| 1. |
- Chen, Hui, 1992, et al.
(författare)
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MCRB-based Performance Analysis of 6G Localization under Hardware Impairments
- 2022
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Ingår i: 2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022. ; , s. 115-120
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Konferensbidrag (refereegranskat)abstract
- Location information is expected to be the key to meeting the needs of communication and context-aware services in 6G systems. User localization is achieved based on delay and/or angle estimation using uplink or downlink pilot signals. However, hardware impairments (HWIs) distort the signals at both the transmitter and receiver sides and thus affect the localization performance. While this impact can be ignored at lower frequencies where HWIs are less severe, modeling and analysis efforts are needed for 6G to evaluate the localization degradation due to HWIs. In this work, we model various types of impairments and conduct a misspecified Cramér-Rao bound analysis to evaluate the HWI-induced performance loss. Simulation results with different types of HWIs show that each HWI leads to a different level of degradation in angle and delay estimation performance.
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| 2. |
- Ge, Yu, 1995, et al.
(författare)
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Iterated Posterior Linearization PMB Filter for 5G SLAM
- 2022
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Ingår i: IEEE International Conference on Communications. - 1550-3607. ; 2022-May, s. 877-882
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Konferensbidrag (refereegranskat)abstract
- 5G millimeter wave (mmWave) signals have inherent geometric connections to the propagation channel and the propagation environment. Thus, they can be used to jointly localize the receiver and map the propagation environment, which is termed as simultaneous localization and mapping (SLAM). One of the most important tasks in the 5G SLAM is to deal with the nonlinearity of the measurement model. To solve this problem, existing 5G SLAM approaches rely on sigma-point or extended Kalman filters, linearizing the measurement function with respect to the prior probability density function (PDF). In this paper, we study the linearization of the measurement function with respect to the posterior PDF, and implement the iterated posterior linearization filter into the Poisson multi-Bernoulli SLAM filter. Simulation results demonstrate the accuracy and precision improvements of the resulting SLAM filter.
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| 3. |
- Gustavsson, Ulf, 1975, et al.
(författare)
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Efficient amplifer operation
- 2023
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Patent (övrigt vetenskapligt/konstnärligt)abstract
- Efficient amplifier operation. In one aspect, there is a radio transceiver device. The radio transceiver device includes a distorting unit configured to receive an input signal and distort the received input signal, thereby producing a distorted input signal. The radio transceiver device further includes a limiter configured to receive the distorted input signal and produce a limited signal based on the received distorted input signal. The radio transceiver device further includes a power amplifier configured to receive the limited signal and amplify the limited signal, thereby producing an amplified limited signal.
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| 4. |
- Mateos Ramos, José Miguel, 1998, et al.
(författare)
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End-to-End Learning for Integrated Sensing and Communication
- 2022
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Ingår i: IEEE International Conference on Communications. - 1550-3607. ; 2022-May, s. 1942-1947
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Konferensbidrag (refereegranskat)abstract
- Integrated sensing and communication (ISAC) aims to unify radar and communication systems through a combination of joint hardware, joint waveforms, joint signal design, and joint signal processing. At high carrier frequencies, where ISAC is expected to play a major role, joint designs are challenging due to several hardware limitations. Model-based approaches, while powerful and flexible, are inherently limited by how well the models represent reality. Under model deficit, data-driven methods can provide robust ISAC performance. We present a novel approach for data-driven ISAC using an auto-encoder (AE) structure. The approach includes the proposal of the AE architecture, a novel ISAC loss function, and the training procedure. Numerical results demonstrate the power of the proposed AE, in particular under hardware impairments.
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| 5. |
- Rivetti, Steven, 1998, et al.
(författare)
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Spatial Signal Design for Positioning via End-to-End Learning
- 2023
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Ingår i: IEEE Wireless Communications Letters. - 2162-2345 .- 2162-2337. ; 12:3, s. 525-529
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 6. |
- Wu, Yibo, 1996, et al.
(författare)
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Cooperative localization with angular measurements and posterior linearization
- 2020
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Ingår i: 2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Proceedings.
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Konferensbidrag (refereegranskat)abstract
- The application of cooperative localization in vehicular networks is attractive to improve accuracy and coverage of the positioning. Conventional distance measurements between vehicles are limited by the need for synchronization and provide no heading information of the vehicle. To address this, we present a cooperative localization algorithm using posterior linearization belief propagation (PLBP) utilizing angle-of-arrival (AoA)-only measurements. Simulation results show that both directional and positional root mean squared error (RMSE) of vehicles can be decreased significantly and converge to a low value in a few iterations. Furthermore, the influence of parameters for the vehicular network, such as vehicle density, communication radius, prior uncertainty, and AoA measurements noise, is analyzed.
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| 7. |
- Wu, Yibo, 1996, et al.
(författare)
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Frequency-domain digital predistortion for Massive MU-MIMO-OFDM Downlink
- 2022
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Ingår i: GLOBECOM - IEEE Global Telecommunications Conference. ; , s. 579-584
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Konferensbidrag (refereegranskat)abstract
- Digital predistortion (DPD) is a method commonly used to compensate for the nonlinear effects of power amplifiers (PAs). However, the computational complexity of most DPD algorithms becomes an issue in the downlink of massive multi-user (MU) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM), where potentially up to several hundreds of PAs in the base station (BS) require linearization. In this paper, we propose a convolutional neural network (CNN)-based DPD in the frequency domain, taking place before the precoding, where the dimensionality of the signal space depends on the number of users, instead of the number of BS antennas. Simulation results on generalized memory polynomial (GMP)-based PAs show that the proposed CNN-based DPD can lead to very large complexity savings as the number of BS antenna increases at the expense of a small increase in power to achieve the same symbol error rate (SER).
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| 8. |
- Wu, Yibo, 1996, et al.
(författare)
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Impact of Phase Noise on Uplink Cell-Free Massive MIMO OFDM
- 2023
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Ingår i: Proceedings - IEEE Global Communications Conference, GLOBECOM. - 2334-0983 .- 2576-6813. ; , s. 5829-5834
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Konferensbidrag (refereegranskat)abstract
- Cell-Free massive MIMO networks provide huge power gains and resolve inter-cell interference by coherent processing over a massive number of distributed instead of co-located antennas in access points (APs). Cost-efficient hardware is preferred but imperfect local oscillators in both APs and users introduce multiplicative phase noise (PN), which affects the phase coherence between APs and users even with centralized processing. In this paper, we first formulate the system model of a PN-impaired uplink Cell-Free massive MIMO orthogonal frequency division multiplexing network, and then propose a PN-aware linear minimum mean square error channel estimator and derive a PN-impaired uplink spectral efficiency expression. Numerical results are used to quantify the spectral efficiency gain of the proposed channel estimator over alternative schemes for different receiving combiners.
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| 9. |
- Wu, Yibo, 1996, et al.
(författare)
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Low Complexity Joint Impairment Mitigation of I/Q Modulator and PA Using Neural Networks
- 2022
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Ingår i: IEEE Journal on Selected Areas in Communications. - 0733-8716 .- 1558-0008. ; 40:1, s. 54-64
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Tidskriftsartikel (refereegranskat)abstract
- neural networks (NNs) for multiple hardware impairments mitigation of a realistic direct conversion transmitter are impractical due to high computational complexity. We propose two methods to reduce the complexity without significant performance penalty. First, propose a novel NN with shortcut connections, referred to as shortcut real-valued time-delay neural network (SVDEN), where trainable neuron-wise shortcut connections are added between the input and output layers. Second, we implement a NN pruning algorithm that gradually removes connections corresponding to minimal weight magnitudes in each layer. Simulation and experimental results show that SVDEN with pruning achieves better performance for compensating frequency-dependent quadrature imbalance and power amplifier nonlinearity than other NN-based and Volterra-based models, while requiring less or similar complexity.
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| 10. |
- Wu, Yibo, 1996, et al.
(författare)
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Residual Neural Networks for Digital Predistortion
- 2020
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Ingår i: 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings.
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Konferensbidrag (refereegranskat)abstract
- Tracking the nonlinear behavior of an RF power amplifier (PA) is challenging. To tackle this problem, we build a connection between residual learning and the PA nonlinearity, and propose a novel residual neural network structure, referred to as the residual real-valued time-delay neural network (R2TDNN). Instead of learning the whole behavior of the PA, the R2TDNN focuses on learning its nonlinear behavior by adding identity shortcut connections between the input and output layer. In particular, we apply the R2TDNN to digital predistortion and measure experimental results on a real PA. Compared with neural networks recently proposed by Liu et at. and Wang et at., the R2TDNN achieves the best linearization performance in terms of normalized mean square error and adjacent channel power ratio with less or similar computational complexity. Furthermore, the R2TDNN exhibits significantly faster training speed and lower training error.
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| 11. |
- Wu, Yibo, 1996, et al.
(författare)
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Symbol-Based Over-the-Air Digital Predistortion Using Reinforcement Learning
- 2022
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Ingår i: IEEE International Conference on Communications. - 1550-3607. ; 2022-May, s. 2615-2620
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Konferensbidrag (refereegranskat)abstract
- We propose an over-the-air digital predistortion optimization algorithm using reinforcement learning. Based on a symbol-based criterion, the algorithm minimizes the errors between downsampled messages at the receiver side. The algorithm does not require any knowledge about the underlying hardware or channel. For a generalized memory polynomial power amplifier and additive white Gaussian noise channel, we show that the proposed algorithm achieves performance improvements in terms of symbol error rate compared with an indirect learning architecture even when the latter is coupled with a full sampling rate ADC in the feedback path. Furthermore, it maintains a satisfactory adjacent channel power ratio.
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