Browsing by Subject "OFDM"

Now showing 1 - 13 of 13

Open Access
Blind federated learning at the wireless edge with low-resolution ADC and DAC
(IEEE, 2021-06-15) Teğin, Büşra
We study collaborative machine learning systems where a massive dataset is distributed across independent workers which compute their local gradient estimates based on their own datasets. Workers send their estimates through a multipath fading multiple access channel with orthogonal frequency division multiplexing to mitigate the frequency selectivity of the channel. We assume that there is no channel state information (CSI) at the workers, and the parameter server (PS) employs multiple antennas to align the received signals. To reduce the power consumption and the hardware costs, we employ complex-valued low-resolution digital-to-analog converters (DACs) and analog-to-digital converters (ADCs), at the transmitter and the receiver sides, respectively, and study the effects of practical low-cost DACs and ADCs on the learning performance. Our theoretical analysis shows that the impairments caused by low-resolution DACs and ADCs, including those of one-bit DACs and ADCs, do not prevent the convergence of the federated learning algorithms, and the multipath channel effects vanish when a sufficient number of antennas are used at the PS. We also validate our theoretical results via simulations, and demonstrate that using low-resolution, even one-bit, DACs and ADCs causes only a slight decrease in the learning accuracy.
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
Deep receiver design for multi-carrier waveforms using CNNs
(IEEE, 2020) Yıldırım, Y.; Özer, Sedat; Çırpan, H. A.
In this paper, a deep learning based receiver is proposed for a collection of multi-carrier wave-forms including both current and next-generation wireless communication systems. In particular, we propose to use a convolutional neural network (CNN) for jointly detection and demodulation of the received signal at the receiver in wireless environments. We compare our proposed architecture to the classical methods and demonstrate that our proposed CNN-based architecture can perform better on different multi-carrier forms including OFDM and GFDM in various simulations. Furthermore, we compare the total number of required parameters for each network for memory requirements.
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
A delay-tolerant asynchronous two-way-relay system over doubly-selective fading channels
(Institute of Electrical and Electronics Engineers Inc., 2015) Salim, A.; Duman, T. M.
We consider design of asynchronous orthogonal frequency division multiplexing (OFDM) based diamond two-way-relay (DTWR) systems in a time-varying frequency-selective (doubly-selective) fading channel. In a DTWR system, two users exchange their messages with the help of two relays. Most of the existing works on asynchronous DTWR systems assume only small relative propagation delays between the received signals at each node that do not exceed the length of the cyclic-prefix (CP). However, in certain practical communication systems, significant differences in delays may take place, and hence existing solutions requiring excessively long CPs may be highly inefficient. In this paper, we propose a delay-independent CP insertion mechanism in which the CP length depends only on the number of subcarriers and the maximum delay spread of the corresponding channels. We also propose a symbol detection algorithm that is able to tolerate very long relative delays, that even exceed the length of the OFDM block itself, without a large increase in complexity. The proposed system is shown to significantly outperform other alternatives in the literature through a number of specific examples. © 2015 IEEE.
Open Access
Design of application specific instruction set processors for the EFT and FHT algorithms
(2006) Atak, Oğuzhan
Orthogonal Frequency Division Multiplexing (OFDM) is a multicarrier transmission technique which is used in many digital communication systems. In this technique, Fast Fourier Transformation (FFT) and inverse FFT (IFFT) are kernel processing blocks which are used for data modulation and demodulation respectively. Another algorithm which can be used for multi-carrier transmission is the Fast Hartley Transform algorithm. The FHT is a real valued transformation and can give significantly better results than FFT algorithm in terms of energy efficiency, speed and die area. This thesis presents Application Specific Instruction Set Processors (ASIP) for the FFT and FHT algorithms. ASIPs combine the flexibility of general purpose processors and efficiency of application specific integrated circuits (ASIC). Programmability makes the processor flexible and special instructions, memory architecture and pipeline makes the processor efficient. In order to design a low power processor we have selected the recently proposed cached FFT algorithm which outperforms standard FFT. For the cached FFT algorithm we have designed two ASIPs one having a single execution unit
Open Access
Design of application specific processors for the cached FFT algorithm
(IEEE, 2006-05) Atak, Oğuzhan; Atalar, Abdullah; Arıkan, Erdal; Ishebabi, H.; Kammler, D.; Ascheid, G.; Meyr, H.; Nicola, M.; Masera, G.
Orthogonal frequency division multiplexing (OFDM) is a data transmission technique which is used in wired and wireless digital communication systems. In this technique, fast Fourier transformation (FFT) and inverse FFT (IFFT) are kernel processing blocks in an OFDM system, and are used for data (de)modulation. OFDM systems are increasingly required to be flexible to accommodate different standards and operation modes, in addition to being energy-efficient. A trade-off between these two conflicting requirements can be achieved by employing application-specific instruction-set processors (ASIPs). In this paper, two ASIP design concepts for the cached FFT algorithm (CFFT) are presented. A reduction in energy dissipation of up to 25% is achieved compared to an ASIP for the widely used Cooley-Tukey FFT algorithm, which was designed by using the same design methodology and technology. Further, a modified CFFT algorithm which enables a better cache utilization is presented. This modification reduces the energy dissipation by up to 10% compared to the original CFFT implementation.
Open Access
Distributed caching and learning over wireless channels
(2020-01) Tegin, Büşra
Coded caching and coded computing have drawn signiﬁcant attention in recent years due to their advantages in reducing the traﬃc load and in distributing computational burden to edge devices. There have been many research results addressing diﬀerent aspects of these problems; however, there are still various challenges that need to be addressed. In particular, their use over wireless channels is not fully understood. With this motivation, this thesis considers these two distributed systems over wireless channels taking into account realistic channel eﬀects as well as practical implementation constraints. In the ﬁrst part of the thesis, we study coded caching over a wireless packet erasure channel where each receiver encounters packet erasures independently with the same probability. We propose two diﬀerent schemes for packet erasure channels: sending the same message (SSM) and a greedy approach. Also, a simpliﬁed version of the greedy algorithm called the grouped greedy algorithm is proposed to reduce the system complexity. For the grouped greedy algorithm, an upper bound for transmission rate is derived, and it is shown that this upper bound is very close to the simulation results for small packet erasure probabilities. We then study coded caching over non-ergodic fading channels. As the multicast capacity of a broadcast channel is restricted by the user experiencing the worst channel conditions, we formulate an optimization problem to minimize the transmission time by grouping users based on their channel conditions, and transmit coded messages according to the worst channel in the group, as opposed to the worst among all. We develop two algorithms to determine the user groups: a locally optimal iterative algorithm and a numerically more eﬃcient solution through a shortest path problem. In the second part of the thesis, we study collaborative machine learning (ML) systems, which is also known as federated learning, where a massive dataset is distributed across independent workers that compute their local gradient estimates based on their own datasets. Workers send their estimates through a multipath fading multiple access channel (MAC) with orthogonal frequency division multiplexing (OFDM) to mitigate the frequency selectivity of the channel. We assume that the parameter server (PS) employs multiple antennas to align the received signals with no channel state information (CSI) at the workers. To reduce the power consumption and hardware costs, we employ complex-valued low-resolution analog to digital converters (ADCs) at the receiver side and study the eﬀects of practical low cost ADCs on the learning performance of the system. Our theoretical analysis shows that the impairments caused by a low-resolution ADC do not prevent the convergence of the learning algorithm, and fading eﬀects vanish when a suﬃcient number of antennas are used at the PS. We also validate our theoretical results via simulations, and further, we show that using one-bit ADCs causes only a slight decrease in the learning accuracy.
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
FPGA based implementation of IEEE 80211a physical layer
(2010) İnce, Mustafa
Orthogonal Frequency Division Multiplexing (OFDM) is a multicarrier transmission technique, in which a single bitstream is transmitted over a large number of closely-spaced orthogonal subcarriers. It has been adopted for several technologies, such as Wireless Local Area Networks (WLAN), Digital Audio and Terrestrial Television Broadcasting and Worldwide Interoperability for Microwave Access (WiMAX) systems. In this work, IEEE802.11a WLAN standard was implemented on Field Programmable Gate Array (FPGA) for being familiar with the implementation problems of OFDM systems. The algorithms that are used in the implementation were firstly built up in MATLAB environment and the performance of system was observed with a simulator developed for this purpose. The transmitter and receiver FPGA implementations, which support the transmission rates from 6 to 54 Mbps, were designed in Xilinx System Generator Toolbox for MATLAB Simulink environment. The modulation technique and the Forward Error Coding (FEC) rate used at the transmitter are automatically adjusted by the desired bitrate as BPSK, QPSK, 16QAM or 64QAM and 1/2, 2/3 or 3/4, respectively.The transceiver utilizes 5986 slices, 45 block RAMs and 73 multipliers of a Xilinx Virtex-4 sx35 chip corresponding to % 39 of the resources. In addition, the FPGA implementation of the transceiver was also tested by constructing a wireless link between two Lyrtech Software Defined Radio Development Kits and the bit error rate of the designed system was measured by performing a digital loop-back test under an Additive White Gaussian Noise (AWGN) channel.
Open Access
Low complexity equalization for OFDM in doubly selective channels
(2009) Pamuk, Alptekin
In current standards Orthogonal Frequency Division Multiplex -OFDM- is widely used for its high resistance to multi-path environments and high spectral ef- ficiency. However since the transmission duration is longer, it is affected from time variations of the channel more than single carrier systems. Orthogonality of sub-carriers are lost within an OFDM symbol and intercarrier interference(ICI) occurs as a result of time variation of the channel. Channel estimation and equalization become problematic, because the classical structures like MMSE require very complex operations. This thesis studies the channel equalization problem, as separate from the channel estimation problem. The thesis assumes that the channel coefficients are perfectly known and focuses on the estimation of data transmitted on each OFDM carrier. First, a survey of existing algorithms on channel equalization is given and simulations are provided to compare them in terms of complexity and performance under an OFDM system scenario that is consistent with the present WiMAX system parameters and operating conditions. As a novel contribution, the thesis proposes two new equalization methods by amending existing algorithms and shows that these modified algorithms improve the state-of-the-art in channel equalization in terms of complexity andperformance under certain high-mobility scenarios. Finally it is shown that the intercarrier interference cancellation problem remains a major impediment to the implementation of OFDM in high-mobility environments.
Open Access
An overview of physical layer security with finite-alphabet signaling
(Institute of Electrical and Electronics Engineers Inc., 2019) Aghdam, Sina Rezaei; Nooraiepour, A.; Duman, Tolga M.
Providing secure communications over the physical layer with the objective of achieving secrecy without requiring a secret key has been receiving growing attention within the past decade. The vast majority of the existing studies in the area of physical layer security focus exclusively on the scenarios where the channel inputs are Gaussian distributed. However, in practice, the signals employed for transmission are drawn from discrete signal constellations such as phase shift keying and quadrature amplitude modulation. Hence, understanding the impact of the finite-alphabet input constraints and designing secure transmission schemes under this assumption is a mandatory step toward a practical implementation of physical layer security. With this motivation, this paper reviews recent developments on physical layer security with finite-alphabet inputs. We explore transmit signal design algorithms for single-antenna as well as multi-antenna wiretap channels under different assumptions on the channel state information at the transmitter. Moreover, we present a review of the recent results on secure transmission with discrete signaling for various scenarios including multi-carrier transmission systems, broadcast channels with confidential messages, cognitive multiple access and relay networks. Throughout the article, we stress the important behavioral differences of discrete versus Gaussian inputs in the context of the physical layer security. We also present an overview of practical code construction over Gaussian and fading wiretap channels, and discuss some open problems and directions for future research.
Open Access
Performance of edge windowing for OFDM under non-linear power amplifier effects
(IEEE, 2017) Göken, Çağrı; Dizdar, Onur
Edge windowing is a windowing technique for Orthogonal Frequency Division Multiplexing (OFDM) signals based on the idea of using shorter cyclic prefix (CP) and longer window lengths at the edge subcarriers while keeping the symbol length fixed. In this study, we investigate the performance of OFDM signals with edge windowing under non-linear power amplifier (PA) effects by observing out-of-band (OOB) emission characteristics, average error vector magnitude (EVM) and coded block error rate (BLER) performance. We explore whether the possible gains over conventional windowing in the presence of PA is possible. We show that the edge windowing can still provide improvements over conventional windowing in terms of OOB emission suppression under various PA models at the expense of increased average EVM, whereas the channel coding substantially mitigates the performance loss due to inter-symbol and inter-carrier interference (ISI-ICI) effects arising as a result of shorter CP length at the edge subcarriers.