| 1. |
- Bai, Jianan, et al.
(författare)
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Activity Detection in Distributed MIMO: Distributed AMP via Likelihood Ratio Fusion
- 2022
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Ingår i: IEEE Wireless Communications Letters. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2162-2337 .- 2162-2345. ; 11:10, s. 2200-2204
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Tidskriftsartikel (refereegranskat)abstract
- We develop a new algorithm for activity detection for grant-free multiple access in distributed multiple-input multiple-output (MIMO). The algorithm is a distributed version of the approximate message passing (AMP) based on a soft combination of likelihood ratios computed independently at multiple access points. The underpinning theoretical basis of our algorithm is a new observation that we made about the state evolution in the AMP. Specifically, with a minimum mean-square error denoiser, the state maintains a block-diagonal structure whenever the covariance matrices of the signals have such a structure. We show by numerical examples that the algorithm outperforms competing schemes from the literature.
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| 2. |
- Bai, Jianan, et al.
(författare)
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Multi-agent Policy Optimization for Pilot Selection in Delay-constrained Grant-free Multiple Access
- 2021
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Ingår i: 2021 55th Asilomar Conference on Signals, Systems, and Computers. - : IEEE. - 9781665458283 - 9781665458290 - 9781665458276 ; , s. 1477-1481
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Konferensbidrag (refereegranskat)abstract
- Grant-free multiple access (GFMA) mitigates the uplink handshake overhead to support low-latency communication by transmitting payload data together with the pilot (preamble). However, the channel capacity with random access is limited by the number of available orthogonal pilots and the incoordination among devices. We consider a delay-constrained GFMA system, where each device with randomly generated data traffic needs to deliver its data packets before some pre-determined deadline. The pilot selection problem is formulated to minimize the average packet drop rate of the worst user. A priority-sorting based centralized policy is derived by introducing a fairness promoting function. For decentralization, we propose a multi-agent policy optimization algorithm with improved sample efficiency by exploring the model structure. Simulation results show that our proposed scheme facilitates near-optimal coordination between devices by using only partial state information.
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| 3. |
- Bai, Jianan, et al.
(författare)
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Multiagent Reinforcement Learning Meets Random Access in Massive Cellular Internet of Things
- 2021
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Ingår i: IEEE Internet of Things Journal. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2327-4662. ; 8:24, s. 17417-17428
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Tidskriftsartikel (refereegranskat)abstract
- Internet of Things (IoT) has attracted considerable attention in recent years due to its potential of interconnecting a large number of heterogeneous wireless devices. However, it is usually challenging to provide reliable and efficient random access control when massive IoT devices are trying to access the network simultaneously. In this article, we investigate methods to introduce intelligent random access management for a massive cellular IoT network to reduce access latency and access failures. Toward this end, we introduce two novel frameworks, namely, local device selection (LDS) and intelligent preamble selection (IPS). LDS enables local communication between neighboring devices to provide cluster-wide cooperative congestion control, which leads to a better distribution of the access intensity under bursty traffics. Taking advantage of the capability of reinforcement learning in developing cooperative multiagent policies, IPS is introduced to enable the optimization of the preamble selection policy in each IoT clusters. To handle the exponentially growing action space in IPS, we design a novel reinforcement learning structure, named branching actor-critic, to ensure that the output size of the underlying neural networks only grows linearly with the number of action dimensions. Simulation results indicate that the introduced mechanism achieves much lower access delays with fewer access failures in various realistic scenarios of interests.
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| 4. |
- Liu, Jianan, et al.
(författare)
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Deep Instance Segmentation with Automotive Radar Detection Points
- 2023
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Ingår i: IEEE Transactions on Intelligent Vehicles. - 2379-8858. ; 8:1, s. 84-94
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Tidskriftsartikel (refereegranskat)abstract
- Automotive radar provides reliable environmental perception in all-weather conditions with affordable cost, but it hardly supplies semantic and geometry information due to the sparsity of radar detection points. With the development of automotive radar technologies in recent years, instance segmentation becomes possible by using automotive radar. Its data contain contexts such as radar cross section and micro-Doppler effects, and sometimes can provide detection when the field of view is obscured. The outcome from instance segmentation could be potentially used as the input of trackers for tracking targets. The existing methods often utilize a clustering-based classification framework, which fits the need of real-time processing but has limited performance due to minimum information provided by sparse radar detection points. In this paper, we propose an efficient method based on clustering of estimated semantic information to achieve instance segmentation for the sparse radar detection points. In addition, we show that the performance of the proposed approach can be further enhanced by incorporating the visual multi-layer perceptron. The effectiveness of the proposed method is verified by experimental results on the popular RadarScenes dataset, achieving 89.53% mean coverage and 86.97% mean average precision with the IoU threshold of 0.5, which is superior to other approaches in the literature. More significantly, the consumed memory is around 1MB, and the inference time is less than 40ms, indicating that our proposed algorithm is storage and time efficient. These two criteria ensure the practicality of the proposed method in real-world systems.
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| 5. |
- Liu, Jianan, et al.
(författare)
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GNN-PMB: A Simple but Effective Online 3D Multi-Object Tracker without Bells and Whistles
- 2023
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Ingår i: IEEE Transactions on Intelligent Vehicles. - 2379-8858. ; 8:2, s. 1176-1189
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Tidskriftsartikel (refereegranskat)abstract
- Multi-object tracking (MOT) is among crucial applications in modern advanced driver assistance systems (ADAS) and autonomous driving (AD) systems. The global nearest neighbor (GNN) filter, as the earliest random vector-based Bayesian tracking framework, has been adopted in most of state-of-the-arts trackers in the automotive industry. The development of random finite set (RFS) theory facilitates a mathematically rigorous treatment of the MOT problem, and different variants of RFS-based Bayesian filters have then been proposed. However, their effectiveness in the real ADAS and AD application is still an open problem. In this paper, it is demonstrated that the latest RFS-based Bayesian tracking framework could be superior to typical random vector-based Bayesian tracking framework via a systematic comparative study of both traditional random vector-based Bayesian filters with rule-based heuristic track maintenance and RFS-based Bayesian filters on the nuScenes validation dataset. An RFS-based tracker, namely Poisson multi-Bernoulli filter using the global nearest neighbor (GNN-PMB), is proposed to LiDAR-based MOT tasks. This GNN-PMB tracker is simple to use, and it achieves competitive results on the nuScenes dataset. Specifically, the proposed GNN-PMB tracker outperforms most state-of-the-art LiDAR-only trackers and LiDAR and camera fusion-based trackers, ranking the $3^{rd}$ among all LiDAR-only trackers on nuScenes 3D tracking challenge leader board 1 1 https://bit.ly/3bQJ2CP at the time of submission. Our code is available at https://github.com/chisyliu/GnnPmbTracker .
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