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PAPER precoder optimization Using Data Correlation for Wireless Data Aggregation

IEICE TRANSACTIONS ON COMMUNICATIONS 2024年第3期E107B卷 330-338页

作者： Nakai-kasai, Ayano Hayashi, Naoyuki Wadayama, Tadashi Nagoya Inst Technol Grad Sch Engn Nagoya 4668555 Japan

In this paper, we consider precoder design for wireless data aggregation in sensor networks. The precoder optimization problem can be formulated as minimization of mean squared error under transmit power and block diagonal constraints. We include statistical correlation of data into the optimization problem, which is appeared in typical applications but is ignored in conventional designing methods. We propose precoder optimization algorithms based on projected gradient descent with projection onto the constraint sets. The proposed method can achieve better performance than the conventional methods that do not incorporate data correlation, especially when data are highly correlated. We also extend the proposed approach to the context of over -the -air computation.

关键词： wireless data aggregation precoder optimization data corre lation projected gradient descent over-the-air computation

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Joint Data and Tag precoder optimization for MIMO Physical Layer Authentication with Embedded Fingerprinting

Joint Data and Tag Precoder Optimization for MIMO Physical L...

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IEEE Conference on Communications and Network Security (CNS)

作者： Chalise, Batu K. Sadler, Brian M. New York Inst Technol Dept Elect & Comp Engn New York NY 10001 USA US Army Res Lab Adelphi MD USA

ISBN: (纸本)9781538671177

We consider precoder optimization for authentication in a wireless multiple-input-multiple-output (MIMO) system that embeds a fingerprint at the physical layer. The optimization relaxes the assumptions that the same precoding matrix will be used for both data and tag and that the intended receiver (Bob) is able to perfectly estimate the data. The sum mean-square error (MSE) estimate of the tag is derived as a function of data and tag precoder matrices, which is then minimized under individual data and tag power constraints. Simulation results show that the proposed precoder optimization provides much better authentication probability than the equal power allocation. Moreover, by tuning the tag power, the desired tradeoff between authentication probability and key leakage (as measured by key equivocation at an eavesdropper) can be achieved with the proposed method.

关键词： Authentication fingerprinting precoder optimization multiple-input-multiple output

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Spatial Signal Design for Positioning via End-to-End Learning

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IEEE WIRELESS COMMUNICATIONS LETTERS 2023年第3期12卷 525-529页

作者： Rivetti, Steven Mateos-Ramos, Jose Miguel Wu, Yibo Song, Jinxiang Keskin, Musa Furkan Yajnanarayana, Vijaya Hager, Christian Wymeersch, Henk Chalmers Univ Technol Dept Elect Engn S-41258 Gothenburg Sweden Ericsson AB Ericsson Res S-41756 Stockholm Sweden Ericsson Res Bengaluru 560048 India

This letter considers the problem of end-to-end (E2E) learning for joint optimization of transmitter precoding and receiver processing for mmWave downlink positioning. Considering a multiple-input single-output (MISO) scenario, we propose a novel autoencoder (AE) architecture to estimate user equipment (UE) position with multiple base stations (BSs) and demonstrate that E2E learning can match model-based design, both for angle-of-departure (AoD) and position estimation, under ideal conditions without model deficits and outperform it in the presence of hardware impairments.

关键词： Estimation Artificial neural networks Signal design MISO communication Benchmark testing Wireless communication Uncertainty mmWave positioning precoder optimization end-to-end learning

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RSMA-based Bistatic ISAC Framework for LEO Satellite Systems 59

RSMA-based Bistatic ISAC Framework for LEO Satellite Systems

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59th Annual IEEE International Conference on Communications (IEEE ICC)

作者： Park, Juha Seong, Jaehyup Ryu, Jaehak Mao, Yijie Shin, Wonjae Korea Univ Sch Elect Engn Seoul South Korea ShanghaiTech Univ Sch Informat Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9798350304060;9798350304053

To provide ubiquitous global connectivity and target detection on the same platform, a low Earth orbit (LEO) satellite system simultaneously performing communication and radar tasks has drawn significant attention. However, the design of such a joint system should address the challenges of limited power resources on satellites and inter-/intra-function interference. In this paper, we put forth a novel LEO satellite-based bistatic integrated sensing and communication (ISAC) framework that can overcome severe echo path loss by introducing a radar receiver separated from the satellite, thus reducing the satellite's power consumption burden. To effectively manage inter-/intra-function interference, which is a critical factor that can deteriorate system performance, we employ rate-splitting multiple access (RSMA). In RSMA, each user's message is split and encoded into individual private and common streams. We jointly optimize power and beamforming design for the dual-functional signal based on the rate-splitting approach. Numerical results show that the proposed framework enables superior interference control capability and significantly reduces echo path loss compared to several benchmarks. Furthermore, it is validated that the common stream serves three key roles: i) forming a beam towards the radar target, ii) controlling interference between radar and communication functions, and iii) managing interference among communication users. By doing so, efficient joint operation is achieved even without a dedicated radar signal, thanks to the multi-functionality of the common streams under limited spectral and hardware resources.

关键词： Integrated sensing and communications (ISAC) low Earth orbit (LEO) satellite rate-splitting multiple access (RSMA) precoder optimization

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Distributed Linear precoder optimization and Base Station Selection for an Uplink Heterogeneous Network

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2013年第12期61卷 3214-3228页

作者： Hong, Mingyi Luo, Zhi-Quan Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA

In a heterogeneous wireless cellular network, each user may be covered by multiple access points such as macro/pico/relay/femto base stations (BS). An effective approach to maximize the sum utility (e. g., system throughput) in such a network is to jointly optimize users' linear procoders as well as their BS associations. In this paper, we first show that this joint optimization problem is NP-hard and thus is difficult to solve to global optimality. To find a locally optimal solution, we formulate the problem as a noncooperative game in which the users and the BSs both act as players. We introduce a set of new utility functions for the players and show that every Nash equilibrium (NE) of the resulting game is a stationary solution of the original sum utility maximization problem. Moreover, we develop a best-response type algorithm that allows the players to distributedly reach a NE of the game. Simulation results show that the proposed distributed algorithm can effectively relieve local BS congestion and simultaneously achieve high throughput and load balancing in a heterogeneous network.

关键词： Base station selection heterogeneous networks Nash equilibrium noncooperative gams precoder optimization

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Distributed Joint Multi-cell optimization of IRS Parameters with Linear precoders

Distributed Joint Multi-cell Optimization of IRS Parameters ...

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IEEE International Conference on Communications (ICC)

作者： Wiesmayr, R. Honig, M. Joham, M. Utschick, W. Tech Univ Munich Dept ECE Munich Germany Northwestern Univ Dept ECE Evanston IL USA

ISBN: (数字)9781538683477

ISBN: (纸本)9781538683477

We present distributed methods for jointly optimizing Intelligent Reflecting Surface (IRS) phase-shifts and beamformers in a cellular network. The proposed schemes require knowledge of only the intra-cell training sequences and corresponding received signals without explicit channel estimation. Instead, an achievable sum-rate objective is estimated via sample means and maximized directly. This automatically includes and mitigates both intra- and inter-cell interference provided that the uplink training is synchronized across cells. Different schemes are considered that limit the set of known training sequences from interferers. With MIMO links an iterative synchronous bi-directional training scheme jointly optimizes the IRS parameters with the beamformers and combiners. Simulation results show that the proposed distributed methods show a modest performance degradation compared to centralized channel estimation schemes, which estimate all channels including all cross-channels, and perform significantly better than decentralized channel estimation schemes which ignore the inter-cell interference.

关键词： Intelligent reflecting surfaces channel estimation MIMO precoder optimization

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Bi-Directional Training Methods With Frequency-Division Duplexing

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2021年第10期20卷 6493-6505页

作者： Zhou, Hao Honig, Michael L. Liu, Jialing Xiao, Weimin Northwestern Univ Dept Elect & Comp Engn Evanston IL 60208 USA Amazon Web Serv Seattle WA 5210 USA Futurewei Wireless Res & Stand Rolling Meadows IL 60008 USA

We study distributed algorithms for joint adaptation of precoding and combining filters in frequency division duplex (FDD) multiple-input multiple-output (MIMO) cellular systems. Our approach extends bi-directional training (BiT), designed for time division duplex (TDD) systems, to FDD systems where uplink/downlink reciprocity may not apply. An analysis of the performance loss due to different uplink-downlink frequencies in a point-to-point scenario, shows that a direct application of BiT gives mismatched precoders with substantial performance degradation. We first propose an algorithm assuming each base transceiver station (BTS) knows the uplink and downlink channels of mobiles within the cell. We then consider the scenario where neither the BTSs nor the mobiles have a priori channel state information. Our proposed approaches assume angular reciprocity characterized by angles of arrival/departure that vary predictably with frequency. Hence spatial beams corresponding to angles of arrival can be turned around to the corresponding angles of departure in the paired band. We present three methods, differing in how the angular decomposition is applied and used, namely, to reconstruct the combiner directly, or to reconstruct the received signal. Simulation results indicate that when the multipath is sufficiently sparse, most of achievable gain with channel reciprocity and TDD can be recovered.

关键词： MIMO FDD adaptive beamforming precoder optimization distributed optimization

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Bi-Directional Training for Adaptive Beamforming and Power Control in Interference Networks

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2014年第3期62卷 607-618页

作者： Shi, Changxin Berry, Randall A. Honig, Michael L. Northwestern Univ Dept Elect Engn & Comp Sci Evanston IL 60208 USA

We study distributed algorithms for adapting transmit beamformers and linear receiver filters in a Time-Division Duplex Multiple-Input Multiple-Output (MIMO) interference network. Each transmitter transmits a single beam, and neither the transmitters nor receivers have a priori Channel State Information (CSI). Given a fixed set of powers, we present an adaptive version of the Max-SINR algorithm: pilot symbols are alternately transmitted in the forward direction (transmitters to receivers) and in the reverse direction (receivers to transmitters). Unlike previous channel estimation schemes, transmissions in each direction are synchronized across the source or destination nodes, and the pilots are used to update the filters/beams directly using a least squares criterion. To improve the performance with limited training, we include exponential weighting of the least squares objective across data frames. In addition, bi-directional training can be used to implement analog interference pricing for power control: training in the forward direction is used to measure received signal-to-interference plus noise ratios (SINRs) and interference prices, and those estimates combined with synchronous backward training are used to update the powers. Given sufficient training this method achieves the same performance as interference pricing updates with perfect CSI. Numerical results are presented that illustrate the performance of these methods in different settings.

关键词： MIMO adaptive beamforming precoder optimization interference networks

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Optimal Joint Base Station Assignment and Beamforming for Heterogeneous Networks

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2014年第8期62卷 1950-1961页

作者： Sanjabi, Maziar Razaviyayn, Meisam Luo, Zhi-Quan Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA

Consider a downlink MIMO heterogeneous wireless network with multiple cells, each containing many mobile users and a number of base stations with varying capabilities. A central task in the management of such a network is to assign each user to a base station and design a linear transmit strategy to ensure a satisfactory level of network performance. In this work, we propose a formulation for the joint base station assignment and linear transceiver design problem based on the maximization of a system wide utility. We first establish the NP-hardness of the resulting optimization problem for a large family of a-fairness utility functions. Then, we propose an efficient algorithm to approximately solve this problem for a variety of different utility functions. Our numerical experiments show that the proposed algorithm can achieve significantly higher levels of system throughput and user fairness than what is possible by optimizing the precoders alone.

关键词： Base station assignment heterogeneous networks precoder optimization resource management NP-harness

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Comparison of Distributed Beamforming Algorithms for MIMO Interference Networks

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2013年第13期61卷 3476-3489页

作者： Schmidt, David A. Shi, Changxin Berry, Randall A. Honig, Michael L. Utschick, Wolfgang Tech Univ Munich Associate Inst Signal Proc D-80333 Munich Germany Northwestern Univ Dept Elect Engn & Comp Sci Evanston IL 60208 USA

This paper presents a comparative study of algorithms for jointly optimizing beamformers and receive filters in an interference network, where each node may have multiple antennas, each user transmits at most one data stream, and interference is treated as noise. We focus on techniques that seek good suboptimal solutions by means of iterative and distributed updates. Those include forward-backward iterative algorithms (max-signal-to-interference-plus-noise ratio (SINR) and interference leakage), weighted sum mean-squared error (MSE) algorithms, and interference pricing with incremental signal-to-noise ratio (SNR) adjustments. We compare their properties in terms of convergence and information exchange requirements, and then numerically evaluate their sum rate performance averaged over random (stationary) channel realizations. The numerical results show that the max-SINR algorithm achieves the maximum degrees of freedom (i.e., supports the maximum number of users with near-zero interference) and exhibits better convergence behavior at high SNRs than the weighted sum MSE algorithms. However, it assumes fixed power per user and achieves only a single point in the rate region whereas the weighted sum MSE criterion gives different points. In contrast, the incremental SNR algorithm adjusts the beam powers and deactivates users when interference alignment is infeasible. Furthermore, that algorithm can provide a slight increase in sum rate, relative to max-SINR, at the cost of additional iterations.

关键词： MIMO interference channel interference alignment beamforming sum-rate maximization precoder optimization

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