Browsing by Subject "Precoding"

Now showing 1 - 11 of 11

Open Access
Deep learning based interference exploitation in 1-bit massive MIMO precoding
(Institute of Electrical and Electronics Engineers, 2023-02-14) Hossienzadeh, M.; Aghaeinia, H.; Kazemi, Mohammad
In this paper, we focus on one-bit precoding approach for downlink massive multiple-input multiple-output (MIMO) systems, where we exploit the concept of constructive interference (CI) employing deep learning (DL) techniques. One of the main performance limiting factors in wireless communication systems is interference, which needs to be minimized or mitigated. By controlling the interference signals in order to add up constructively at the receiver side, there is a possibility to improve the system performance. This paper presents a DL-based one-bit precoding scheme that improves the massive MIMO performance via CI exploitation in the presence of one-bit digital to analog converters (DAC) as a hardware impairment. More precisely, for phase shift keying signaling, we first formulate the optimization problem in order to maximize the CI effects in the case of a base station equipped with one-bit DACs. Then, after solving the optimization problem and creating a large enough dataset, a DL network is trained to do the precoding. Numerical results show that the DL-based solution approaches the performance of the conventional interference exploitation one-bit precoding schemes in the massive MIMO systems while having an order of magnitude less complexity.
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
Joint precoder and artificial noise design for MIMO wiretap channels with finite-alphabet inputs based on the cut-off rate
(Institute of Electrical and Electronics Engineers Inc., 2017) Aghdam, S. R.; Duman, T. M.
We consider precoder and artificial noise (AN) design for multi-antenna wiretap channels under the finite-alphabet input assumption. We assume that the transmitter has access to the channel coefficients of the legitimate receiver and knows the statistics of the eavesdropper's channel. Accordingly, we propose a secrecy rate maximization algorithm using a gradient descent-based optimization of the precoder matrix and an exhaustive search over the power levels allocated to the AN. We also propose algorithms to reduce the complexities of direct ergodic secrecy rate maximization by: 1) maximizing a cut-off rate-based approximation for the ergodic secrecy rate, simplifying the mutual information expression, which lacks a closed-form and 2) diagonalizing the channels toward the legitimate receiver and the eavesdropper, which allows for employing a per-group precoding-based technique. Our numerical results reveal that jointly optimizing the precoder and the AN outperforms the existing solutions in the literature, which rely on the precoder optimization only. We also demonstrate that the proposed low complexity alternatives result in a small loss in performance while offering a significant reduction in computational complexity.
Open Access
Low complexity precoding for MIMOME wiretap channels based on cut-off rate
(IEEE, 2016) Aghdam, Sina Rezaei; Duman, Tolga M.
We propose a low complexity transmit signal design scheme for achieving information-theoretic secrecy over a MIMO wiretap channel driven by finite-alphabet inputs. We assume that the transmitter has perfect channel state information (CSI) of the main channel and also knows the statistics of the eavesdropper's channel. The proposed transmission scheme relies on jointly optimizing the precoder matrix and the artificial noise so as to maximize the achievable secrecy rates. In order to lower the computational complexity associated with the transmit signal design, we employ a design metric using the cut-off rate instead of the mutual information. We formulate a gradient-descent based optimization algorithm and demonstrate via extensive numerical examples that the proposed signal design scheme can yield an enhanced secrecy performance compared with the existing solutions in spite of its relatively lower computational complexity. The impacts of the modulation order as well as the number of antennas at the transmitter and receiver ends on the achievable secrecy rates are also investigated.
Open Access
One-bit massive MIMO precoding using unsupervised deep learning
(Institute of Electrical and Electronics Engineers, 2024-02-01) Hosseinzadeh, Mohsen; Aghaeinia, Hassan; Kazemi, Mohammad
The recently emerged symbol-level precoding (SLP) technique is a promising solution in multi-user wireless communication systems due to its ability to transform harmful multi-user interference (MUI) into useful signals, thereby improving system performance. Conventional symbol-level precoding designs have a significant computational complexity that makes their practical implementation difficult and imposes excessive computational complexity on the system. To deal with this problem, we suggest a new deep learning (DL) based approach that utilizes low-complexity designs of symbol-level precoding. This paper focuses on DL-based one-bit precoding approaches for downlink massive multiple-input multiple-output (MIMO) systems, where one-bit digital-to-analog converters (DACs) are used to reduce cost and power. Unlike previous works, the optimized one-bit precoder for multiuser massive MIMO system (HDL-O1PmMIMO) for a wide range of signal-to-noise-ratio (SNR) has a low computational complexity, making it suitable for real precoding scenarios. In this paper, we first design an unsupervised DL-based precoder (UDL-O1PmMIMO) to address the low SNR scenarios, using which we then design a hybrid DL-based precoder (HDL-O1PmMIMO) to address both low and high SNR scenarios. The method suggested in this article utilizes a novel residual DL network structure, which helps overcome the problem of training very deep networks. Additionally, a novel customized cost function, specifically for one-bit precoding in massive MIMO systems, is introduced to optimize the performance of the system in handling interference. The results of an experiment conducted on a general test set using Python and MATLAB show that the proposed approach outperforms existing methods in three aspects: it has a lower bit error rate, it takes less time to generate the precoded vector, and it is more resistant to imperfect channel estimation.
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
Physical layer security for space shift keying transmission with precoding
(Institute of Electrical and Electronics Engineers Inc., 2016) Aghdam, S. R.; Duman, T. M.
We investigate the effect of transmitter side channel state information on the achievable secrecy rates of space shift keying. Through derivation of the gradient of the secrecy rate, we formulate an iterative algorithm to maximize the achievable secrecy rates. We also introduce two lower complexity signal design algorithms for different scenarios based on the number of antennas at the eavesdropper. Our results illustrate the effectiveness of the proposed precoding techniques in attaining positive secrecy rates over a wide range of signal to noise ratios. © 2016 IEEE.
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
Search-free precoder selection for 5G new radio using neural networks
(IEEE, 2020-12) Akyıldız, Talha; Duman, Tolga M.
We propose a search-free precoder selection method with neural networks motivated by the fact that large codebook sizes are adopted in 5G New Radio (5G-NR). The proposed method does not require an explicit codebook search unlike the traditional selection algorithms. Instead, it aims at finding the precoder matrix index that maximizes the corresponding channel capacity using a neural network directly. The network is trained off-line using extensive simulated data with the underlying channel statistics; however, the actual selection algorithm is based on simple calculations with the neural network, hence it is feasible for real time implementation. We demonstrate that the proposed search-free selection algorithm is highly efficient, i.e., it results in a performance very close to optimal precoder in the codebook while its complexity is significantly lower. Simulations with realistic channel models of 5G-NR corroborate these observations as well. We also show that pruning of the trained neural network gives a way to achieve further complexity reduction with a very small reduction in the system performance.
Open Access
Secure multi-antenna transmission with finite-alphabet signaling
(2017-12) Aghdam, Sina Rezaei
With the ever-growing demand for services that rely on transmission over wireless networks, a challenging issue is the security of the transmitted information. Due to its open nature, wireless communications is prone to eavesdropping attacks. Typically, secrecy of the transmitted information is ensured with the aid of cryptographic techniques, which are deployed on upper layers of the network protocol stack. However, due to the need for key distribution and management, cryptographic solutions are difficult to implement in decentralized networks. Moreover, the security provided by key based solutions is not provable from a mathematical point of view. Physical layer security is an alternative or complement to the cryptographic techniques, which can resolve the complexities associated with key distribution and management. The basic principle of physical layer security is to exploit the randomness of the communication channels to allow a transmitter deliver its message to an intended receiver reliably while guaranteeing that a third party cannot infer any information about it. Much of the existing research in physical layer security focuses on investigating the information theoretic limits of secure communications. Among different techniques proposed, multiple-antenna based solutions have been shown to exhibit a high potential for enhancing security. Furthermore, Gaussian inputs are proved to be the optimal input distributions in a variety of scenarios. However, due to the high detection complexity, Gaussian signaling is not used in practice, and the transmission is carried out with the aid of symbols drawn from standard signal constellations. In this thesis, we develop several secure multi-antenna transmission techniques under the practical finite-alphabet input assumption. We first consider multipleinput multiple-output (MIMO) wiretap channels under finite-alphabet input constraints. We assume that the statistical channel state information (CSI) of the eavesdropper is available at the transmitter, and study two different scenarios regarding the transmitter's knowledge on the main channel CSI (MCSI) including availability of perfect and statistical MCSI at the transmitter. In each scenario, we introduce iterative algorithms for joint optimization of data precoder and arti ficial noise. We also propose different strategies to reduce the computational complexity associated with the transmit signal design. Moreover, we consider the setups with simultaneous wireless information and power transfer (SWIPT), and propose transmission schemes for achieving the trade-off between the secrecy rate and the harvested power. We demonstrate the efficacy of the proposed transmit signal design algorithms via extensive numerical examples. We also introduce several secure transmission schemes with spatial modulation and space shift keying (SSK). We derive an expression for the achievable secrecy rate, and develop precoder optimization algorithms for its maximization using transmitter side CSI. Furthermore, we introduce a group of secure SSK transmission schemes, which rely on dynamic antenna index assignment over reciprocal channels. Our results reveal that the fundamentally different working principle of SSK opens up new avenues for secure multi-antenna transmission.