Browsing by Subject "Wireless communications"

Now showing 1 - 9 of 9

Open Access
Deep learning based channel equalization for MIMO ISI channels
(2022-09) Eren, Berke
Future wireless communications is expected to bring significant changes along with a number of emerging technologies such as 5G, virtual reality, edge computing, and IoT. These developments pose unprecedented demands in terms of capacity, coverage, latency, efficiency, flexibility, compatibility, and quality of experience on wireless communication systems. Machine Learning (ML) techniques are considered as a promising tool to tackle this challenge due to their ability to manage big data, powerful nonlinear mapping, and distributed computing capabilities. There have been many research results addressing different aspects of ML algorithms and their connections to wireless communications; however, there are still various challenges that need to be addressed. In particular, their use for communication systems with memory, is not fully investigated. With this motivation, this thesis considers an application of ML, in particular, deep learning (DL), techniques for communications over intersymbol interference (ISI) channels. In this thesis, we propose DL-based channel equalization algorithms for channels with ISI. We introduce three different DL-based ISI detectors, namely sliding bidirectional long short term memory (Sli-BiLSTM), sliding multi layer perceptron (Sli-MLP), and sliding iterative (Sli-Iterative), and demonstrate that they are computationally efficient and capable of performing equalization under a variety of channel conditions with the knowledge of the channel state information. We also employ sliding bidirectional gated recurrent unit (Sli-BiGRU) and Sli- MLP, which are more suitable for use with fixed ISI channels. As an extension, we also examine DL-based equalization techniques for multiple-input multipleoutput (MIMO) ISI channels. Numerical results show that proposed models are well suited for equalization of ISI channels with perfect as well as noisy CSI for a broad range of signal-to-noise ratio (SNR) levels as long as the ISI length is very close to the optimal solution, namely, the maximum likelihood sequence estimation, implemented through the Viterbi Algorithm while having considerably less complexity, and they have superior performance compared to MMSE-based channel equalization.
Open Access
Deep-learning for communication systems: new channel estimation, equalization, and secure transmission solutions
(2023-08) Gümüş, Mücahit
Traditional communication system design takes a model-based approach that aims to optimize relevant performance metrics using somewhat simple and tractable channel and signal models. For instance, channel codes are designed for simple additive white Gaussian or fading channel models, channel equalization algorithms are based on mathematical models for inter-symbol interference (ISI), and channel estimation techniques are developed with the underlying channel statistics and characterizations in mind. Through utilizing superior mathematical models and expert knowledge in signal processing and information theory, the model-based approach has been highly successful and has enabled development of many communication systems until now. On the other hand, beyond 5G wireless communication systems will further exploit the massive number of antennas, higher bandwidths, and more advanced multiple access technologies. As communication systems become more and more complicated, it is becoming increasingly important to go beyond the limits of the model-based approach. Noting that there have been tremendous advancements in learning from data over the past decades, a major research question is whether machine learning based approaches can be used to develop new communication technologies. With the above motivation, this thesis deals with the development of deep neural network (DNN) solutions to address various challenges in wireless communications. We ﬁrst consider orthogonal frequency division multiplexing (OFDM) over rapidly time-varying multipath channels, for which the performance of standard channel estimation and equalization techniques degrades dramatically due to inter-carrier in-terference (ICI). We focus on improving the overall system performance by designing DNN architectures for both channel estimation and data demodulation. In addition, we study OFDM over frequency-selective channels without cyclic preﬁx insertion in an eﬀort to improve the overall throughputs. Speciﬁcally, we design a recurrent neu-ral network to mitigate the eﬀects of ISI and ICI for improved symbol detection. Furthermore, we explore secure transmission over multi-input multi-output multi-antenna eavesdropper wiretap channels with ﬁnite alphabet inputs. We use a linear precoder to maximize the secrecy rate, which beneﬁts from the generalized singular value decomposition to obtain independent streams and exploits function approximation abilities of DNNs for solving the required power allocation problem. We also propose a DNN technique to jointly optimize the data precoder and the power allocation for artiﬁcial noise. We use extensive numerical examples and computational complexity analyses to demonstrate the eﬀectiveness of the proposed solutions.
Open Access
Exact and heuristic approaches based on noninterfering transmissions for joint gateway selection, time slot allocation, routing and power control for wireless mesh networks
(Elsevier, 2017) Gokbayrak, K.; Yıldırım, E. A.
Wireless mesh networks (WMNs) provide cost-effective alternatives for extending wireless communication over larger geographical areas. In this paper, given a WMN with its nodes and possible wireless links, we consider the problem of gateway node selection for connecting the network to the Internet along with operational problems such as routing, wireless transmission capacity allocation, and transmission power control for efficient use of wired and wireless resources. Under the assumption that each node of the WMN has a fixed traffic rate, our goal is to allocate capacities to the nodes in proportion to their traffic rates so as to maximize the minimum capacity-to-demand ratio, referred to as the service level. We adopt a time division multiple access (TDMA) scheme, in which a time frame on the same frequency channel is divided into several time slots and each node can transmit in one or more time slots. We propose two mixed integer linear programming formulations. The first formulation, which is based on individual transmissions in each time slot, is a straightforward extension of a previous formulation developed by the authors for a related problem under a different set of assumptions. The alternative formulation, on the other hand, is based on sets of noninterfering wireless transmissions. In contrast with the first formulation, the size of the alternative formulation is independent of the number of time slots in a frame. We identify simple necessary and sufficient conditions for simultaneous transmissions on different links of the network in the same time slot without any significant interference. Our characterization, as a byproduct, prescribes a power level for each of the transmitting nodes. Motivated by this characterization, we propose a simple scheme to enumerate all sets of noninterfering transmissions, which is used as an input for the alternative formulation. We also introduce a set of valid inequalities for both formulations. For large instances, we propose a three-stage heuristic approach. In the first stage, we solve a partial relaxation of our alternative optimization model and determine the gateway locations. This stage also provides an upper bound on the optimal service level. In the second stage, a routing tree is constructed for each gateway node computed in the first stage. Finally, in the third stage, the alternative optimization model is solved by fixing the resulting gateway locations and the routing trees from the previous two stages. For even larger networks, we propose a heuristic approach for solving the partial relaxation in the first stage using a neighborhood search on gateway locations. Our computational results demonstrate the promising performance of our exact and heuristic approaches and the valid inequalities
Open Access
Learning and inference for wireless communications applications using in-memory analog computing
(2024-07) Ali, Muhammad Atif
The exponential growth of wireless communication technologies has created a crucial need for more efficient and intelligent signal processing in decentralized devices and systems. Traditional digital computing architectures increasingly struggle to meet these rising computational demands, leading to performance bottlenecks and energy inefficiencies. The problem becomes more significant on edge devices with limited computing capabilities and severe energy limitations. Integrating machine learning algorithms with in-memory analog computing, specifically memristor-based architectures, provides a non-traditional computing paradigm and can potentially enhance the energy efficiency of edge devices. By leveraging the properties of memristors, which can perform both storage and computation, this research investigates ways to potentially reduce latency and power consumption in signal-processing tasks for wireless communications. This study examines memristor-based analog computing for deep learning and inference in three areas of (wireless) communications: cellular network traffic prediction, multi-sensor over-the-air inference for internet-of-things devices, and neural successive cancellation decoding for polar codes. The research includes the development of robust training techniques for memristive neural networks to cater for degraded performance due to noise in analog computations and offer acceptable prediction accuracy with reduced computational overhead for network traffic management. It explores in-memory computing for an Lp-norm inspired sensor fusion method with analog sensors and enables more efficient multi-sensor data fusion. Also, it investigates the incorporation of analog memristive computing in neural successive cancellation decoders for polar codes, which could lead to more energy-efficient decoding algorithms. The findings of the thesis suggest potential improvements in energy efficiency and provide insights into the benefits and limitations of using in-memory computing for wireless communication applications.
Open Access
A new wireless asynchronous data communications module for industrial applications
(2013) Ege, Y.; Şensoy, M.G.; Kalender O.; Nazlibilek, S.; Çitak H.
All the sensors such as temperature, humidity, and pressure used in industry provide analog outputs as inputs for their control units. Wireless transmission of the data has advantages on wired transmission such as USB port, parallel port and serial port and therefore has great importance for industrial applications. In this work, a new wireless asynchronous data communications module has been developed to send the earth magnetic field data around a ferromagnetic material detected by a KMZ51 AMR sensor. The transmitter module transmits the analog data obtained from a source to a computer environment where they are stored and then presented in a graphical form. In this design, an amplitude shift keying (ASK) transceiver working at the frequency of 433.92 MHz which is a frequency inside the so called Industrial Scientific Medical band (ISM band) used for wireless communications. The analog data first fed into a 10-bit ADC controlled by a PIC microcontroller and then the digital data is sent to the transmitter. A preamble bit string is added in front of the data bits and another bit string for achieving synchronization and determination the start of the data is used. The data arriving at the receiver is taken by the microcontroller and sent to a LCD display as well as the serial port of a computer where it is written in a text file. A Visual Basic based graphics interface is designed to receive, store and present the data in the form of graphical shapes. In the paper, all the work has been explained in detail. © 2013 Published by Elsevier Ltd. All rights reserved.
Open Access
Optimum power randomization for the minimization of outage probability
(IEEE, 2013) Dulek, B.; Vanli, N. D.; Gezici, Sinan; Varshney P. K.
The optimum power randomization problem is studied to minimize outage probability in flat block-fading Gaussian channels under an average transmit power constraint and in the presence of channel distribution information at the transmitter. When the probability density function of the channel power gain is continuously differentiable with a finite second moment, it is shown that the outage probability curve is a nonincreasing function of the normalized transmit power with at least one inflection point and the total number of inflection points is odd. Based on this result, it is proved that the optimum power transmission strategy involves randomization between at most two power levels. In the case of a single inflection point, the optimum strategy simplifies to on-off signaling for weak transmitters. Through analytical and numerical discussions, it is shown that the proposed framework can be adapted to a wide variety of scenarios including log-normal shadowing, diversity combining over Rayleigh fading channels, Nakagami-m fading, spectrum sharing, and jamming applications. We also show that power randomization does not necessarily improve the outage performance when the finite second moment assumption is violated by the power distribution of the fading. © 2013 IEEE.
Open Access
Over the air federated edge learning with hierarchical clustering
(IEEE, 2024-12) Aygün, Ozan; Kazemi, Mohammad; Gündüz, Deniz; Duman, Tolga Mete
We examine federated learning (FL) with over-the-air (OTA) aggregation, where mobile users (MUs) aim to reach a consensus on a global model with the help of a parameter server (PS) that aggregates the local gradients. In OTA FL, MUs train their models using local data at every training round and transmit their gradients simultaneously using the same frequency band in an uncoded fashion. Based on the received signal of the superposed gradients, the PS performs a global model update. While the OTA FL has a significantly decreased communication cost, it is susceptible to adverse channel effects and noise. Employing multiple antennas at the receiver side can reduce these effects, yet the path-loss is still a limiting factor for users located far away from the PS. To ameliorate this issue, in this paper, we propose a wireless-based hierarchical FL scheme that uses intermediate servers (ISs) to form clusters in the areas where the MUs are more densely located. Our scheme utilizes OTA cluster aggregations for the communication of the MUs with their corresponding IS, and OTA global aggregations from the ISs to the PS. We present a convergence analysis for the proposed algorithm, and show through numerical evaluations of the derived analytical expressions and experimental results that utilizing ISs results in a faster convergence and a better performance than the OTA FL alone while using less transmit power. We also validate the results on the performance using different numbers of cluster iterations with different datasets and data distributions. We conclude that the best choice of cluster aggregations depends on the data distribution among the MUs and the clusters.
Open Access
Performance analysis of an in-band full-duplex MAC protocol for future wireless networks
(IEEE, 2024-03) Sarmad, Wardah; Shahid, Syed Maaz; Karasan, Ezhan; Kwon, Sungoh
In this article, we extend IEEE 802.11 from carrier-sense multiple access with collision avoidance to carrier-sense multiple access with collision detection (CSMA/CD) for in-band full-duplex (IBFD) wireless systems by utilizing the capabilities of full-duplex. In IBFD communications, nodes can effectively apply CSMA/CD but this may result in false alarms and missed collision detection due to residual self-interference. To analyze the performance of medium access control (MAC) protocol for an IBFD communications system, first, a Markov chain-based analytical model is designed for a CSMA/CD-based IEEE 802.11 distributed coordination function with IBFD capabilities. Then, the analytical expressions for goodput and packet loss probability are driven to investigate the impact of various parameters, including contention window size, packet length, and the number of nodes, on the performance of the designed model in the presence of sensing errors. The accuracy of the analytical model is validated by comparing the numerical and simulation results for saturated traffic conditions.
Open Access
Tree-based channel assignment schemes for multi-channel wireless sensor networks
(John Wiley & Sons Ltd., 2016) Terzi, C.; Korpeoglu, I.
Many sensor node platforms used for establishing wireless sensor networks (WSNs) can support multiple radio channels for wireless communication. Therefore, rather than using a single radio channel for whole network, multiple channels can be utilized in a sensor network simultaneously to decrease overall network interference, which may help increase the aggregate network throughput and decrease packet collisions and delays. This method, however, requires appropriate schemes to be used for assigning channels to nodes for multi-channel communication in the network. Because data generated by sensor nodes are usually delivered to the sink node using routing trees, a tree-based channel assignment scheme is a natural approach for assigning channels in a WSN. We present two fast tree-based channel assignment schemes (called bottom up channel assignment and neighbor count-based channel assignment) for multi-channel WSNs. We also propose a new interference metric that is used by our algorithms in making decisions. We validated and evaluated our proposed schemes via extensive simulation experiments. Our simulation results show that our algorithms can decrease interference in a network, thereby increasing performance, and that our algorithms are good alternatives for static channel assignment in WSNs.