semantic signals from the raw input signals, which has become increasingly tractable with the recent advances in machine learning (ML) and artificial intelligence (AI) techniques. The accurate extraction of semantic signals using the aforementioned ML and AI methods, and the detection of semantic innovation for scheduling transmission and/or storage events are critical tasks for reliable semantic signal processing and communications. In this work, we propose a reliable semantic information extraction framework based on our previous work on semantic signal representations in a hierarchical graph-based structure. The proposed framework includes a time integration method to increase fidelity of ML outputs in a class-aware manner, a graph-edit-distance based metric to detect innovation events at the graph-level and filter out sporadic errors, and a Hidden Markov Model (HMM) to produce smooth and reliable graph signals. The proposed methods within the framework are demonstrated individually and collectively through simulations and case studies based on real-world computer vision examples.
Open Access
Robust joint precoding/combining design for multiuser MIMO systems with calibration errors
(Institute of Electrical and Electronics Engineers, 2023-01-05) Kazemi, Mohammad; Göken, Çağrı; Duman, Tolga Mete
We consider the downlink of a multiuser system operating in the time-division duplexing mode, for which base station (BS) and users are equipped with multiple antennas, and provide a robust precoding/combining design against imperfect channel state information (CSI) and calibration errors due to hardware mismatch. Towards this end, we first formulate a robust joint precoder and combiner design as a stochastic minimum mean squared error optimization problem. Then, employing an alternating optimization approach, we propose an algorithm to obtain the precoding and combining matrices assuming imperfect CSI and calibration errors at both the BS and the user sides. We also provide asymptotic closed-form expressions for the mean squared error (MSE) and the achievable sum-rate in the massive MIMO regime. The results indicate that while the MSE linearly increases with the calibration errors at the user side, the sum-rate is asymptotically independent of them. Extensive simulation results show that the proposed robust joint precoder/combiner outperforms the existing solutions while having the same order of complexity. Moreover, when the BS sends a quantized version of the combining coefficients to the users, it is observed that the proposed solution is more robust to the quantization errors than the existing algorithms.
Open Access
Robust joint transceiver design for multiuser MIMO systems with calibration errors
(Institute of Electrical and Electronics Engineers, 2022-08-11) Kazemi, Mohammad; Göken, Ç.; Duman, Tolga Mete
We consider the downlink of a multiuser multiple-input multiple-output (MIMO) system operating in the time-division duplexing (TDD) mode. In this mode, assuming reciprocity, the channel coefficients estimated during the uplink channel training are utilized by the base station (BS) in the downlink data transmission. However, due to hardware mismatches, the uplink and downlink channels are not exactly the same, and therefore, there are calibration errors, which degrade the system performance. In this paper, our goal is to provide a transceiver design which has a robust performance under imperfect channel reciprocity. To this end, we first formulate a robust joint precoder and combiner design as a stochastic minimum mean square error (MMSE) optimization problem. Then, employing an alternating optimization approach, we propose an algorithm to obtain the precoding and combining matrices assuming imperfect CSI and calibration errors at both the BS and user sides. Extensive simulation results show that the proposed robust joint precoder/combiner outperforms the existing solutions in the literature.
Open Access
Straggler mitigation through unequal error protection for distributed approximate matrix multiplication
(Institute of Electrical and Electronics Engineers Inc., 2022-02-01) Tegin, Büşra; Hernandez, Eduin E.; Rini, Stefano; Duman, Tolga Mete
Large-scale machine learning and data mining methods routinely distribute computations across multiple agents to parallelize processing. The time required for the computations at the agents is affected by the availability of local resources and/or poor channel conditions, thus giving rise to the “straggler problem.” In this paper, we address this problem for distributed approximate matrix multiplication. In particular, we employ Unequal Error Protection (UEP) codes to obtain an approximation of the matrix product to provide higher protection for the blocks with a higher effect on the multiplication outcome. We characterize the performance of the proposed approach from a theoretical perspective by bounding the expected reconstruction error for matrices with uncorrelated entries. We also apply the proposed coding strategy to the computation of the back-propagation step in the training of a Deep Neural Network (DNN) for an image classification task in the evaluation of the gradients. Our numerical experiments show that it is indeed possible to obtain significant improvements in the overall time required to achieve DNN training convergence by producing approximation of matrix products using UEP codes in the presence of stragglers.
Open Access
Towards goal-oriented semantic signal processing: Applications and future challenges
(Elsevier, 2021-06-15) Kalfa, Mert; Gök, Mehmetcan; Atalık, Arda; Tegin, Büşra; Arıkan, Orhan; Duman, Tolga Mete
Advances in machine learning technology have enabled real-time extraction of semantic information in signals which can revolutionize signal processing techniques and improve their performance significantly for the next generation of applications. With the objective of a concrete representation and efficient processing of the semantic information, we propose and demonstrate a formal graph-based semantic language and a goal filtering method that enables goal-oriented signal processing. The proposed semantic signal processing framework can easily be tailored for specific applications and goals in a diverse range of signal processing applications. To illustrate its wide range of applicability, we investigate several use cases and provide details on how the proposed goal-oriented semantic signal processing framework can be customized. We also investigate and propose techniques for communications where sensor data is semantically processed and semantic information is exchanged across a sensor network.
Open Access
Unsourced random access using multiple stages of orthogonal pilots: MIMO and single-antenna structures
(Institute of Electrical and Electronics Engineers , 2024-06-27) Ahmadi, Mohammad Javad; Kazemi, Mohammad; Duman, Tolga Mete
We study the problem of unsourced random access (URA) over Rayleigh block-fading channels with a receiver equipped with multiple antennas. We propose a slotted structure with multiple stages of orthogonal pilots, each of which is randomly picked from a codebook. In the proposed signaling structure, each user encodes its message using a polar code and appends it to the selected pilot sequences to construct its transmitted signal. Accordingly, the transmitted signal is composed of multiple orthogonal pilot parts and a polar-coded part, which is sent through a randomly selected slot. The performance of the proposed scheme is further improved by randomly dividing users into different groups each having a unique interleaver-power pair. We also apply the idea of multiple stages of orthogonal pilots to the case of a single receive antenna. In all the set-ups, we use an iterative approach for decoding the transmitted messages along with a suitable successive interference cancellation technique. The use of orthogonal pilots and the slotted structure lead to improved accuracy and reduced computational complexity in the proposed set-ups, and make the implementation with short blocklengths more viable. Performance of the proposed set-ups is illustrated via extensive simulation results which show that the proposed set-ups with multiple antennas perform better than the existing MIMO URA solutions for both short and large blocklengths, and that the proposed single-antenna set-ups are superior to the existing single-antenna URA schemes.