Publicação
Resource Allocation for OFDMA Systems and Energy Harvesting Communications in Multi-User Offline Scenarios
| Resumo: | We formulate the resource allocation problem of maximizing throughput in an OFDMA (Orthogonal Frequency Division Multiple Access) downlink network where the BS (Base Station) adapts the power allocation according to non-causal (offline) knowledge of the harvested energy and channel state. The offline case is important from a theoretical point of view since it provides a bound on the performance of the online problem (causal). Differently from previous work, that consider a continuous relation between SNR (Signal-to-Noise Ratio) and transmit data rate, we employ a discrete mapping that depends on the required MCSs (Modulation and Code Schemes). Also, we propose a heuristic algorithm that provides near-optimal results and achieves a good complexity/performance trade-off. |
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| Autores principais: | Carvalho, Jair Alves de |
| Outros Autores: | Saraiva, Juno Vitorino; Lima, Francisco Rafael Marques; Maciel, Tarcísio Ferreira; Cavalcanti, Francisco Rodrigo Porto |
| Assunto: | Resource allocation Rate maximization OFDMA MCS Energy Harvesting, Heuristic |
| Ano: | 2021 |
| País: | Brasil |
| Tipo de documento: | outro |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | IBICT |
| Idioma: | português |
| Origem: | Oasisbr |
| Resumo: | We formulate the resource allocation problem of maximizing throughput in an OFDMA (Orthogonal Frequency Division Multiple Access) downlink network where the BS (Base Station) adapts the power allocation according to non-causal (offline) knowledge of the harvested energy and channel state. The offline case is important from a theoretical point of view since it provides a bound on the performance of the online problem (causal). Differently from previous work, that consider a continuous relation between SNR (Signal-to-Noise Ratio) and transmit data rate, we employ a discrete mapping that depends on the required MCSs (Modulation and Code Schemes). Also, we propose a heuristic algorithm that provides near-optimal results and achieves a good complexity/performance trade-off. |
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