Channel Estimation Overhead Reduction for Downlink FDD Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) is the concept of deploying a very large number of antennas at the base stations (BS) of cellular networks. Frequency-division duplexing (FDD) massive MIMO systems in the downlink (DL) suffer significantly from the channel estimation overhead. In this thesis, we propose a minimum mean square error (MMSE)-based channel estimation framework that exploits the spatial correlation between the antennas at the BS to reduce the latter overhead. We investigate how the number of antennas at the BS affects the channel estimation error through analytical and asymptotic analysis. In addition, we derive a lower bound on the spectral efficiency of the communication system. Close form expressions of the asymptotic MSE and the spectral efficiency lower bound are obtained. Furthermore, perfect match between theoretical and simulation results is observed, and results show the feasibility of our proposed scheme

Multi-Antenna Data-Driven Eavesdropping Attacks and Symbol-Level Precoding Countermeasures

In this work, we consider secure communications in wireless multi-user (MU) multiple-input single-output (MISO) systems with channel coding in the presence of a multi-antenna eavesdropper (Eve). In this setting, we exploit machine learning (ML) tools to design soft and hard decoding schemes by using precoded pilot symbols as training data. In this context, we propose ML frameworks for decoders that allow an Eve to determine the transmitted message with high accuracy. We thereby show that MU-MISO systems are vulnerable to such eavesdropping attacks even when relatively secure transmission techniques are employed, such as symbol-level precoding (SLP). To counteract this attack, we propose two novel SLP-based schemes that increase the bit-error rate at Eve by impeding the learning process. We design these two security-enhanced schemes to meet different requirements regarding complexity, security, and power consumption. Simulation results validate both the ML-based eavesdropping attacks as well as the countermeasures, and show that the gain in security is achieved without affecting the decoding performance at the intended users.Comment: Submitted to the IEEE Transactions on Information Forensics and Securit

Data-driven Precoded MIMO Detection Robust to Channel Estimation Errors

We study the problem of symbol detection in downlink coded multiple-input multiple-output (MIMO) systems with precoding and without the explicit knowledge of the channel-state information (CSI) at the receiver. In this context, we investigate the impact of imperfect CSI at the transmitter (CSIT) on the detection performance. We first model the CSIT degradation based on channel estimation errors to investigate its impact on the detection performance at the receiver. To mitigate the effect of CSIT deterioration at the latter, we propose learning based techniques for hard and soft detection that use downlink precoded pilot symbols as training data. We note that these pilots are originally intended for signal-to-interference-plus-noise ratio (SINR) estimation. We validate the approach by proposing a lightweight implementation that is suitable for online training using several state-of-the-art classifiers. We compare the bit error rate (BER) and the runtime complexity of the proposed approaches where we achieve superior detection performance in harsh channel conditions while maintaining low computational requirements. Specifically, numerical results show that severe CSIT degradation impedes the correct detection when a conventional detector is used. However, the proposed learning-based detectors can achieve good detection performance even under severe CSIT deterioration, and can yield 4-8 dB power gain for BER values lower than 10-4 when compared to the classic linear minimum mean square error (MMSE) detector

MACHINE LEARNING FOR MIMO DETECTION AND EAVESDROPPING WITH SYMBOL-LEVEL PRECODING COUNTERMEASURES

Multiple-input multiple-output (MIMO) technology is an integral part of many current wireless communication systems that can drastically improve the data rates and the spectral efficiency. One major performance limiting factor in MIMO communication is the inter-channel interference (ICI) that adversely affects the transmission's achievable rate, since the receiver has to deal with multiple interfering symbol streams that are transmitted concurrently through a channel subject to random noise and interference. In the case when the channel-state information (CSI) is known at the receiver, i.e., CSIR, it could be used by the latter to compensate for the undesired effects of ICI. Although the problem of symbol detection in MIMO systems -- where the knowledge of CSIR is available -- is a well studied problem with numerous classical detection methods, the complexity of optimal detection methods increase prohibitively in systems with large dimensions, making them impractical for real-time communication. The problem of signal detection in precoded MIMO channels without explicit knowledge of the CSIR is challenging and still being considered in recent research. In particular, this problem is a common occurrence in systems where CSI at the receiver is not available, e.g., time-division duplex (TDD) systems. In this thesis, we investigate the problem of multi-antenna signal detection in the case of a highly distorted received signal due to the ICI effects. The core idea of this thesis is to use pilot data, without explicitly estimating the CSI, to improve the detection performance at the receiver. Motivated by low-complexity signal detection and given the accessibility to pilot data, which form an integral part of communications systems, in this thesis, we propose ML based techniques for MIMO detection in systems where the downlink transmission is precoded using imperfect CSI at the transmitter. Firstly, in the context of a single-user MIMO system, we address the problem of MIMO detection when the received signals are highly distorted, i.e., the case where the signal distortion is caused by signals being precoded with a highly degraded CSI at the transmitter (CSIT). In this setting, we propose ML-based MIMO detectors robust to severe CSIT degradation. The second and third contributions relate to a downlink multi-user multiple-input single-output (MU-MISO) system, for which we propose ML-based detectors that are robust to inaccurate CSIT for uncoded and coded systems, respectively. Herein, the proposed ML detectors are presented as eavesdropping attacks, where, by using the proposed ML detectors, an eavesdropper (Eve) is able to learn the symbol detection function based on precoded pilots and to detect the transmitted symbols, intended for legitimate users, with high accuracy. To counteract these attacks, six symbol-level precoding (SLP)-based countermeasures are proposed with varying security, complexity, and power consumption trade-offs. Numerical results validate the effectiveness of the proposed ML-based detectors and the robustness to the harmful effects of ICI

Downlink Training Overhead Reduction Technique for FDD Massive MIMO Systems

In this letter, a novel minimum mean square error (MSE) based channel estimation framework is proposed to reduce the downlink channel training overhead in frequency division duplexing massive multiple-input multiple-output systems, where the overhead reduction is achieved through training only a subset of antennas and by exploiting the spatial correlation between the antennas at the base station. Closed-form expressions of the analytical MSE and the asymptotic MSE of the system are obtained. Furthermore, a perfect match between theoretical and simulation results is observed, where the channel training overhead can be reduced by half with an acceptable performance.11sciescopu

Machine learning for physical-layer security: Attacks and SLP Countermeasures for Multiantenna Downlink Systems

Most physical-layer security (PLS) work employ information theoretic metrics for performance analysis. In this paper, however, we investigate PLS from a signal processing point of view, where we rely on bit-error rate (BER) at the eavesdropper (Eve) as a metric for information leakage. Meanwhile, recently, symbol-level precoding (SLP) has been shown to provide PLS gains as a new way for security. However, in this work, we introduce a machine learning (ML) based attack, where we show that even SLP schemes can be vulnerable to such attacks. Namely, this attack manifests when an eavesdropper (Eve) utilizes ML in order to learn the precoding pattern when precoded pilots are sent. With this ability, an Eve can decode data with favorable accuracy. As a countermeasure to this attack, we propose a novel precoding design. The proposed countermeasure yields high BER at the Eve, which makes symbol detection practically infeasible for the latter, thus providing physical-layer security between the base station (BS) and the users. In the numerical results, we validate both the attack and the countermeasure, and show that this gain in security can be achieved at the expense of only a small additional power consumption at the transmitter

Learning-Assisted Eavesdropping and Symbol-Level Precoding Countermeasures for Downlink MU-MISO Systems

In this work, we introduce a machine-learning (ML) based detection attack, where an eavesdropper (Eve) is able to learn the symbol detection function based on precoded pilots. With this ability, an Eve can correctly detect symbols with a high probability. To counteract this attack, we propose a novel symbol-level precoding (SLP) scheme that enhances physical-layer security (PLS) while guaranteeing a constructive interference effect at the intended users. Contrary to conventional SLP schemes, the proposed scheme is robust to the ML-based attack. In particular, the proposed scheme enhances security by designing Eve's received signal to lie at the boundaries of the detection regions. This distinct design causes Eve's detection decisions to be based almost purely on noise. The proposed countermeasure is then extended to account for multi-antennas at the Eve and also for multi-level modulation schemes. In the numerical results, we validate both the detection attack and the countermeasures and show that this gain in security can be achieved at the expense of only a small additional power consumption at the transmitter, and more importantly, these benefits are obtained without affecting the performance at the intended user

Multi-Antenna Data-Driven Eavesdropping Attacks and Symbol-Level Precoding Countermeasures

In this work, we consider secure communications in wireless multi-user (MU) multiple-input single-output (MISO) systems with channel coding in the presence of a multi-antenna eavesdropper (Eve), who is a legit user trying to eavesdrop other users. In this setting, we exploit machine learning (ML) tools to design soft and hard decoding schemes by using precoded pilot symbols as training data. The proposed ML frameworks allow an Eve to determine the transmitted message with high accuracy. We thereby show that MU-MISO systems are vulnerable to such eavesdropping attacks even when relatively secure transmission techniques are employed, such as symbol-level precoding (SLP). To counteract this attack, we propose two novel SLP-based schemes that increase the bit-error rate at Eve by impeding the learning process. We design these two security-enhanced schemes to meet different requirements regarding runtime, security, and power consumption. Simulation results validate both the ML-based eavesdropping attacks as well as the countermeasures, and show that the gain in security is achieved without affecting the decoding performance at the intended users

A Novel Learning-based Hard Decoding Scheme and Symbol-Level Precoding Countermeasures

In this work, we consider an eavesdropping scenario in wireless multi-user (MU) multiple-input single-output (MISO) systems with channel coding in the presence of a multi-antenna eavesdropper (Eve). In this setting, we exploit machine learning (ML) tools to design a hard decoding scheme by using precoded pilot symbols as training data. Within this, we propose an ML framework for a multi-antenna hard decoder that allows an Eve to decode the transmitted message with decent accuracy. We show that MU-MISO systems are vulnerable to such an attack when conventional block-level precoding is used. To counteract this attack, we propose a novel symbol-level precoding scheme that increases the bit-error rate at Eve by obstructing the learning process. Simulation results validate both the ML-based attack as well as the countermeasure, and show that the gain in security is achieved without affecting the performance at the intended users