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Arumugam Nallanathan - One of the best experts on this subject based on the ideXlab platform.

  • joint pilot and payload power allocation for massive mimo enabled urllc iiot networks
    IEEE Journal on Selected Areas in Communications, 2020
    Co-Authors: Hong Ren, Maged Elkashlan, Cunhua Pan, Yansha Deng, Arumugam Nallanathan
    Abstract:

    The Fourth Industrial Revolution (Industrial 4.0) is coming, and this revolution will fundamentally enhance the way factories manufacture products. The conventional wired lines connecting central controller to robots or actuators will be replaced by wireless communication networks due to its low cost of maintenance and high deployment flexibility. However, some critical industrial applications require ultra-high reliability and low latency communication (URLLC). In this paper, we advocate the adoption of massive multiple-input multiple output (MIMO) to support the wireless transmission for industrial applications as it can provide deterministic communications similar as wired lines thanks to its channel hardening effects. To reduce the latency, the channel blocklength for packet transmission is finite, which incurs transmission Rate degradation and decoding error probability. Thus, conventional resource allocation for massive MIMO transmission based on Shannon capacity assuming the infinite channel blocklength is no longer optimal. We first derive the closed-form expression of lower bound (LB) of Achievable uplink Data Rate for massive MIMO system with imperfect channel state information (CSI) for both maximum-ratio combining (MRC) and zero-forcing (ZF) receivers. Then, we propose novel low complexity algorithms to solve the Achievable Data Rate maximization problems by jointly optimizing the pilot and payload transmission power for both MRC and ZF. Simulation results confirm the rapid convergence speed and performance advantage over the existing benchmark algorithms.

  • joint pilot and payload power allocation for massive mimo enabled urllc iiot networks
    arXiv: Signal Processing, 2019
    Co-Authors: Hong Ren, Maged Elkashlan, Cunhua Pan, Yansha Deng, Arumugam Nallanathan
    Abstract:

    The Fourth Industrial Revolution (Industrial 4.0) is coming, and this revolution will fundamentally enhance the way the factories manufacture products. The conventional wired lines connecting central controller to robots or actuators will be replaced by wireless communication networks due to its low cost of maintenance and high deployment flexibility. However, some critical industrial applications require ultra-high reliability and low latency communication (URLLC). In this paper, we advocate the adoption of massive multiple-input multiple output (MIMO) to support the wireless transmission for industrial applications as it can provide deterministic communications similar as wired lines thanks to its channel hardening effects. To reduce the latency, the channel blocklength for packet transmission is finite, and suffers from transmission Rate degradation and decoding error probability. Thus, conventional resource allocation for massive MIMO transmission based on Shannon capacity assuming the infinite channel blocklength is no longer optimal. We first derive the closed-form expression of lower bound (LB) of Achievable uplink Data Rate for massive MIMO system with imperfect channel state information (CSI) for both maximum-ratio combining (MRC) and zero-forcing (ZF) receivers. Then, we propose novel low-complexity algorithms to solve the Achievable Data Rate maximization problems by jointly optimizing the pilot and payload transmission power for both MRC and ZF. Simulation results confirm the rapid convergence speed and performance advantage over the existing benchmark algorithms.

  • Achievable Data Rate for urllc enabled uav systems with 3 d channel model
    arXiv: Signal Processing, 2019
    Co-Authors: Hong Ren, Maged Elkashlan, Cunhua Pan, Yansha Deng, Kezhi Wang, Arumugam Nallanathan
    Abstract:

    In this paper, we investigate the average Achievable Data Rate (AADR) of the control information delivery from the ground control station (GCS) to unmanned-aerial-vehicle (UAV) under a 3-D channel, which requires ultra-reliable and low-latency communications (URLLC) to avoid collision. The value of AADR can give insights on the packet size design. Achievable Data Rate under short channel blocklength is adopted to characterize the system performance. The UAV is assumed to be uniformly distributed within a restricted space. We first adopt the Gaussian-Chebyshev quadrature (GCQ) to approximate the exact AADR. The tight lower bound of AADR is derived in a closed form. Numerical results verify the correctness and tightness of our derived results.

  • resource allocation for urllc in 5g mission critical iot networks
    International Conference on Communications, 2019
    Co-Authors: Hong Ren, Maged Elkashlan, Cunhua Pan, Yansha Deng, Arumugam Nallanathan
    Abstract:

    Ultra-reliable and low-latency communication (URLLC) is one of three pillar applications that should be supported by the fifth generation (5G) communications. The research on this topic is still in its infancy due to the difficulties in guaranteeing extremely high reliability (say 10<sup>−9</sup>) and low latency (say 1 ms) simultaneously. The Achievable Data Rate under the short packet transmission is a complicated function of the transmission power, the blocklength and the decoding error probability. In this paper, we consider resource allocation problem in a factory automation scenario, where the central controller aims for transmitting different packets to two devices (e.g., a robot and an actuator). Two transmission schemes are considered: orthogonal multiple access (OMA) and relay-assisted transmission. We aim to jointly optimize the blocklength and power allocation to minimize the error probability of the actuator subject to reliability requirement of the robot as well as the latency constraints. We develop low-complexity algorithms to address the optimization problems for each transmission scheme. Simulation results demonstRate that the relay-assisted transmission significantly outperforms the OMA scheme.

  • robust beamforming design for ultra dense user centric c ran in the face of realistic pilot contamination and limited feedback
    IEEE Transactions on Wireless Communications, 2019
    Co-Authors: Cunhua Pan, Maged Elkashlan, Hong Ren, Arumugam Nallanathan, Lajos Hanzo
    Abstract:

    The ultra-dense cloud radio access network (UD-CRAN), in which remote radio heads are densely deployed in the network, is considered. To reduce the channel estimation overhead, we focus on the design of robust transmit beamforming for user-centric frequency division duplex UD-CRANs, where only limited channel state information (CSI) is available. Specifically, we conceive a complete procedure for acquiring the CSI that includes two key steps: channel estimation and channel quantization. The phase ambiguity (PA) is also quantized for coherent cooperative transmission. Based on the imperfect CSI, we aim to optimize the beamforming vectors in order to minimize the total transmit power subject to the users’ Rate requirements and fronthaul capacity constraints. We derive the closed-form expression of the Achievable Data Rate by exploiting the statistical properties of multiple uncertain terms. Then, we propose a low-complexity iterative algorithm for solving this problem based on the successive convex approximation technique. In each iteration, the Lagrange dual-decomposition method is employed for obtaining the optimal beamforming vector. Furthermore, a pair of low-complexity user selection algorithms is provided to guarantee the feasibility of the problem. The simulation results confirm the accuracy of our robust algorithm in terms of meeting the Rate requirements. Finally, our simulation results verify that using a single bit for quantizing the PA achieves good performance.

Hong Ren - One of the best experts on this subject based on the ideXlab platform.

  • joint pilot and payload power allocation for massive mimo enabled urllc iiot networks
    IEEE Journal on Selected Areas in Communications, 2020
    Co-Authors: Hong Ren, Maged Elkashlan, Cunhua Pan, Yansha Deng, Arumugam Nallanathan
    Abstract:

    The Fourth Industrial Revolution (Industrial 4.0) is coming, and this revolution will fundamentally enhance the way factories manufacture products. The conventional wired lines connecting central controller to robots or actuators will be replaced by wireless communication networks due to its low cost of maintenance and high deployment flexibility. However, some critical industrial applications require ultra-high reliability and low latency communication (URLLC). In this paper, we advocate the adoption of massive multiple-input multiple output (MIMO) to support the wireless transmission for industrial applications as it can provide deterministic communications similar as wired lines thanks to its channel hardening effects. To reduce the latency, the channel blocklength for packet transmission is finite, which incurs transmission Rate degradation and decoding error probability. Thus, conventional resource allocation for massive MIMO transmission based on Shannon capacity assuming the infinite channel blocklength is no longer optimal. We first derive the closed-form expression of lower bound (LB) of Achievable uplink Data Rate for massive MIMO system with imperfect channel state information (CSI) for both maximum-ratio combining (MRC) and zero-forcing (ZF) receivers. Then, we propose novel low complexity algorithms to solve the Achievable Data Rate maximization problems by jointly optimizing the pilot and payload transmission power for both MRC and ZF. Simulation results confirm the rapid convergence speed and performance advantage over the existing benchmark algorithms.

  • joint pilot and payload power allocation for massive mimo enabled urllc iiot networks
    arXiv: Signal Processing, 2019
    Co-Authors: Hong Ren, Maged Elkashlan, Cunhua Pan, Yansha Deng, Arumugam Nallanathan
    Abstract:

    The Fourth Industrial Revolution (Industrial 4.0) is coming, and this revolution will fundamentally enhance the way the factories manufacture products. The conventional wired lines connecting central controller to robots or actuators will be replaced by wireless communication networks due to its low cost of maintenance and high deployment flexibility. However, some critical industrial applications require ultra-high reliability and low latency communication (URLLC). In this paper, we advocate the adoption of massive multiple-input multiple output (MIMO) to support the wireless transmission for industrial applications as it can provide deterministic communications similar as wired lines thanks to its channel hardening effects. To reduce the latency, the channel blocklength for packet transmission is finite, and suffers from transmission Rate degradation and decoding error probability. Thus, conventional resource allocation for massive MIMO transmission based on Shannon capacity assuming the infinite channel blocklength is no longer optimal. We first derive the closed-form expression of lower bound (LB) of Achievable uplink Data Rate for massive MIMO system with imperfect channel state information (CSI) for both maximum-ratio combining (MRC) and zero-forcing (ZF) receivers. Then, we propose novel low-complexity algorithms to solve the Achievable Data Rate maximization problems by jointly optimizing the pilot and payload transmission power for both MRC and ZF. Simulation results confirm the rapid convergence speed and performance advantage over the existing benchmark algorithms.

  • Achievable Data Rate for urllc enabled uav systems with 3 d channel model
    arXiv: Signal Processing, 2019
    Co-Authors: Hong Ren, Maged Elkashlan, Cunhua Pan, Yansha Deng, Kezhi Wang, Arumugam Nallanathan
    Abstract:

    In this paper, we investigate the average Achievable Data Rate (AADR) of the control information delivery from the ground control station (GCS) to unmanned-aerial-vehicle (UAV) under a 3-D channel, which requires ultra-reliable and low-latency communications (URLLC) to avoid collision. The value of AADR can give insights on the packet size design. Achievable Data Rate under short channel blocklength is adopted to characterize the system performance. The UAV is assumed to be uniformly distributed within a restricted space. We first adopt the Gaussian-Chebyshev quadrature (GCQ) to approximate the exact AADR. The tight lower bound of AADR is derived in a closed form. Numerical results verify the correctness and tightness of our derived results.

  • resource allocation for urllc in 5g mission critical iot networks
    International Conference on Communications, 2019
    Co-Authors: Hong Ren, Maged Elkashlan, Cunhua Pan, Yansha Deng, Arumugam Nallanathan
    Abstract:

    Ultra-reliable and low-latency communication (URLLC) is one of three pillar applications that should be supported by the fifth generation (5G) communications. The research on this topic is still in its infancy due to the difficulties in guaranteeing extremely high reliability (say 10<sup>−9</sup>) and low latency (say 1 ms) simultaneously. The Achievable Data Rate under the short packet transmission is a complicated function of the transmission power, the blocklength and the decoding error probability. In this paper, we consider resource allocation problem in a factory automation scenario, where the central controller aims for transmitting different packets to two devices (e.g., a robot and an actuator). Two transmission schemes are considered: orthogonal multiple access (OMA) and relay-assisted transmission. We aim to jointly optimize the blocklength and power allocation to minimize the error probability of the actuator subject to reliability requirement of the robot as well as the latency constraints. We develop low-complexity algorithms to address the optimization problems for each transmission scheme. Simulation results demonstRate that the relay-assisted transmission significantly outperforms the OMA scheme.

  • robust beamforming design for ultra dense user centric c ran in the face of realistic pilot contamination and limited feedback
    IEEE Transactions on Wireless Communications, 2019
    Co-Authors: Cunhua Pan, Maged Elkashlan, Hong Ren, Arumugam Nallanathan, Lajos Hanzo
    Abstract:

    The ultra-dense cloud radio access network (UD-CRAN), in which remote radio heads are densely deployed in the network, is considered. To reduce the channel estimation overhead, we focus on the design of robust transmit beamforming for user-centric frequency division duplex UD-CRANs, where only limited channel state information (CSI) is available. Specifically, we conceive a complete procedure for acquiring the CSI that includes two key steps: channel estimation and channel quantization. The phase ambiguity (PA) is also quantized for coherent cooperative transmission. Based on the imperfect CSI, we aim to optimize the beamforming vectors in order to minimize the total transmit power subject to the users’ Rate requirements and fronthaul capacity constraints. We derive the closed-form expression of the Achievable Data Rate by exploiting the statistical properties of multiple uncertain terms. Then, we propose a low-complexity iterative algorithm for solving this problem based on the successive convex approximation technique. In each iteration, the Lagrange dual-decomposition method is employed for obtaining the optimal beamforming vector. Furthermore, a pair of low-complexity user selection algorithms is provided to guarantee the feasibility of the problem. The simulation results confirm the accuracy of our robust algorithm in terms of meeting the Rate requirements. Finally, our simulation results verify that using a single bit for quantizing the PA achieves good performance.

Cunhua Pan - One of the best experts on this subject based on the ideXlab platform.

  • joint pilot and payload power allocation for massive mimo enabled urllc iiot networks
    IEEE Journal on Selected Areas in Communications, 2020
    Co-Authors: Hong Ren, Maged Elkashlan, Cunhua Pan, Yansha Deng, Arumugam Nallanathan
    Abstract:

    The Fourth Industrial Revolution (Industrial 4.0) is coming, and this revolution will fundamentally enhance the way factories manufacture products. The conventional wired lines connecting central controller to robots or actuators will be replaced by wireless communication networks due to its low cost of maintenance and high deployment flexibility. However, some critical industrial applications require ultra-high reliability and low latency communication (URLLC). In this paper, we advocate the adoption of massive multiple-input multiple output (MIMO) to support the wireless transmission for industrial applications as it can provide deterministic communications similar as wired lines thanks to its channel hardening effects. To reduce the latency, the channel blocklength for packet transmission is finite, which incurs transmission Rate degradation and decoding error probability. Thus, conventional resource allocation for massive MIMO transmission based on Shannon capacity assuming the infinite channel blocklength is no longer optimal. We first derive the closed-form expression of lower bound (LB) of Achievable uplink Data Rate for massive MIMO system with imperfect channel state information (CSI) for both maximum-ratio combining (MRC) and zero-forcing (ZF) receivers. Then, we propose novel low complexity algorithms to solve the Achievable Data Rate maximization problems by jointly optimizing the pilot and payload transmission power for both MRC and ZF. Simulation results confirm the rapid convergence speed and performance advantage over the existing benchmark algorithms.

  • joint pilot and payload power allocation for massive mimo enabled urllc iiot networks
    arXiv: Signal Processing, 2019
    Co-Authors: Hong Ren, Maged Elkashlan, Cunhua Pan, Yansha Deng, Arumugam Nallanathan
    Abstract:

    The Fourth Industrial Revolution (Industrial 4.0) is coming, and this revolution will fundamentally enhance the way the factories manufacture products. The conventional wired lines connecting central controller to robots or actuators will be replaced by wireless communication networks due to its low cost of maintenance and high deployment flexibility. However, some critical industrial applications require ultra-high reliability and low latency communication (URLLC). In this paper, we advocate the adoption of massive multiple-input multiple output (MIMO) to support the wireless transmission for industrial applications as it can provide deterministic communications similar as wired lines thanks to its channel hardening effects. To reduce the latency, the channel blocklength for packet transmission is finite, and suffers from transmission Rate degradation and decoding error probability. Thus, conventional resource allocation for massive MIMO transmission based on Shannon capacity assuming the infinite channel blocklength is no longer optimal. We first derive the closed-form expression of lower bound (LB) of Achievable uplink Data Rate for massive MIMO system with imperfect channel state information (CSI) for both maximum-ratio combining (MRC) and zero-forcing (ZF) receivers. Then, we propose novel low-complexity algorithms to solve the Achievable Data Rate maximization problems by jointly optimizing the pilot and payload transmission power for both MRC and ZF. Simulation results confirm the rapid convergence speed and performance advantage over the existing benchmark algorithms.

  • Achievable Data Rate for urllc enabled uav systems with 3 d channel model
    arXiv: Signal Processing, 2019
    Co-Authors: Hong Ren, Maged Elkashlan, Cunhua Pan, Yansha Deng, Kezhi Wang, Arumugam Nallanathan
    Abstract:

    In this paper, we investigate the average Achievable Data Rate (AADR) of the control information delivery from the ground control station (GCS) to unmanned-aerial-vehicle (UAV) under a 3-D channel, which requires ultra-reliable and low-latency communications (URLLC) to avoid collision. The value of AADR can give insights on the packet size design. Achievable Data Rate under short channel blocklength is adopted to characterize the system performance. The UAV is assumed to be uniformly distributed within a restricted space. We first adopt the Gaussian-Chebyshev quadrature (GCQ) to approximate the exact AADR. The tight lower bound of AADR is derived in a closed form. Numerical results verify the correctness and tightness of our derived results.

  • resource allocation for urllc in 5g mission critical iot networks
    International Conference on Communications, 2019
    Co-Authors: Hong Ren, Maged Elkashlan, Cunhua Pan, Yansha Deng, Arumugam Nallanathan
    Abstract:

    Ultra-reliable and low-latency communication (URLLC) is one of three pillar applications that should be supported by the fifth generation (5G) communications. The research on this topic is still in its infancy due to the difficulties in guaranteeing extremely high reliability (say 10<sup>−9</sup>) and low latency (say 1 ms) simultaneously. The Achievable Data Rate under the short packet transmission is a complicated function of the transmission power, the blocklength and the decoding error probability. In this paper, we consider resource allocation problem in a factory automation scenario, where the central controller aims for transmitting different packets to two devices (e.g., a robot and an actuator). Two transmission schemes are considered: orthogonal multiple access (OMA) and relay-assisted transmission. We aim to jointly optimize the blocklength and power allocation to minimize the error probability of the actuator subject to reliability requirement of the robot as well as the latency constraints. We develop low-complexity algorithms to address the optimization problems for each transmission scheme. Simulation results demonstRate that the relay-assisted transmission significantly outperforms the OMA scheme.

  • robust beamforming design for ultra dense user centric c ran in the face of realistic pilot contamination and limited feedback
    IEEE Transactions on Wireless Communications, 2019
    Co-Authors: Cunhua Pan, Maged Elkashlan, Hong Ren, Arumugam Nallanathan, Lajos Hanzo
    Abstract:

    The ultra-dense cloud radio access network (UD-CRAN), in which remote radio heads are densely deployed in the network, is considered. To reduce the channel estimation overhead, we focus on the design of robust transmit beamforming for user-centric frequency division duplex UD-CRANs, where only limited channel state information (CSI) is available. Specifically, we conceive a complete procedure for acquiring the CSI that includes two key steps: channel estimation and channel quantization. The phase ambiguity (PA) is also quantized for coherent cooperative transmission. Based on the imperfect CSI, we aim to optimize the beamforming vectors in order to minimize the total transmit power subject to the users’ Rate requirements and fronthaul capacity constraints. We derive the closed-form expression of the Achievable Data Rate by exploiting the statistical properties of multiple uncertain terms. Then, we propose a low-complexity iterative algorithm for solving this problem based on the successive convex approximation technique. In each iteration, the Lagrange dual-decomposition method is employed for obtaining the optimal beamforming vector. Furthermore, a pair of low-complexity user selection algorithms is provided to guarantee the feasibility of the problem. The simulation results confirm the accuracy of our robust algorithm in terms of meeting the Rate requirements. Finally, our simulation results verify that using a single bit for quantizing the PA achieves good performance.

Maged Elkashlan - One of the best experts on this subject based on the ideXlab platform.

  • joint pilot and payload power allocation for massive mimo enabled urllc iiot networks
    IEEE Journal on Selected Areas in Communications, 2020
    Co-Authors: Hong Ren, Maged Elkashlan, Cunhua Pan, Yansha Deng, Arumugam Nallanathan
    Abstract:

    The Fourth Industrial Revolution (Industrial 4.0) is coming, and this revolution will fundamentally enhance the way factories manufacture products. The conventional wired lines connecting central controller to robots or actuators will be replaced by wireless communication networks due to its low cost of maintenance and high deployment flexibility. However, some critical industrial applications require ultra-high reliability and low latency communication (URLLC). In this paper, we advocate the adoption of massive multiple-input multiple output (MIMO) to support the wireless transmission for industrial applications as it can provide deterministic communications similar as wired lines thanks to its channel hardening effects. To reduce the latency, the channel blocklength for packet transmission is finite, which incurs transmission Rate degradation and decoding error probability. Thus, conventional resource allocation for massive MIMO transmission based on Shannon capacity assuming the infinite channel blocklength is no longer optimal. We first derive the closed-form expression of lower bound (LB) of Achievable uplink Data Rate for massive MIMO system with imperfect channel state information (CSI) for both maximum-ratio combining (MRC) and zero-forcing (ZF) receivers. Then, we propose novel low complexity algorithms to solve the Achievable Data Rate maximization problems by jointly optimizing the pilot and payload transmission power for both MRC and ZF. Simulation results confirm the rapid convergence speed and performance advantage over the existing benchmark algorithms.

  • joint pilot and payload power allocation for massive mimo enabled urllc iiot networks
    arXiv: Signal Processing, 2019
    Co-Authors: Hong Ren, Maged Elkashlan, Cunhua Pan, Yansha Deng, Arumugam Nallanathan
    Abstract:

    The Fourth Industrial Revolution (Industrial 4.0) is coming, and this revolution will fundamentally enhance the way the factories manufacture products. The conventional wired lines connecting central controller to robots or actuators will be replaced by wireless communication networks due to its low cost of maintenance and high deployment flexibility. However, some critical industrial applications require ultra-high reliability and low latency communication (URLLC). In this paper, we advocate the adoption of massive multiple-input multiple output (MIMO) to support the wireless transmission for industrial applications as it can provide deterministic communications similar as wired lines thanks to its channel hardening effects. To reduce the latency, the channel blocklength for packet transmission is finite, and suffers from transmission Rate degradation and decoding error probability. Thus, conventional resource allocation for massive MIMO transmission based on Shannon capacity assuming the infinite channel blocklength is no longer optimal. We first derive the closed-form expression of lower bound (LB) of Achievable uplink Data Rate for massive MIMO system with imperfect channel state information (CSI) for both maximum-ratio combining (MRC) and zero-forcing (ZF) receivers. Then, we propose novel low-complexity algorithms to solve the Achievable Data Rate maximization problems by jointly optimizing the pilot and payload transmission power for both MRC and ZF. Simulation results confirm the rapid convergence speed and performance advantage over the existing benchmark algorithms.

  • Achievable Data Rate for urllc enabled uav systems with 3 d channel model
    arXiv: Signal Processing, 2019
    Co-Authors: Hong Ren, Maged Elkashlan, Cunhua Pan, Yansha Deng, Kezhi Wang, Arumugam Nallanathan
    Abstract:

    In this paper, we investigate the average Achievable Data Rate (AADR) of the control information delivery from the ground control station (GCS) to unmanned-aerial-vehicle (UAV) under a 3-D channel, which requires ultra-reliable and low-latency communications (URLLC) to avoid collision. The value of AADR can give insights on the packet size design. Achievable Data Rate under short channel blocklength is adopted to characterize the system performance. The UAV is assumed to be uniformly distributed within a restricted space. We first adopt the Gaussian-Chebyshev quadrature (GCQ) to approximate the exact AADR. The tight lower bound of AADR is derived in a closed form. Numerical results verify the correctness and tightness of our derived results.

  • resource allocation for urllc in 5g mission critical iot networks
    International Conference on Communications, 2019
    Co-Authors: Hong Ren, Maged Elkashlan, Cunhua Pan, Yansha Deng, Arumugam Nallanathan
    Abstract:

    Ultra-reliable and low-latency communication (URLLC) is one of three pillar applications that should be supported by the fifth generation (5G) communications. The research on this topic is still in its infancy due to the difficulties in guaranteeing extremely high reliability (say 10<sup>−9</sup>) and low latency (say 1 ms) simultaneously. The Achievable Data Rate under the short packet transmission is a complicated function of the transmission power, the blocklength and the decoding error probability. In this paper, we consider resource allocation problem in a factory automation scenario, where the central controller aims for transmitting different packets to two devices (e.g., a robot and an actuator). Two transmission schemes are considered: orthogonal multiple access (OMA) and relay-assisted transmission. We aim to jointly optimize the blocklength and power allocation to minimize the error probability of the actuator subject to reliability requirement of the robot as well as the latency constraints. We develop low-complexity algorithms to address the optimization problems for each transmission scheme. Simulation results demonstRate that the relay-assisted transmission significantly outperforms the OMA scheme.

  • robust beamforming design for ultra dense user centric c ran in the face of realistic pilot contamination and limited feedback
    IEEE Transactions on Wireless Communications, 2019
    Co-Authors: Cunhua Pan, Maged Elkashlan, Hong Ren, Arumugam Nallanathan, Lajos Hanzo
    Abstract:

    The ultra-dense cloud radio access network (UD-CRAN), in which remote radio heads are densely deployed in the network, is considered. To reduce the channel estimation overhead, we focus on the design of robust transmit beamforming for user-centric frequency division duplex UD-CRANs, where only limited channel state information (CSI) is available. Specifically, we conceive a complete procedure for acquiring the CSI that includes two key steps: channel estimation and channel quantization. The phase ambiguity (PA) is also quantized for coherent cooperative transmission. Based on the imperfect CSI, we aim to optimize the beamforming vectors in order to minimize the total transmit power subject to the users’ Rate requirements and fronthaul capacity constraints. We derive the closed-form expression of the Achievable Data Rate by exploiting the statistical properties of multiple uncertain terms. Then, we propose a low-complexity iterative algorithm for solving this problem based on the successive convex approximation technique. In each iteration, the Lagrange dual-decomposition method is employed for obtaining the optimal beamforming vector. Furthermore, a pair of low-complexity user selection algorithms is provided to guarantee the feasibility of the problem. The simulation results confirm the accuracy of our robust algorithm in terms of meeting the Rate requirements. Finally, our simulation results verify that using a single bit for quantizing the PA achieves good performance.

Lajos Hanzo - One of the best experts on this subject based on the ideXlab platform.

  • ai assisted mac for reconfigurable intelligent surface aided wireless networks challenges and opportunities
    arXiv: Networking and Internet Architecture, 2021
    Co-Authors: Xuelin Cao, Marco Di Renzo, Zhu Han, Vincent H Poor, Bo Yang, Chongwen Huang, Chau Yuen, Dusit Niyato, Lajos Hanzo
    Abstract:

    Recently, significant research attention has been devoted to the study of reconfigurable intelligent surfaces (RISs), which are capable of reconfiguring the wireless propagation environment by exploiting the unique properties of metamaterials-based integRated large arrays of inexpensive antennas. Existing research demonstRates that RISs significantly improve the physical layer performance, including the wireless coverage, Achievable Data Rate and energy efficiency. However, the medium access control (MAC) of multiple users accessing an RIS-enabled channel is still in its infancy, while many open issues remain to be addressed. In this article, we present four typical RIS-aided multi-user scenarios with special emphasis on the MAC schemes. We then propose and elaboRate upon centralized, distributed and hybrid artificial-intelligence (AI)-assisted MAC architectures in RIS-aided multi-user communications systems. Finally, we discuss some challenges, perspectives and potential applications of RISs as they are related to MAC design.

  • ai assisted mac for reconfigurable intelligent surface aided wireless networks challenges and opportunities
    IEEE Communications Magazine, 2021
    Co-Authors: Xuelin Cao, Marco Di Renzo, Zhu Han, Vincent H Poor, Bo Yang, Chongwen Huang, Chau Yuen, Dusit Niyato, Lajos Hanzo
    Abstract:

    Recently, significant research attention has been devoted to the study of reconfigurable intelligent surfaces (RISs), which are capable of reconfiguring the wireless propagation environment by exploiting the unique properties of metamaterials-based integRated large arrays of inexpensive antennas. Existing research demonstRates that RISs significantly improve physical layer performance, including wireless coverage, Achievable Data Rate, and energy efficiency. However, the medium access control (MAC) of multiple users accessing an RIS-enabled channel is still in its infancy, while many open issues remain to be addressed. In this article, we present four typical RIS-aided multi-user scenarios with special emphasis on the MAC schemes. We then propose and elaboRate on centralized, distributed, and hybrid artificial-in-telligence-assisted MAC architectures in RIS-aid-ed multi-user communications systems. Finally, we discuss some challenges, perspectives, and potential applications of RISs as they are related to MAC design.

  • robust beamforming design for ultra dense user centric c ran in the face of realistic pilot contamination and limited feedback
    IEEE Transactions on Wireless Communications, 2019
    Co-Authors: Cunhua Pan, Maged Elkashlan, Hong Ren, Arumugam Nallanathan, Lajos Hanzo
    Abstract:

    The ultra-dense cloud radio access network (UD-CRAN), in which remote radio heads are densely deployed in the network, is considered. To reduce the channel estimation overhead, we focus on the design of robust transmit beamforming for user-centric frequency division duplex UD-CRANs, where only limited channel state information (CSI) is available. Specifically, we conceive a complete procedure for acquiring the CSI that includes two key steps: channel estimation and channel quantization. The phase ambiguity (PA) is also quantized for coherent cooperative transmission. Based on the imperfect CSI, we aim to optimize the beamforming vectors in order to minimize the total transmit power subject to the users’ Rate requirements and fronthaul capacity constraints. We derive the closed-form expression of the Achievable Data Rate by exploiting the statistical properties of multiple uncertain terms. Then, we propose a low-complexity iterative algorithm for solving this problem based on the successive convex approximation technique. In each iteration, the Lagrange dual-decomposition method is employed for obtaining the optimal beamforming vector. Furthermore, a pair of low-complexity user selection algorithms is provided to guarantee the feasibility of the problem. The simulation results confirm the accuracy of our robust algorithm in terms of meeting the Rate requirements. Finally, our simulation results verify that using a single bit for quantizing the PA achieves good performance.

  • robust beamforming design for ultra dense user centric c ran in the face of realistic pilot contamination and limited feedback
    arXiv: Signal Processing, 2018
    Co-Authors: Cunhua Pan, Maged Elkashlan, Hong Ren, Arumugam Nallanathan, Lajos Hanzo
    Abstract:

    The ultra-dense cloud radio access network (UD-CRAN), in which remote radio heads (RRHs) are densely deployed in the network, is considered. To reduce the channel estimation overhead, we focus on the design of robust transmit beamforming for user-centric frequency division duplex (FDD) UD-CRANs, where only limited channel state information (CSI) is available. Specifically, we conceive a complete procedure for acquiring the CSI that includes two key steps: channel estimation and channel quantization. The phase ambiguity (PA) is also quantized for coherent cooperative transmission. Based on the imperfect CSI, we aim for optimizing the beamforming vectors in order to minimize the total transmit power subject to users' Rate requirements and fronthaul capacity constraints. We derive the closed-form expression of the Achievable Data Rate by exploiting the statistical properties of multiple uncertain terms. Then, we propose a low-complexity iterative algorithm for solving this problem based on the successive convex approximation technique. In each iteration, the Lagrange dual decomposition method is employed for obtaining the optimal beamforming vector. Furthermore, a pair of low-complexity user selection algorithms are provided to guarantee the feasibility of the problem. Simulation results confirm the accuracy of our robust algorithm in terms of meeting the Rate requirements. Finally, our simulation results verify that using a single bit for quantizing the PA is capable of achieving good performance.