Search

To sustain communication reliability and use the harvested energy effectively, it is crucial to consider energy sharing between energy harvesting tags (EHT) in a multiple access network, which are basic building blocks for the internet of things (IoT) applications. This technique also achieves higher throughput compared with the non-cooperative strategies despite energy losses occurred during energy transfer. We propose an energy cooperative communication strategy for a multiple access network of tags that depends on the harvested battery energy. We develop an optimal transmission policy for EHTs that maximizes the long-term joint average throughput using a Markov decision process (MDP) model. Simulation results show that the proposed energy cooperative policy produces improved performance than traditional policies., Comment: Contains errors

Energy harvesting wireless devices are recently emerging as a viable infrastructure for internet of things (IoT) applications. In this paper, an energy cooperative transmission strategy is proposed for a network energy harvesting tags, that is adapted to the available energy resource and identification request. We develop an optimal transmission policy to maximize the long-term average throughput performance via a Markov decision process (MDP) formulation. Numerical results are provided to show the performance of the energy cooperative transmission policy under various scenarios.

Strong impulse noise is widely known to adversely affect conventional receivers designed only to consider background noise. Although sophisticated receivers offer substantial performance improvements, fully exploiting the impulse statistics, which are generally not time invariant and are difficult to model accurately, can be unrealistic. Alternatively, without making assumptions regarding the underlying impulse channel model, the authors' previously developed robust decoding schemes can achieve performance equivalent to that of their optimal counterparts in impulse noise channels. In this letter, a robust turbo decoding metric is proposed to address the inherent memory in an impulse channel: a 2-D trellis is used to adapt for channel state transitions when statistics on the memory impulse channel model are lacking. The simulation results verified the robustness of the proposed decoder in harsh environments.

Communication systems are susceptible to impulse noise, particularly when the impulse statistics are not time invariant and are difficult to model accurately. To address the challenge of impulse noise, a robust and efficient decoding scheme was devised for single-carrier convolutionally coded transmissions over memory impulse noise channels. By accommodating channel states but without relying on statistical knowledge of impulses, the Viterbi algorithm (VA) based on an expanded set of trellis states was employed to perform maximum-likelihood decoding. A detailed analysis of complexity was offered; the analytical results reinforced the efficiency of the proposed scheme compared with the traditional VA. The simulation results indicated that the proposed decoding scheme is compellingly robust: The bit error probability performance level attained using the proposed decoder is remarkably close to that of an optimal decoder, which uses impulse statistics; furthermore, the proposed decoder was superior to the alpha-penalty function decoder, which neglects the channel memory property and experiences an error floor under fairly general circumstances.

Energy harvesting tags with cooperative communication capabilities are recently emerging as a viable infrastructure for internet of things (IoT) applications. This letter studies the cooperative transmission strategy for a network of energy harvesting active networked tags (EnHANTs), that is adapted to the available energy resource and identification request. We consider a network of tags to communicate with the reader either directly or by cooperating with neighboring tags. We formulate the problem as a Markov decision process (MDP). The simulation results are provided to show the performance of the cooperative transmission policy under various energy harvesting scenarios.

Using cooperative transmission allows energy-harvesting active networked tags (EnHANTs) to cooperate regarding information transmission and reception, achieving higher throughput compared with non-cooperative counterparts. In this paper, a battery-threshold-based optimal cooperative transmission strategy is proposed for EnHANTs, allowing EnHANT-equipped objects to communicate with the destination (i.e., the reader) either directly or by cooperating with neighbouring objects. A strategy adapted to request states and available energy resources is considered, and an optimal transmission policy for EnHANTs is developed to maximise the long-term average throughput by formulating a Markov decision process (MDP) model. The simulated results indicate that the proposed strategy yields superior performance compared with single-link transmission.