Demand and Interference Aware Adaptive Resource Management for High Throughput GEO Satellite Systems
The scarce spectrum and power resources, the inter-beam interference, together with the high traffic demand, pose new major challenges for the next generation of Very High Throughput Satellite (VHTS) systems. Accordingly, future satellites are expected to employ advanced resource/interference manage...
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Veröffentlicht in: | IEEE open journal of the Communications Society 2022, Vol.3, p.759-775 |
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description | The scarce spectrum and power resources, the inter-beam interference, together with the high traffic demand, pose new major challenges for the next generation of Very High Throughput Satellite (VHTS) systems. Accordingly, future satellites are expected to employ advanced resource/interference management techniques to achieve high system spectrum efficiency and low power consumption while ensuring user demand satisfaction. This paper proposes a novel demand and interference aware adaptive resource management for geostationary (GEO) VHTS systems. For this, we formulate a multi-objective optimization problem to minimize the total transmit power consumption and system bandwidth usage while matching the offered capacity with the demand per beam. In this context, we consider resource management for a system with full-precoding, i.e., all beams are precoded; without precoding, i.e., no precoding is applied to any beam; and with partial precoding, i.e., only some beams are precoded. The nature of the problem is non-convex and we solve it by jointly using the Dinkelbach and Successive Convex Approximation (SCA) methods. The simulation results show that the proposed method outperforms the benchmark schemes. Specifically, we show that the proposed method requires low resource consumption, low computational time, and simultaneously achieves a high demand satisfaction. |
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Accordingly, future satellites are expected to employ advanced resource/interference management techniques to achieve high system spectrum efficiency and low power consumption while ensuring user demand satisfaction. This paper proposes a novel demand and interference aware adaptive resource management for geostationary (GEO) VHTS systems. For this, we formulate a multi-objective optimization problem to minimize the total transmit power consumption and system bandwidth usage while matching the offered capacity with the demand per beam. In this context, we consider resource management for a system with full-precoding, i.e., all beams are precoded; without precoding, i.e., no precoding is applied to any beam; and with partial precoding, i.e., only some beams are precoded. The nature of the problem is non-convex and we solve it by jointly using the Dinkelbach and Successive Convex Approximation (SCA) methods. The simulation results show that the proposed method outperforms the benchmark schemes. 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Accordingly, future satellites are expected to employ advanced resource/interference management techniques to achieve high system spectrum efficiency and low power consumption while ensuring user demand satisfaction. This paper proposes a novel demand and interference aware adaptive resource management for geostationary (GEO) VHTS systems. For this, we formulate a multi-objective optimization problem to minimize the total transmit power consumption and system bandwidth usage while matching the offered capacity with the demand per beam. In this context, we consider resource management for a system with full-precoding, i.e., all beams are precoded; without precoding, i.e., no precoding is applied to any beam; and with partial precoding, i.e., only some beams are precoded. The nature of the problem is non-convex and we solve it by jointly using the Dinkelbach and Successive Convex Approximation (SCA) methods. The simulation results show that the proposed method outperforms the benchmark schemes. Specifically, we show that the proposed method requires low resource consumption, low computational time, and simultaneously achieves a high demand satisfaction.</description><subject>Approximation</subject><subject>Bandwidth</subject><subject>Computing time</subject><subject>Demand</subject><subject>Demand satisfaction</subject><subject>Dinkelbach method</subject><subject>High throughput GEO Satellite</subject><subject>Interference</subject><subject>Multiple objective analysis</subject><subject>Optimization</subject><subject>Power consumption</subject><subject>Precoding</subject><subject>Radio resource management technique</subject><subject>Resource management</subject><subject>Satellite broadcasting</subject><subject>Satellites</subject><subject>Successive convex approximation</subject><issn>2644-125X</issn><issn>2644-125X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>D8T</sourceid><sourceid>DOA</sourceid><recordid>eNpVkV9r2zAUxc3oYKXrJ-iLYc9O9V_WY0i7JqMlsHRjb0KWrhJnie1Jcku__ZS6lO1BV-Jyzk_ce4riCqMZxkhdr78t1g-bGUGEzCgWUtb8Q3FOBGMVJvzX2T_vT8VljHuEEOEYY8rOC3cDR9O58nRWXYLgIUBnoZw_m5CrM0Nqn6D8DrEfQ-4_mM5s4QhdKn0fymW73ZWPu9CP290wpvLudl1uTILDoU1Qbl5igmP8XHz05hDh8u2-KH58vX1cLKv79d1qMb-vLGMiVQp7cA213rGGOyxZo0BBg4hEBjNs6lpSL61wWAjLsRNCGgXOUwEWbFPTi2I1cV1v9noI7dGEF92bVr82-rDVJqTWHkA3nhqLlFTWYWasNITbxgjSgGc8f5VZ1cSKzzCMzX-0m_bn_JX2O-00zYtVPOu_TPoh9H9GiEnv88K6PK4mgota8lqdqHRS2dDHGMC_czHSpzT1lKY-panf0syuq8nVAsC7Q2UkYoL-BbqInRU</recordid><startdate>2022</startdate><enddate>2022</enddate><creator>Abdu, Tedros Salih</creator><creator>Kisseleff, Steven</creator><creator>Lagunas, Eva</creator><creator>Chatzinotas, Symeon</creator><creator>Ottersten, Bjorn</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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subjects | Approximation Bandwidth Computing time Demand Demand satisfaction Dinkelbach method High throughput GEO Satellite Interference Multiple objective analysis Optimization Power consumption Precoding Radio resource management technique Resource management Satellite broadcasting Satellites Successive convex approximation |
title | Demand and Interference Aware Adaptive Resource Management for High Throughput GEO Satellite Systems |
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