Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118705
Sota Uchimura, G. Abreu, K. Ishibashi
We propose a hybrid beamforming for coordinated multi-point (CoMP) transmission using orthogonal frequency division multiplexing (OFDM) over millimeter-wave (mmWave) channels to combat random propagation path blockages. In particular, a sum-of-outage-probability minimization problem with manifold constraints is formulated, which designs the hybrid beamformers, the data rate allocation, and the power allocation over subcarriers jointly to meet the prescribed data rate requirement. A new block stochastic learning mechanism exploiting prior knowledge of the path blockages is also introduced to solve the problem efficiently. Numerical results confirm the effectiveness of the proposed approach in minimizing the outage probability of users according to their target rate. Furthermore, these results also show that the proposed hybrid CoMP transmission only with a few radio frequency (RF) chains and knowledge of blockage probabilities achieves comparable outage performance to a fully digital CoMP transmission alternative with perfect knowledge of instantaneous path blockages.
{"title":"Hybrid Beamforming for Outage-Minimization in Frequency Selective Millimeter-Wave Channels","authors":"Sota Uchimura, G. Abreu, K. Ishibashi","doi":"10.1109/WCNC55385.2023.10118705","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118705","url":null,"abstract":"We propose a hybrid beamforming for coordinated multi-point (CoMP) transmission using orthogonal frequency division multiplexing (OFDM) over millimeter-wave (mmWave) channels to combat random propagation path blockages. In particular, a sum-of-outage-probability minimization problem with manifold constraints is formulated, which designs the hybrid beamformers, the data rate allocation, and the power allocation over subcarriers jointly to meet the prescribed data rate requirement. A new block stochastic learning mechanism exploiting prior knowledge of the path blockages is also introduced to solve the problem efficiently. Numerical results confirm the effectiveness of the proposed approach in minimizing the outage probability of users according to their target rate. Furthermore, these results also show that the proposed hybrid CoMP transmission only with a few radio frequency (RF) chains and knowledge of blockage probabilities achieves comparable outage performance to a fully digital CoMP transmission alternative with perfect knowledge of instantaneous path blockages.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127763070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118771
Zhiyu Huang, Zhichao Sheng, A. Nasir, Cheng Yin, Antonino Masaracchia
This paper investigates the unmanned aerial vehicle (UAV)-assisted communication network with multiple downlink users (DLUs) and uplink users (ULUs) in the presence of multiple malicious jammers. To guarantee fairness among the users and their uplink and downlink communication throughput, we aim to maximize the minimum average throughput by jointly optimizing the scheduling of ULUs/DLUs, three dimensional (3D) trajectory and the UAV transmission power. Although the optimization problem is computationally intractable due to its non-convexity, we develop an iterative algorithm based on the block coordinate descend approach and the successive convex approximation technique to solve the problem efficiently. Numerical outcomes show that our proposed algorithm can improve throughput significantly over several benchmark schemes.
{"title":"UAV-Assisted Downlink-and-Uplink Communication in the Presence of Multiple Malicious Jammers","authors":"Zhiyu Huang, Zhichao Sheng, A. Nasir, Cheng Yin, Antonino Masaracchia","doi":"10.1109/WCNC55385.2023.10118771","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118771","url":null,"abstract":"This paper investigates the unmanned aerial vehicle (UAV)-assisted communication network with multiple downlink users (DLUs) and uplink users (ULUs) in the presence of multiple malicious jammers. To guarantee fairness among the users and their uplink and downlink communication throughput, we aim to maximize the minimum average throughput by jointly optimizing the scheduling of ULUs/DLUs, three dimensional (3D) trajectory and the UAV transmission power. Although the optimization problem is computationally intractable due to its non-convexity, we develop an iterative algorithm based on the block coordinate descend approach and the successive convex approximation technique to solve the problem efficiently. Numerical outcomes show that our proposed algorithm can improve throughput significantly over several benchmark schemes.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134053420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118892
Yuzhou Shang, Zhaoyang Zhang, Zhaohui Yang
Polar code is envisioned as a promising candidate for ultra-reliable low-latency communications (URLLC) in fifth-generation (5G) communication and beyond. To decode polar code, a successive cancellation list (SCL) decoder with a large list size can provide near maximum likelihood (ML) decoding performance. However, a large list size will lead to unacceptable spatial complexity, making it impractical. When the list size is small, although the complexity is low, its performance still needs to be improved. The main reason is that the sequence features implied in log-likelihood ratio (LLR) sequences are lost during calculating path metrics used for path selection. Because of the excellent sequence feature extraction ability of the long short-term memory (LSTM) network, we propose an LSTM-based path selection mechanism to replace the path metric-based path selection mechanism in SCL. In our proposed scheme, the LSTM network selects the surviving path according to the LLR sequences corresponding to the current paths. Simulation results show the effectiveness of the proposed LSTM-based path selection mechanism.
{"title":"LSTM-based Path Selection for Successive Cancellation List Decoding for Short Polar Codes","authors":"Yuzhou Shang, Zhaoyang Zhang, Zhaohui Yang","doi":"10.1109/WCNC55385.2023.10118892","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118892","url":null,"abstract":"Polar code is envisioned as a promising candidate for ultra-reliable low-latency communications (URLLC) in fifth-generation (5G) communication and beyond. To decode polar code, a successive cancellation list (SCL) decoder with a large list size can provide near maximum likelihood (ML) decoding performance. However, a large list size will lead to unacceptable spatial complexity, making it impractical. When the list size is small, although the complexity is low, its performance still needs to be improved. The main reason is that the sequence features implied in log-likelihood ratio (LLR) sequences are lost during calculating path metrics used for path selection. Because of the excellent sequence feature extraction ability of the long short-term memory (LSTM) network, we propose an LSTM-based path selection mechanism to replace the path metric-based path selection mechanism in SCL. In our proposed scheme, the LSTM network selects the surviving path according to the LLR sequences corresponding to the current paths. Simulation results show the effectiveness of the proposed LSTM-based path selection mechanism.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"153 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134345497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118714
Sara Norouzi, B. Champagne
As an enabling technology for emerging and future generations of wireless networks, sparse code multiple access (SCMA) offers major improvements in terms of spectral efficiency and massive connectivity. Although the message passing algorithm (MPA) for SCMA decoding at the receiver side can achieve near optimum performance, it entails high computational complexity. In this paper, to address this issue, we propose a novel SCMA decoder based on deep residual neural network (ResNet), wherein the decoder is trained to predict the transmit codewords. In our approach, residual blocks are employed to tackle the problems of accuracy saturation and vanishing gradients with deep learning based decoder, while batch normalization is utilized to enhance the stability and robustness of the decoder. The performance of the proposed ResNet decoder for SCMA is validated by means of simulations over AWGN and Rayleigh fading channels. The results show that besides a much reduced complexity, the proposed decoder leads to improvements in term of bit error rate (BER) over competing deep neural network (DNN) based decoders.
{"title":"Deep Residual Neural Network Decoder for Sparse Code Multiple Access","authors":"Sara Norouzi, B. Champagne","doi":"10.1109/WCNC55385.2023.10118714","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118714","url":null,"abstract":"As an enabling technology for emerging and future generations of wireless networks, sparse code multiple access (SCMA) offers major improvements in terms of spectral efficiency and massive connectivity. Although the message passing algorithm (MPA) for SCMA decoding at the receiver side can achieve near optimum performance, it entails high computational complexity. In this paper, to address this issue, we propose a novel SCMA decoder based on deep residual neural network (ResNet), wherein the decoder is trained to predict the transmit codewords. In our approach, residual blocks are employed to tackle the problems of accuracy saturation and vanishing gradients with deep learning based decoder, while batch normalization is utilized to enhance the stability and robustness of the decoder. The performance of the proposed ResNet decoder for SCMA is validated by means of simulations over AWGN and Rayleigh fading channels. The results show that besides a much reduced complexity, the proposed decoder leads to improvements in term of bit error rate (BER) over competing deep neural network (DNN) based decoders.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116755553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118776
Arash Sahbafard, R. Schmidt, F. Kaltenberger, A. Springer, Hans-Peter Bernhard
5G, the latest generation of cellular technology, targets not only enhanced data rates but also new applications which require, e.g., ultra-reliable low latency communication. Verticals like industrial automation or automotive, which want to make use of this type of wireless services, need experimental deployments to test the performance of 5G in various modes and environments for their use cases. Due to the ongoing stan-dardization process, experimental 5G networks based on open-source frameworks are especially well suited, as they provide the possibility to easily implement new features introduced by the yearly 5G standard releases. We thus present an experimental 5G standalone deployment, based on the OpenAirInterface, which is an open-source framework, that is being used both, commercially and for academic purposes. We evaluate coverage parameters including reference signal received power, reference signal received quality, and signal to interference and noise ratio both for single user and multiple user scenarios. The measured downlink data rate reaches up to 390 Mbps at a bandwidth of 60 MHz, which is close to the achievable theoretical value. The average latency both for uplink and downlink was measured to be 19 ms for the round trip time, while the minimum latency value was 6 ms, which is acceptable for many application.
{"title":"On the Performance of an Indoor Open-Source 5G Standalone Deployment","authors":"Arash Sahbafard, R. Schmidt, F. Kaltenberger, A. Springer, Hans-Peter Bernhard","doi":"10.1109/WCNC55385.2023.10118776","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118776","url":null,"abstract":"5G, the latest generation of cellular technology, targets not only enhanced data rates but also new applications which require, e.g., ultra-reliable low latency communication. Verticals like industrial automation or automotive, which want to make use of this type of wireless services, need experimental deployments to test the performance of 5G in various modes and environments for their use cases. Due to the ongoing stan-dardization process, experimental 5G networks based on open-source frameworks are especially well suited, as they provide the possibility to easily implement new features introduced by the yearly 5G standard releases. We thus present an experimental 5G standalone deployment, based on the OpenAirInterface, which is an open-source framework, that is being used both, commercially and for academic purposes. We evaluate coverage parameters including reference signal received power, reference signal received quality, and signal to interference and noise ratio both for single user and multiple user scenarios. The measured downlink data rate reaches up to 390 Mbps at a bandwidth of 60 MHz, which is close to the achievable theoretical value. The average latency both for uplink and downlink was measured to be 19 ms for the round trip time, while the minimum latency value was 6 ms, which is acceptable for many application.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115784436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118859
Xiao Zhang, Tao Peng, Yichen Guo, Wenbo Wang
The fifth-generation (5G) wireless network is expected to support emerging applications requiring ultra high reliability and low latency, such as self-driving cars, factory automation (industry 4.0), and smart grid, known as ultra-reliable and low-latency communications (URLLC). Uplink grant-free (GF) transmission is considered as a promising technology for supporting the rigorous requirements of URLLC by saving the time of requesting/waiting for the scheduling grant and supporting the K-repetition transmission. Besides, the intercell interference (ICI) in uplink multi-cell GF transmission is another critical issue to be solved. In this paper, we propose an interference-aware based radio resource configuration framework of URLLC uplink GF transmission which means that we can configure the radio resources by utilizing the available interference information to mitigate the impact of severe ICI on the transmission performance in URLLC. Numerical results show that, the proposed scheme can greatly improve the total transmission reliability and has higher scalability and robustness compared to prior art solutions under the condition of satisfying the transmission delay requirement and resource constraint.
{"title":"Interference-Aware Based Resource Configuration Optimization for URLLC Grant-Free Transmission","authors":"Xiao Zhang, Tao Peng, Yichen Guo, Wenbo Wang","doi":"10.1109/WCNC55385.2023.10118859","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118859","url":null,"abstract":"The fifth-generation (5G) wireless network is expected to support emerging applications requiring ultra high reliability and low latency, such as self-driving cars, factory automation (industry 4.0), and smart grid, known as ultra-reliable and low-latency communications (URLLC). Uplink grant-free (GF) transmission is considered as a promising technology for supporting the rigorous requirements of URLLC by saving the time of requesting/waiting for the scheduling grant and supporting the K-repetition transmission. Besides, the intercell interference (ICI) in uplink multi-cell GF transmission is another critical issue to be solved. In this paper, we propose an interference-aware based radio resource configuration framework of URLLC uplink GF transmission which means that we can configure the radio resources by utilizing the available interference information to mitigate the impact of severe ICI on the transmission performance in URLLC. Numerical results show that, the proposed scheme can greatly improve the total transmission reliability and has higher scalability and robustness compared to prior art solutions under the condition of satisfying the transmission delay requirement and resource constraint.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123434826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118853
S.I. Hassouna, M. Jamshed, M. Rehman, M. Imran, Q. Abbasi
In wireless communications, various study findings have shown that a reconfigurable intelligent surface (RIS) may successfully alter wireless wave parameters like phase and amplitude without requiring sophisticated signal processing and decoding at the receiver. However, it is necessary to take into account designing the surface under a realistic frequency selective fading channel. Because of this, we chose a wideband OFDM multi-user communication system based on an actual RIS setup that considers mutual coupling (MC) and electromagnetic interference (EMI). We used Hadamard matrix in the pilot transmissions to estimate the uncontrollable and the controllable channels. The best pilot configuration was selected to initialize the gradient descent method in order to calculate the optimal reflection coefficient that maximize the data rate for each user in the presence of EMI and MC. Simulation results revealed that the data rate has been degraded when considering EMI and MC for around 30 Mbits/s for each user. This confirms that both EMI and MC must be given considerable attention in our research due to their inevitable effects on the system performance.
{"title":"Investigating the Data Rate of Intelligent Reflecting Surfaces with Mutual Coupling and EMI","authors":"S.I. Hassouna, M. Jamshed, M. Rehman, M. Imran, Q. Abbasi","doi":"10.1109/WCNC55385.2023.10118853","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118853","url":null,"abstract":"In wireless communications, various study findings have shown that a reconfigurable intelligent surface (RIS) may successfully alter wireless wave parameters like phase and amplitude without requiring sophisticated signal processing and decoding at the receiver. However, it is necessary to take into account designing the surface under a realistic frequency selective fading channel. Because of this, we chose a wideband OFDM multi-user communication system based on an actual RIS setup that considers mutual coupling (MC) and electromagnetic interference (EMI). We used Hadamard matrix in the pilot transmissions to estimate the uncontrollable and the controllable channels. The best pilot configuration was selected to initialize the gradient descent method in order to calculate the optimal reflection coefficient that maximize the data rate for each user in the presence of EMI and MC. Simulation results revealed that the data rate has been degraded when considering EMI and MC for around 30 Mbits/s for each user. This confirms that both EMI and MC must be given considerable attention in our research due to their inevitable effects on the system performance.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124448146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118961
Sravani Kurma, Keshav Singh, P. Sharma, Chih-Peng Li
Reconfigurable intelligent surface (RIS) is a break-through technology that enhances both energy efficiency (EE) and spectrum efficiency (SE) by artificial reconfiguration of the electromagnetic waves utilizing the reflective property of the metasurface elements. This work studies the optimization of the SE-EE trade-off using the deep reinforcement learning (DRL) algorithm in a RIS-assisted full-duplex multi-user multiple-input multiple-output (MIMO) communication system. We use partial channel state information to control the overhead signaling requirement and demand for energy supply to the system. We consider resource efficiency (RE), in which the RIS’s phase-shift design and power allocation at the nodes (i.e., node in BS in downlink (DL) and user in uplink (UL)) are jointly optimized, with the goal of investigating the SE-EE trade-off of the considered system using an appropriate performance metric. We adopt a DRL-based approach for the proposed system to tackle the challenges involved in optimization due to time-varying channels and exploitation in real-time applications. Additionally, simulation outcomes exemplify the efficiency and swift conver-gence rate of the proposed algorithm and demonstrate how different system characteristics, including co-channel interference (CCI), residual self-interference (RSI), and the number of RIS reflecting elements, affect the system’s performance.
{"title":"DRL Approach for Spectral-Energy Trade-off in RIS-assisted Full-duplex Multi-user MIMO Systems","authors":"Sravani Kurma, Keshav Singh, P. Sharma, Chih-Peng Li","doi":"10.1109/WCNC55385.2023.10118961","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118961","url":null,"abstract":"Reconfigurable intelligent surface (RIS) is a break-through technology that enhances both energy efficiency (EE) and spectrum efficiency (SE) by artificial reconfiguration of the electromagnetic waves utilizing the reflective property of the metasurface elements. This work studies the optimization of the SE-EE trade-off using the deep reinforcement learning (DRL) algorithm in a RIS-assisted full-duplex multi-user multiple-input multiple-output (MIMO) communication system. We use partial channel state information to control the overhead signaling requirement and demand for energy supply to the system. We consider resource efficiency (RE), in which the RIS’s phase-shift design and power allocation at the nodes (i.e., node in BS in downlink (DL) and user in uplink (UL)) are jointly optimized, with the goal of investigating the SE-EE trade-off of the considered system using an appropriate performance metric. We adopt a DRL-based approach for the proposed system to tackle the challenges involved in optimization due to time-varying channels and exploitation in real-time applications. Additionally, simulation outcomes exemplify the efficiency and swift conver-gence rate of the proposed algorithm and demonstrate how different system characteristics, including co-channel interference (CCI), residual self-interference (RSI), and the number of RIS reflecting elements, affect the system’s performance.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"195 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121853117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118908
Marwan Dhuheir, A. Erbad, Sinan Sabeeh
Recently, Unmanned Aerial Vehicles (UAVs) have shown impressive performance in many critical applications, such as surveillance, search and rescue operations, environmental monitoring, etc. In many of these applications, the UAVs capture images as well as other sensory data and then send the data processing requests to remote servers. Nevertheless, this approach is not always practical in real-time-based applications due to unstable connections, limited bandwidth, limited energy, and strict end-to-end latency. One promising solution is to divide the inference requests into subtasks that can be distributed among UAVs in a swarm based on the available resources. Moreover, these tasks create intermediate results that need to be transmitted reliably as the swarm moves to cover the area. Our system model deals with real-time requests, aiming to find the optimal transmission power that guarantees higher reliability and low latency. We formulate the Low Latency and High-Reliability (LLHR) distributed inference as an optimization problem, and due to the complexity of the problem, we divide it into three subproblems. In the first subproblem, we find the optimal transmit power of the connected UAVs with guaranteed transmission reliability. The second subproblem aims to find the optimal positions of the UAVs in the grid, while the last subproblem finds the optimal placement of the CNN layers in the available UAVs. We conduct extensive simulations and compare our work to two baseline models demonstrating that our model outperforms the competing models.
{"title":"LLHR: Low Latency and High Reliability CNN Distributed Inference for Resource-Constrained UAV Swarms","authors":"Marwan Dhuheir, A. Erbad, Sinan Sabeeh","doi":"10.1109/WCNC55385.2023.10118908","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118908","url":null,"abstract":"Recently, Unmanned Aerial Vehicles (UAVs) have shown impressive performance in many critical applications, such as surveillance, search and rescue operations, environmental monitoring, etc. In many of these applications, the UAVs capture images as well as other sensory data and then send the data processing requests to remote servers. Nevertheless, this approach is not always practical in real-time-based applications due to unstable connections, limited bandwidth, limited energy, and strict end-to-end latency. One promising solution is to divide the inference requests into subtasks that can be distributed among UAVs in a swarm based on the available resources. Moreover, these tasks create intermediate results that need to be transmitted reliably as the swarm moves to cover the area. Our system model deals with real-time requests, aiming to find the optimal transmission power that guarantees higher reliability and low latency. We formulate the Low Latency and High-Reliability (LLHR) distributed inference as an optimization problem, and due to the complexity of the problem, we divide it into three subproblems. In the first subproblem, we find the optimal transmit power of the connected UAVs with guaranteed transmission reliability. The second subproblem aims to find the optimal positions of the UAVs in the grid, while the last subproblem finds the optimal placement of the CNN layers in the available UAVs. We conduct extensive simulations and compare our work to two baseline models demonstrating that our model outperforms the competing models.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122080562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118795
Qian Xu, Jianyong Sun
In this paper, we investigate the transmit beamforming design for weighted sum-rate maximization in massive multiple-input multiple-output (MIMO) downlink systems. Currently, the most popular algorithm for this scenario is the weighted minimum mean square error (WMMSE) algorithm. We propose a two-stage majorization-minimization (MM) based beamforming (dubbed TMMBF) which transforms the weighted sum-rate maximization problem into a quadratic convex problem by utilizing the MM method twice. The proposed algorithm converges to a stationary point of the weighted sum-rate maximization problem. Interestingly, we find that the WMMSE algorithm is a special case of the TMMBF algorithm, thus unifying the WMMSE algorithm into the MM framework for the first time. In addition, the surrogate function of TMMBF is tighter than that of WMMSE, resulting in faster convergence of the TMMBF algorithm. The simulation results on 3GPP channel models generated from Quadriga show that the TMMBF algorithm has better performance and faster numerical convergence compared to the WMMSE algorithm.
{"title":"A Two-Stage Majorization-Minimization Based Beamforming for Downlink Massive MIMO","authors":"Qian Xu, Jianyong Sun","doi":"10.1109/WCNC55385.2023.10118795","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118795","url":null,"abstract":"In this paper, we investigate the transmit beamforming design for weighted sum-rate maximization in massive multiple-input multiple-output (MIMO) downlink systems. Currently, the most popular algorithm for this scenario is the weighted minimum mean square error (WMMSE) algorithm. We propose a two-stage majorization-minimization (MM) based beamforming (dubbed TMMBF) which transforms the weighted sum-rate maximization problem into a quadratic convex problem by utilizing the MM method twice. The proposed algorithm converges to a stationary point of the weighted sum-rate maximization problem. Interestingly, we find that the WMMSE algorithm is a special case of the TMMBF algorithm, thus unifying the WMMSE algorithm into the MM framework for the first time. In addition, the surrogate function of TMMBF is tighter than that of WMMSE, resulting in faster convergence of the TMMBF algorithm. The simulation results on 3GPP channel models generated from Quadriga show that the TMMBF algorithm has better performance and faster numerical convergence compared to the WMMSE algorithm.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122796560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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