Two-Way Training for Discriminatory Channel Estimation in Wireless MIMO Systems

This work examines the use of two-way training to efficiently discriminate the channel estimation performances at a legitimate receiver (LR) and an unauthorized receiver (UR) in a multiple-input multiple-output (MIMO) wireless system. This work improves upon the original discriminatory channel estimation (DCE) scheme proposed by Chang et al where multiple stages of feedback and retraining were used. While most studies on physical layer secrecy are under the information-theoretic framework and focus directly on the data transmission phase, studies on DCE focus on the training phase and aim to provide a practical signal processing technique to discriminate between the channel estimation performances at LR and UR. A key feature of DCE designs is the insertion of artificial noise (AN) in the training signal to degrade the channel estimation performance at UR. To do so, AN must be placed in a carefully chosen subspace based on the transmitter's knowledge of LR's channel in order to minimize its effect on LR. In this paper, we adopt the idea of two-way training that allows both the transmitter and LR to send training signals to facilitate channel estimation at both ends. Both reciprocal and non-reciprocal channels are considered and a two-way DCE scheme is proposed for each scenario. {For mathematical tractability, we assume that all terminals employ the linear minimum mean square error criterion for channel estimation. Based on the mean square error (MSE) of the channel estimates at all terminals,} we formulate and solve an optimization problem where the optimal power allocation between the training signal and AN is found by minimizing the MSE of LR's channel estimate subject to a constraint on the MSE achievable at UR. Numerical results show that the proposed DCE schemes can effectively discriminate between the channel estimation and hence the data detection performances at LR and UR.Comment: 1

Principles of Physical Layer Security in Multiuser Wireless Networks: A Survey

This paper provides a comprehensive review of the domain of physical layer security in multiuser wireless networks. The essential premise of physical-layer security is to enable the exchange of confidential messages over a wireless medium in the presence of unauthorized eavesdroppers without relying on higher-layer encryption. This can be achieved primarily in two ways: without the need for a secret key by intelligently designing transmit coding strategies, or by exploiting the wireless communication medium to develop secret keys over public channels. The survey begins with an overview of the foundations dating back to the pioneering work of Shannon and Wyner on information-theoretic security. We then describe the evolution of secure transmission strategies from point-to-point channels to multiple-antenna systems, followed by generalizations to multiuser broadcast, multiple-access, interference, and relay networks. Secret-key generation and establishment protocols based on physical layer mechanisms are subsequently covered. Approaches for secrecy based on channel coding design are then examined, along with a description of inter-disciplinary approaches based on game theory and stochastic geometry. The associated problem of physical-layer message authentication is also introduced briefly. The survey concludes with observations on potential research directions in this area.Comment: 23 pages, 10 figures, 303 refs. arXiv admin note: text overlap with arXiv:1303.1609 by other authors. IEEE Communications Surveys and Tutorials, 201

Sensory Communication

Contains table of contents for Section 2, an introduction, reports on nine research projects and a list of publications.National Institutes of Health Grant 5 R01 DC00117National Institutes of Health Grant 2 R01 DC00270National Institutes of Health Grant 1 P01 DC00361National Institutes of Health Grant 2 R01 DC00100National Institutes of Health Grant FV00428National Institutes of Health Grant 5 R01 DC00126U.S. Air Force - Office of Scientific Research Grant AFOSR 90-200U.S. Navy - Office of Naval Research Grant N00014-90-J-1935National Institutes of Health Grant 5 R29 DC0062

Sensory Communication

Contains table of contents for Section 2, an introduction, reports on ten research projects and a list of publications.National Institutes of Health Grant 5 R01 DC00117National Institutes of Health Grant 5 R01 DC00270National Institutes of Health Grant 5 P01 DC00361National Institutes of Health Grant 2 R01 DC00100National Institutes of Health Grant 7 R29 DC00428National Institutes of Health Grant 2 R01 DC00126U.S. Air Force - Office of Scientific Research Grant AFOSR 90-0200U.S. Navy - Office of Naval Research Grant N00014-90-J-1935National Institutes of Health Grant 5 R29 DC00625U.S. Navy - Office of Naval Research Grant N00014-91-J-145

Two-way training for discriminatory channel estimation in wireless MIMO systems

This work examines the use of two-way training to efficiently discriminate the channel estimation performances at a legitimate receiver (LR) and an unauthorized receiver (UR) in a multiple-input multiple-output (MIMO) wireless system. This work improves upon the original discriminatory channel estimation (DCE) scheme proposed by Chang where multiple stages of feedback and retraining were used. While most studies on physical layer secrecy are under the information-theoretic framework and focus directly on the data transmission phase, studies on DCE focus on the training phase and aim to provide a practical signal processing technique to discriminate between the channel estimation performances (and, thus, the effective received signal qualities) at LR and UR. A key feature of DCE designs is the insertion of artificial noise (AN) in the training signal to degrade the channel estimation performance at UR. To do so, AN must be placed in a carefully chosen subspace, based on the transmitter's knowledge of LR's channel, in order to minimize its effect on LR. In this paper, we adopt the idea of two-way training that allows both the transmitter and LR to send training signals to facilitate channel estimation at both ends. Both reciprocal and nonreciprocal channels are considered and a two-way DCE scheme is proposed for each scenario. For mathematical tractability, we assume that all terminals employ the linear minimum mean square error criterion for channel estimation. Based on the mean square error (MSE) of the channel estimates at all terminals, we formulate and solve an optimization problem where the optimal power allocation between the training signal and AN is found by minimizing the MSE of LR's channel estimate subject to a constraint on the MSE achievable at UR. Numerical results show that the proposed DCE schemes can effectively discriminate between the channel estimation and, hence, the data detection performances at LR and UR.This work was supported in part by the National Science Council, Taiwan, by Grant NSC 100-2628-E-007-025-MY3 and Grant NSC 101-2218-E-011-043, and in part by the Australian Research Council's Discovery Projects Funding Scheme (Project no.DP110102548)

DeepTx: Deep Learning Beamforming with Channel Prediction

Machine learning algorithms have recently been considered for many tasks in the field of wireless communications. Previously, we have proposed the use of a deep fully convolutional neural network (CNN) for receiver processing and shown it to provide considerable performance gains. In this study, we focus on machine learning algorithms for the transmitter. In particular, we consider beamforming and propose a CNN which, for a given uplink channel estimate as input, outputs downlink channel information to be used for beamforming. The CNN is trained in a supervised manner considering both uplink and downlink transmissions with a loss function that is based on UE receiver performance. The main task of the neural network is to predict the channel evolution between uplink and downlink slots, but it can also learn to handle inefficiencies and errors in the whole chain, including the actual beamforming phase. The provided numerical experiments demonstrate the improved beamforming performance.Comment: 27 pages, this work has been submitted to the IEEE for possible publication; v2: Fixed typo in author name, v3: a revisio

Adaptive and Robust Beam Selection in Millimeter-Wave Massive MIMO Systems

Future 6G wireless communications network will increase the data capacity to unprecedented numbers and thus empower the deployment of new real-time applications. Millimeter-Wave (mmWave) band and Massive MIMO are considered as two of the main pillars of 6G to handle the gigantic influx in data traffic and number of mobile users and IoT devices. The small wavelengths at these frequencies mean that more antenna elements can be placed in the same area. Thereby, high spatial processing gains are achievable that can theoretically compensate for the higher isotropic path loss. The propagation characteristics at mmWave band, create sparse channels in typical scenarios, where only few paths convey significant power. Considering this feature, Hybrid (analog-digital) Beamforming introduces a new signal processing framework which enables energy and cost-efficient implementation of massive MIMO with innovative smart arrays. In this setup, the analog beamalignment via beam selection in link access phase, is the critical performance limiting step. Considering the variable operating condition in mmWave channels, a desirable solution should have the following features: efficiency in training (limited coherence time, delay constraints), adaptivity to channel conditions (large SNR range) and robustness to realized channels (LOS, NLOS, Multipath, non-ideal beam patterns). For the link access task, we present a new energy-detection framework based on variable length channel measurements with (orthogonal) beam codebooks. The proposed beam selection technique denoted as composite M-ary Sequential Competition Test (SCT) solves the beam selection problem when knowledge about the SNR operating point is not available. It adaptively changes the test length when the SNR varies to achieve an essentially constant performance level. In addition, it is robust to non-ideal beam patterns and different types of the realized channel. Compared to the conventional fixed length energy-detection techniques, the SCT can increase the training efficiency up to two times while reducing the delay if the channel condition is good. Having the flexibility to allocate resources for channel measurements through different beams adaptively in time, we improve the SCT to eliminate unpromising beams from the remaining candidate set as soon as possible. In this way, the Sequential Competition and Elimination Test (SCET) significantly further reduces training time by increasing the efficiency. The developed ideas can be applied with different codebook types considered for practical applications. The reliable performance of the beam selection technique is evident through experimental evaluation done using the state-of-the-art test-bed developed at the Vodafone Chair that combines a Universal Software Radio Peripheral (USRP) based platform with mmWave frontends

Domain Generalization in Machine Learning Models for Wireless Communications: Concepts, State-of-the-Art, and Open Issues

Data-driven machine learning (ML) is promoted as one potential technology to be used in next-generations wireless systems. This led to a large body of research work that applies ML techniques to solve problems in different layers of the wireless transmission link. However, most of these applications rely on supervised learning which assumes that the source (training) and target (test) data are independent and identically distributed (i.i.d). This assumption is often violated in the real world due to domain or distribution shifts between the source and the target data. Thus, it is important to ensure that these algorithms generalize to out-of-distribution (OOD) data. In this context, domain generalization (DG) tackles the OOD-related issues by learning models on different and distinct source domains/datasets with generalization capabilities to unseen new domains without additional finetuning. Motivated by the importance of DG requirements for wireless applications, we present a comprehensive overview of the recent developments in DG and the different sources of domain shift. We also summarize the existing DG methods and review their applications in selected wireless communication problems, and conclude with insights and open questions

Communication Biophysics

Contains reports on six research projects.National Institutes of Health (Grant 5 PO1 NS13126)National Institutes of Health (Grant 5 RO1 NS18682)National Institutes of Health (Grant 5 RO1 NS20322)National Institutes of Health (Grant 5 R01 NS20269)National Institutes of Health (Grant 5 T32NS 07047)Symbion, Inc.National Science Foundation (Grant BNS 83-19874)National Science Foundation (Grant BNS 83-19887)National Institutes of Health (Grant 6 RO1 NS 12846)National Institutes of Health (Grant 1 RO1 NS 21322