The Experts below are selected from a list of 27 Experts worldwide ranked by ideXlab platform
Yafeng Liu - One of the best experts on this subject based on the ideXlab platform.
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joint base station clustering and beamforming for non orthogonal multicast and Unicast transmission with backhaul constraints
IEEE Transactions on Wireless Communications, 2018Co-Authors: Erkai Chen, Meixia Tao, Yafeng LiuAbstract:The demand for providing multicast services in cellular networks is continuously and fastly increasing. In this paper, we propose a non-orthogonal transmission framework based on layered-division multiplexing (LDM) to support multicast and Unicast services concurrently in cooperative multi-cell cellular networks with a limited backhaul capacity. We adopt a two-layer LDM structure where the first layer is intended for multicast services, the second layer is for Unicast services, and the two layers are superposed with different beamformers. Each user decodes the multicast Message first, subtracts it, and then decodes its dedicated Unicast Message. We formulate a joint multicast and Unicast beamforming problem with adaptive base station clustering that aims to maximize the weighted sum of the multicast rate and the Unicast rate under per-BS power and backhaul constraints. To solve the problem, we first develop a branch-and-bound algorithm to find its global optimum. We then reformulate the problem as a sparse beamforming problem and propose a low-complexity algorithm based on convex-concave procedure. Simulation results demonstrate the significant superiority of the proposed LDM-based non-orthogonal scheme over orthogonal schemes in terms of the achievable multicast-Unicast rate region.
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joint base station clustering and beamforming for non orthogonal multicast and Unicast transmission with backhaul constraints
arXiv: Information Theory, 2017Co-Authors: Erkai Chen, Meixia Tao, Yafeng LiuAbstract:The demand for providing multicast services in cellular networks is continuously and fastly increasing. In this work, we propose a non-orthogonal transmission framework based on layered-division multiplexing (LDM) to support multicast and Unicast services concurrently in cooperative multi-cell cellular networks with limited backhaul capacity. We adopt a two-layer LDM structure where the first layer is intended for multicast services, the second layer is for Unicast services, and the two layers are superposed with different beamformers. Each user decodes the multicast Message first, subtracts it, and then decodes its dedicated Unicast Message. We formulate a joint multicast and Unicast beamforming problem with adaptive base station clustering that aims to maximize the weighted sum of the multicast rate and the Unicast rate under per-BS power and backhaul constraints. To solve the problem, we first develop a branch-and-bound algorithm to find its global optimum. We then reformulate the problem as a sparse beamforming problem and propose a low-complexity algorithm based on convex-concave procedure. Simulation results demonstrate the significant superiority of the proposed LDM-based non-orthogonal scheme over orthogonal schemes in terms of the achievable multicast-Unicast rate region.
Erkai Chen - One of the best experts on this subject based on the ideXlab platform.
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joint base station clustering and beamforming for non orthogonal multicast and Unicast transmission with backhaul constraints
IEEE Transactions on Wireless Communications, 2018Co-Authors: Erkai Chen, Meixia Tao, Yafeng LiuAbstract:The demand for providing multicast services in cellular networks is continuously and fastly increasing. In this paper, we propose a non-orthogonal transmission framework based on layered-division multiplexing (LDM) to support multicast and Unicast services concurrently in cooperative multi-cell cellular networks with a limited backhaul capacity. We adopt a two-layer LDM structure where the first layer is intended for multicast services, the second layer is for Unicast services, and the two layers are superposed with different beamformers. Each user decodes the multicast Message first, subtracts it, and then decodes its dedicated Unicast Message. We formulate a joint multicast and Unicast beamforming problem with adaptive base station clustering that aims to maximize the weighted sum of the multicast rate and the Unicast rate under per-BS power and backhaul constraints. To solve the problem, we first develop a branch-and-bound algorithm to find its global optimum. We then reformulate the problem as a sparse beamforming problem and propose a low-complexity algorithm based on convex-concave procedure. Simulation results demonstrate the significant superiority of the proposed LDM-based non-orthogonal scheme over orthogonal schemes in terms of the achievable multicast-Unicast rate region.
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joint base station clustering and beamforming for non orthogonal multicast and Unicast transmission with backhaul constraints
arXiv: Information Theory, 2017Co-Authors: Erkai Chen, Meixia Tao, Yafeng LiuAbstract:The demand for providing multicast services in cellular networks is continuously and fastly increasing. In this work, we propose a non-orthogonal transmission framework based on layered-division multiplexing (LDM) to support multicast and Unicast services concurrently in cooperative multi-cell cellular networks with limited backhaul capacity. We adopt a two-layer LDM structure where the first layer is intended for multicast services, the second layer is for Unicast services, and the two layers are superposed with different beamformers. Each user decodes the multicast Message first, subtracts it, and then decodes its dedicated Unicast Message. We formulate a joint multicast and Unicast beamforming problem with adaptive base station clustering that aims to maximize the weighted sum of the multicast rate and the Unicast rate under per-BS power and backhaul constraints. To solve the problem, we first develop a branch-and-bound algorithm to find its global optimum. We then reformulate the problem as a sparse beamforming problem and propose a low-complexity algorithm based on convex-concave procedure. Simulation results demonstrate the significant superiority of the proposed LDM-based non-orthogonal scheme over orthogonal schemes in terms of the achievable multicast-Unicast rate region.
Meixia Tao - One of the best experts on this subject based on the ideXlab platform.
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joint base station clustering and beamforming for non orthogonal multicast and Unicast transmission with backhaul constraints
IEEE Transactions on Wireless Communications, 2018Co-Authors: Erkai Chen, Meixia Tao, Yafeng LiuAbstract:The demand for providing multicast services in cellular networks is continuously and fastly increasing. In this paper, we propose a non-orthogonal transmission framework based on layered-division multiplexing (LDM) to support multicast and Unicast services concurrently in cooperative multi-cell cellular networks with a limited backhaul capacity. We adopt a two-layer LDM structure where the first layer is intended for multicast services, the second layer is for Unicast services, and the two layers are superposed with different beamformers. Each user decodes the multicast Message first, subtracts it, and then decodes its dedicated Unicast Message. We formulate a joint multicast and Unicast beamforming problem with adaptive base station clustering that aims to maximize the weighted sum of the multicast rate and the Unicast rate under per-BS power and backhaul constraints. To solve the problem, we first develop a branch-and-bound algorithm to find its global optimum. We then reformulate the problem as a sparse beamforming problem and propose a low-complexity algorithm based on convex-concave procedure. Simulation results demonstrate the significant superiority of the proposed LDM-based non-orthogonal scheme over orthogonal schemes in terms of the achievable multicast-Unicast rate region.
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joint base station clustering and beamforming for non orthogonal multicast and Unicast transmission with backhaul constraints
arXiv: Information Theory, 2017Co-Authors: Erkai Chen, Meixia Tao, Yafeng LiuAbstract:The demand for providing multicast services in cellular networks is continuously and fastly increasing. In this work, we propose a non-orthogonal transmission framework based on layered-division multiplexing (LDM) to support multicast and Unicast services concurrently in cooperative multi-cell cellular networks with limited backhaul capacity. We adopt a two-layer LDM structure where the first layer is intended for multicast services, the second layer is for Unicast services, and the two layers are superposed with different beamformers. Each user decodes the multicast Message first, subtracts it, and then decodes its dedicated Unicast Message. We formulate a joint multicast and Unicast beamforming problem with adaptive base station clustering that aims to maximize the weighted sum of the multicast rate and the Unicast rate under per-BS power and backhaul constraints. To solve the problem, we first develop a branch-and-bound algorithm to find its global optimum. We then reformulate the problem as a sparse beamforming problem and propose a low-complexity algorithm based on convex-concave procedure. Simulation results demonstrate the significant superiority of the proposed LDM-based non-orthogonal scheme over orthogonal schemes in terms of the achievable multicast-Unicast rate region.
K Muneeswaran - One of the best experts on this subject based on the ideXlab platform.
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multi constraint multi objective qos aware routing heuristics for query driven sensor networks using fuzzy soft sets
Applied Soft Computing, 2017Co-Authors: Kavi S Priya, T Revathi, K MuneeswaranAbstract:Display Omitted A fuzzy based routing technique is proposed to enhance the lifetime of randomly deployed homogenous sensor network for query driven applications.Multiple broadcast query from sink to the sensor nodes considers the routing uncertainties due to link quality, remaining energy and traffic load.Routing individual Unicast replies from the sensor nodes to the sink will consider only link quality and remaining energy of each node.In the link layer asynchronous scheduling algorithm is used to reduce latency due to time synchronization for network communication.Nearest neighbor tree, Fuzzy and A star algorithms are used to find optimal route in the sensor network under energy and bandwidth constraints. In this paper, a fuzzy based distributed power aware routing scheme considering both energy and bandwidth constraints, especially for query driven applications in the asynchronous duty-cycled wireless sensor networks are devised. The proposed multi-constraint, multi-objective routing optimization approach under strict resource constraints guarantees reliability and fast data delivery along with efficient power management in spite of unreliable wireless links and limited power supply. In query driven applications, the request from the sink to the individual sensor node will be a broadcast Message, whereas the individual sensor nodes replies back to sink as Unicast Messages. In the proposed work, the fuzzy approach and A Star algorithm are utilized for satisfying energy and bandwidth constraints to route the broadcast Messages of the sink while querying all the sensor nodes in the network. Every node will be provided with a guidance list, which is used to decide the next best neighbor node with good route quality for forwarding the received multi-hop broadcast Messages. The route quality of the every node is estimated with fuzzy rules based on the network parameters such as maximum remaining energy, minimum traffic load and better link quality to increase the network lifetime. The provision of overhearing the broadcast Messages and acknowledgements within the transmission range minimizes the effort to search for the active time of nodes while routing the broadcast Messages with asynchronous scheduling. Further, in the proposed work only the time slot of its nearest neighbor relay node (to which packets are to be forwarded) is learnt to reduce the number of Message transmissions in the network. For the Unicast Message replies, the fuzzy membership function is modified and devised based on the routing metrics such as higher residual energy, minimum traffic loads and minimum hop count under energy and bandwidth constraints. Also, the multi-hop heuristic routing algorithm called Nearest Neighbor Tree is effectively used to reduce the number of neighbors in the guidance list that are elected for forwarding. This helps to increase the individual sensor nodes lifetime, thereby maximizes the network lifetime and guarantees increased network throughput. The simulation results show that the proposed technique reduces repeated transmissions, decreases the number of transmissions, shortens the active time of the sensor nodes and increases the network lifetime for query driven sensor network applications invariant to total the number of sensor nodes and sinks in the network. The proposed algorithm is tested in a small test bed of sensor network with ten nodes that monitors the room temperature.
Kavi S Priya - One of the best experts on this subject based on the ideXlab platform.
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multi constraint multi objective qos aware routing heuristics for query driven sensor networks using fuzzy soft sets
Applied Soft Computing, 2017Co-Authors: Kavi S Priya, T Revathi, K MuneeswaranAbstract:Display Omitted A fuzzy based routing technique is proposed to enhance the lifetime of randomly deployed homogenous sensor network for query driven applications.Multiple broadcast query from sink to the sensor nodes considers the routing uncertainties due to link quality, remaining energy and traffic load.Routing individual Unicast replies from the sensor nodes to the sink will consider only link quality and remaining energy of each node.In the link layer asynchronous scheduling algorithm is used to reduce latency due to time synchronization for network communication.Nearest neighbor tree, Fuzzy and A star algorithms are used to find optimal route in the sensor network under energy and bandwidth constraints. In this paper, a fuzzy based distributed power aware routing scheme considering both energy and bandwidth constraints, especially for query driven applications in the asynchronous duty-cycled wireless sensor networks are devised. The proposed multi-constraint, multi-objective routing optimization approach under strict resource constraints guarantees reliability and fast data delivery along with efficient power management in spite of unreliable wireless links and limited power supply. In query driven applications, the request from the sink to the individual sensor node will be a broadcast Message, whereas the individual sensor nodes replies back to sink as Unicast Messages. In the proposed work, the fuzzy approach and A Star algorithm are utilized for satisfying energy and bandwidth constraints to route the broadcast Messages of the sink while querying all the sensor nodes in the network. Every node will be provided with a guidance list, which is used to decide the next best neighbor node with good route quality for forwarding the received multi-hop broadcast Messages. The route quality of the every node is estimated with fuzzy rules based on the network parameters such as maximum remaining energy, minimum traffic load and better link quality to increase the network lifetime. The provision of overhearing the broadcast Messages and acknowledgements within the transmission range minimizes the effort to search for the active time of nodes while routing the broadcast Messages with asynchronous scheduling. Further, in the proposed work only the time slot of its nearest neighbor relay node (to which packets are to be forwarded) is learnt to reduce the number of Message transmissions in the network. For the Unicast Message replies, the fuzzy membership function is modified and devised based on the routing metrics such as higher residual energy, minimum traffic loads and minimum hop count under energy and bandwidth constraints. Also, the multi-hop heuristic routing algorithm called Nearest Neighbor Tree is effectively used to reduce the number of neighbors in the guidance list that are elected for forwarding. This helps to increase the individual sensor nodes lifetime, thereby maximizes the network lifetime and guarantees increased network throughput. The simulation results show that the proposed technique reduces repeated transmissions, decreases the number of transmissions, shortens the active time of the sensor nodes and increases the network lifetime for query driven sensor network applications invariant to total the number of sensor nodes and sinks in the network. The proposed algorithm is tested in a small test bed of sensor network with ten nodes that monitors the room temperature.
