| 1. |
- Tang, Li, et al.
(författare)
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Convolutional LSTM Network with Hierarchical Attention for Relation Classification in Clinical Texts
- 2019
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Ingår i: 2019 international joint conference on neural networks (IJCNN). - : IEEE. - 9781728119854
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Konferensbidrag (refereegranskat)abstract
- Identifying relation from clinical texts is a complex and challenging task due to the specific biomedical knowledge. Existing methods for this work generally have the misclassification problem caused by sample class imbalance. In this paper, we propose a hierarchical attention-based convolutional long short-term memory (ConvLSTM) network model to solve this problem. We construct a sentence as multi-dimensional hierarchical sequence and directly learn local and global context information by a single-layer ConvLSTM network. Besides, a hierarchical attention-based pooling is built to capture the parts of a sentence that are relevant with the target semantic relation. Experiments on the 2010 i2b2/VA relation dataset show that our model outperforms several previous state-of-the-art models without relying on any external features.
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| 2. |
- Zhou, Xuan, et al.
(författare)
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Low Complexity Generalized-LDPC Decoder Based on Trellis Splicing
- 2021
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Ingår i: IEEE Communications Letters. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1089-7798 .- 1558-2558. ; 25:3, s. 830-834
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Tidskriftsartikel (refereegranskat)abstract
- The high reliability short length low-density parity-check (LDPC) codes for Ultra Reliable Low Latency Communication (URLLC) are particularly important to future wireless communication systems. Even though the error floor region performance can be improved by using generalized-LDPC (G-LDPC) decoder, its penalty of increasing complexity can not be ignored. In this letter, we invoke the early stop criterion and the trellis splicing (TS) method to G-LDPC decoder. Hence, a G-LDPC-TS decoder is proposed. The proposed method can effectively reduce the number of variable nodes (VNs) involved in BCJR (Bahl-Cocke-Jelinek-Raviv) decoding of G-LDPC; thus, it yields a lower computational complexity. The results confirm that our G-LDPC-TS decoder can achieve a significant complexity reduction without sacrificing the performance.
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| 3. |
- Chen, Chen, et al.
(författare)
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A V2V Emergent Message Dissemination Scheme for 6G-Oriented Vehicular Networks
- 2023
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Ingår i: Chinese journal of electronics. - : Institute of Electrical and Electronics Engineers (IEEE). - 1022-4653 .- 2075-5597. ; 32:6, s. 1179-1191
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Tidskriftsartikel (refereegranskat)abstract
- To ensure traffic safety and improve traffic efficiency, vehicular networks come up with multiple types of messages for safety and efficiency applications. In sixth-generation (6G) systems, these messages should be timely and error-free disseminated through vehicle-to-vehicle (V2V) communication to ensure traffic safety and efficiency. V2V supports direct communication between two vehicle user equipments, regardless of whether a base station is involved. We propose a packet delivery ratio (PDR)-based message dissemination scheme (PDR-MD) between V2V in 6G-oriented vehicular networks to select relay vehicles when broadcasting emergent messages. This scheme grasps the balance between vehicle distance and PDR so as to reduce transmission delay while ensuring reliable PDR. We compared the PDR-MD scheme with other probabilistic broadcasting schemes. The experimental results show that the PDR-MD protocol can maintain close to 95% and above PDR in transmitting emergent messages, and the transfer rate stays below 40%.
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| 4. |
- Chen, Zemeng, et al.
(författare)
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Open access dataset integrating EEG and fNIRS during Stroop tasks
- 2023
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Ingår i: Scientific Data. - : Springer Nature. - 2052-4463. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Conflict monitoring and processing are crucial components of the human cognitive system, with significant implications for daily life and the diagnosis of cognitive disorders. The Stroop task, combined with brain function detection technology, has been widely employed as a classical paradigm for investigating conflict processing. However, there remains a lack of public datasets that integrate Electroencephalogram (EEG) and functional Near-infrared Spectroscopy (fNIRS) to simultaneously record brain activity during a Stroop task. We introduce a dual-modality Stroop task dataset incorporating 34-channel EEG (sampling frequency is 1000 Hz) and 20-channel high temporal resolution fNIRS (sampling frequency is 100 Hz) measurements covering the whole frontal cerebral cortex from 21 participants (9 females/12 males, aged 23.0 ± 2.3 years). Event-related potential analysis of EEG recordings and activation analysis of fNIRS recordings were performed to show the significant Stroop effect. We expected that the data provided would be utilized to investigate multimodal data processing algorithms during cognitive processing.
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| 5. |
- Huang, Yiqian, et al.
(författare)
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A Novel Maximum Distance Separable Code Based RIS-OFDM : Design and Optimization
- 2022
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Ingår i: 2022 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM 2022). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 1521-1526
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Konferensbidrag (refereegranskat)abstract
- In this paper, we propose a novel maximum distance separable (MDS) code based and reconfigurable intelligent surface (RIS) assisted wireless communication system with orthogonal frequency division multiplexing (OFDM). Specifically, input bits are firstly divided into groups and their MDS codes are utilized to decide the amplitudes and phases of subcarriers. The introduction of the MDS code helps to increase the minimum Hamming distance between symbols and improve on the capability of error detection. Besides, the RIS is adopted to create additional paths between the radio frequency (RF) and the receiver as well as alter the signal phases with derived optimal solution. Benefiting from the strength of the RIS, the proposed system can better overcome multipath fading compared with conventional systems. Simulation results are presented to demonstrate the efficacy of the proposed system in terms of reducing bit error rate (BER) through multipath channels.
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| 6. |
- Lei, Wanlu
(författare)
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A study of wireless communications with reinforcement learning
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The explosive proliferation of mobile users and wireless data traffic in recent years pose imminent challenges upon wireless system design. The trendfor wireless communications becoming more complicated, decentralized andintelligent is inevitable. Lots of key issues in this field are decision-makingrelated problems such as resource allocation, transmission control, intelligentbeam tracking in millimeter Wave (mmWave) systems and so on. Reinforcement learning (RL) was once a languishing field of AI for solving varioussequential decision-making problems. However, it got revived in the late 80sand early 90s when it was connected to dynamic programming (DP). Then,recently RL has progressed in many applications, especially when underliningmodels do not have explicit mathematical solutions and simulations must beused. For instance, the success of RL in AlphaGo and AlphaZero motivatedlots of recent research activities in RL from both academia and industries.Moreover, since computation power has dramatically increased within thelast decade, the methods of simulations and online learning (planning) become feasible for implementations and deployment of RL. Despite of its potentials, the applications of RL to wireless communications are still far frommature. Therefore, it is of great interest to investigate RL-based methodsand algorithms to adapt to different wireless communication scenarios. Morespecifically, this thesis with regards to RL in wireless communications can beroughly divided into the following parts:In the first part of the thesis, we develop a framework based on deepRL (DRL) to solve the spectrum allocation problem in the emerging integrated access and backhaul (IAB) architecture with large scale deploymentand dynamic environment. We propose to use the latest DRL method by integrating an actor-critic spectrum allocation (ACSA) scheme and a deep neuralnetwork (DNN) to achieve real-time spectrum allocation in different scenarios. The proposed methods are evaluated through numerical simulations andshow promising results compared with some baseline allocation policies.In the second part of the thesis, we investigate the decentralized RL algorithms using Alternating direction method of multipliers (ADMM) in applications of Edge IoT. For RL in a decentralized setup, edge nodes (agents)connected through a communication network aim to work collaboratively tofind a policy to optimize the global reward as the sum of local rewards. However, communication costs, scalability and adaptation in complex environments with heterogeneous agents may significantly limit the performance ofdecentralized RL. ADMM has a structure that allows for decentralized implementation and has shown faster convergence than gradient-descent-basedmethods. Therefore, we propose an adaptive stochastic incremental ADMM(asI-ADMM) algorithm and apply the asI-ADMM to decentralized RL withedge computing-empowered IoT networks. We provide convergence properties for proposed algorithms by designing a Lyapunov function and prove thatthe asI-ADMM has O(1=k) + O(1=M) convergence rate where k and M are thenumber of iterations and batch samples, respectively.The third part of the thesis considers the problem of joint beam training and data transmission control of delay-sensitive communications overvimmWave channels. We formulate the problem as a constrained Markov Decision Process (MDP), which aims to minimize the cumulative energy consumption over the whole considered period of time under delay constraints.By introducing a Lagrange multiplier, we reformulate the constrained MDPto an unconstrained one. Then, we solve it using the parallel-rollout-basedRL method in a data-driven manner. Our numerical results demonstrate thatthe optimized policy obtained from parallel rollout significantly outperformsother baseline policies in both energy consumption and delay performance.The final part of the thesis is a further study of the beam tracking problem using supervised learning approach. Due to computation and delay limitation in real deployment, a light-weight algorithm is desired in the beamtracking problem in mmWave networks. We formulate the beam tracking(beam sweeping) problem as a binary-classification problem, and investigatesupervised learning methods for the solution. The methods are tested in bothsimulation scenarios, i.e., ray-tracing model, and real testing data with Ericsson over-the-air (OTA) dataset. It showed that the proposed methods cansignificantly improve cell capacity and reduce overhead consumption whenthe number of UEs increases in the network.
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| 7. |
- Li, Nan, et al.
(författare)
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Diverse Communication Modes in Cooperative Downlink Non-Orthogonal Multiple Access - Invited Paper
- 2018
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Ingår i: Proceedings 87th IEEE Vehicular Technology Conference. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 1-5
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Konferensbidrag (refereegranskat)abstract
- We consider cooperation in downlink non-orthogonal multiple access (NOMA) in a network supporting diverse communication modes. One user (UE2) exists as a potential relay between a base station (BS) and another user (UE1). With relaying the signal for UE1, UE2 obtains the opportunity for its own transmission to UE3 in D2D mode, meanwhile maintaining the transmission efficiency for UE1. On the basis of supporting different communication modes, we propose a NOMA-based cooperation scheme at UE2 to combine the relay message with its own. We derive achievable rate regions for two cases depending on the status of the Rayleigh fading channel of the UE2. We find solutions based on experiments through the transmit power allocation strategy at the UE2 and(/or) the BS. We show the impacts of our cooperative scheme and the corresponding user geometry on the achievable rates and the resource sharing strategies.
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| 8. |
- Li, Nan, et al.
(författare)
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On Resource Allocation of Cooperative Multiple Access Strategy in Energy-Efficient Industrial Internet of Things
- 2021
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Ingår i: IEEE Transactions on Industrial Informatics. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1551-3203 .- 1941-0050. ; 17:2, s. 1069-1078
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Tidskriftsartikel (refereegranskat)abstract
- An event-triggered attitude control algorithm is developed for quadrotor unmanned aerial vehicles (UAVs) subject to external disturbances. In this article, first an event-triggered supertwisting stabilizing control strategy for a class of second-order nonlinear systems is proposed. Then, a Lyapunov-based stability analysis is provided for the closed-loop system, and the Zeno-free execution of triggering sequence is guaranteed via rigorous analysis. Furthermore, the proposed control strategy is applied on attitude control of UAVs to reduce the computing cost without degrading the performance of the system. Finally, the efficiency of the developed method is validated by numerical simulation.
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| 9. |
- Li, Nan, et al.
(författare)
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Optimized Cooperative Multiple Access in Industrial Cognitive Networks
- 2018
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Ingår i: IEEE Transactions on Industrial Informatics. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1551-3203 .- 1941-0050. ; 14:6, s. 2666-2676
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Tidskriftsartikel (refereegranskat)abstract
- We consider optimized cooperation in joint orthogonal multiple access and nonorthogonal multiple access in industrial cognitive networks, in which lots of devices may have to share spectrum and some devices (e.g., those for critical control devices) have higher transmission priority, known as primary users. We consider one secondary transmitter (less important devices) as a potential relay between a primary transmitter and receiver pair. The choice of cooperation scheme differs in terms of use cases. With decode-and-forward relaying, the channel between the primary and secondary users limits the achievable rates especially when it experiences poor channel conditions. To alleviate this problem, we apply analog network coding to directly combine the received primary message for relaying with the secondary message. We find achievable rate regions for these two schemes over Rayleigh fading channels. We then investigate an optimization problem jointly considering orthogonalmultiple access and nonorthogonal multiple access, where the secondary rate is maximized under the constraint of maintaining the primary rate. We find both analytical solutions as well as solutions based on experiments through the time sharing strategy between the primary and secondary system and the transmit power allocation strategy at the secondary transmitter. We show the performance improvements of exploiting analog network coding and the impacts of cooperative schemes and user geometry on achievable rates and resource sharing strategies.
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| 10. |
- Li, Nan, et al.
(författare)
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Performance Analysis of Cognitive User Cooperation Using Binary Network Coding
- 2018
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Ingår i: IEEE Transactions on Vehicular Technology. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9545 .- 1939-9359. ; 67:8, s. 7355-7369
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Tidskriftsartikel (refereegranskat)abstract
- We consider a cognitive radio network where a primary and a secondary transmitter, respectively, communicate a message to their primary and secondary receivers over a packet-based wireless link, using a joint automatic-repeat-request (ARQ) error control scheme. The secondary transmitter assists in the retransmission of the primary message, which improves the primary performance, and as a reward it is granted limited access to the transmission resources. Conventional ARQ, as well as two network-coding schemes are investigated for applications in the retransmission phase; namely the static network-coding scheme and the adaptive network-coding scheme. For each scheme we analyze the transmission process by investigating the distribution of the number of transmission attempts. We divide every frame into three transmission sessions and in each session we discover that the number of transmission attempts follows a certain negative binomial distribution, in which case can be further approximated by a normal distribution. Considering both the cases of an adaptive frame size and a truncated frame size, we derive analytical results on system performances and discuss the comparison of three schemes. Besides, the approximation method greatly reduces the complexity of transmission analysis, especially in the truncated frame-size case. Numerical results show that our analysis are valid and closely match the simulations.
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