In this paper,we propose a rate splitting multiple access(RSMA)based integrated sensing and communication system(ISAC),where the sensing and communication are realized simultaneously with the RSMA signal.Further,recon...In this paper,we propose a rate splitting multiple access(RSMA)based integrated sensing and communication system(ISAC),where the sensing and communication are realized simultaneously with the RSMA signal.Further,reconfigurable holographic surface(RHS)is utilized to replace the traditional antennas for beam generation,expecting to combine the advantages of RSMA and RHS.To maximize the weighted summation of system rate and probing power,an optimization problem is formulated to jointly design the digital beamformer,the holographic beamformer and the message splitting vectors.To solve the non-convex problem,we first decompose it into two subproblems,where one jointly designs the digital beamformer and message splitting vectors,and the other deals with the holographic beamformer.An iterative algorithm,which leverages successive convex approximation and semi-definite relaxation,is proposed to achieve the sub-optimal solution through solving these two subproblems alternatively.Simulations confirm the effectiveness and efficiency of the proposed algorithm.展开更多
Rate splitting multiple access(RSMA)has shown great potentials for the next generation communication systems.In this work,we consider a two-user system in hybrid satellite terrestrial network(HSTN)where one of them is...Rate splitting multiple access(RSMA)has shown great potentials for the next generation communication systems.In this work,we consider a two-user system in hybrid satellite terrestrial network(HSTN)where one of them is heavily shadowed and the other uses cooperative RSMA to improve the transmission quality.The non-convex weighted sum rate(WSR)problem formulated based on this model is usually optimized by computational burdened weighted minimum mean square error(WMMSE)algorithm.We propose to apply deep unfolding to solve the optimization problem,which maps WMMSE iterations into a layer-wise network and could achieve better performance within limited iterations.We also incorporate momentum accelerated projection gradient descent(PGD)algorithm to circumvent the complicated operations in WMMSE that are not amenable for unfolding and mapping.The momentum and step size in deep unfolding network are selected as trainable parameters for training.As shown in the simulation results,deep unfolding scheme has WSR and convergence speed advantages over original WMMSE algorithm.展开更多
As the demands of massive connections and vast coverage rapidly grow in the next wireless communication networks, rate splitting multiple access(RSMA) is considered to be the new promising access scheme since it can p...As the demands of massive connections and vast coverage rapidly grow in the next wireless communication networks, rate splitting multiple access(RSMA) is considered to be the new promising access scheme since it can provide higher efficiency with limited spectrum resources. In this paper, combining spectrum splitting with rate splitting, we propose to allocate resources with traffic offloading in hybrid satellite terrestrial networks. A novel deep reinforcement learning method is adopted to solve this challenging non-convex problem. However, the neverending learning process could prohibit its practical implementation. Therefore, we introduce the switch mechanism to avoid unnecessary learning. Additionally, the QoS constraint in the scheme can rule out unsuccessful transmission. The simulation results validates the energy efficiency performance and the convergence speed of the proposed algorithm.展开更多
基金supported by the Joint Funds of the National Natural Science Foundation of China(No.U23A20277)the Joint Funds of the National Natural Science Foundation of China(No.U22A2003).
文摘In this paper,we propose a rate splitting multiple access(RSMA)based integrated sensing and communication system(ISAC),where the sensing and communication are realized simultaneously with the RSMA signal.Further,reconfigurable holographic surface(RHS)is utilized to replace the traditional antennas for beam generation,expecting to combine the advantages of RSMA and RHS.To maximize the weighted summation of system rate and probing power,an optimization problem is formulated to jointly design the digital beamformer,the holographic beamformer and the message splitting vectors.To solve the non-convex problem,we first decompose it into two subproblems,where one jointly designs the digital beamformer and message splitting vectors,and the other deals with the holographic beamformer.An iterative algorithm,which leverages successive convex approximation and semi-definite relaxation,is proposed to achieve the sub-optimal solution through solving these two subproblems alternatively.Simulations confirm the effectiveness and efficiency of the proposed algorithm.
基金sponsored by National Natural Science Foundation of China (No. 61871422, No.62027801)
文摘Rate splitting multiple access(RSMA)has shown great potentials for the next generation communication systems.In this work,we consider a two-user system in hybrid satellite terrestrial network(HSTN)where one of them is heavily shadowed and the other uses cooperative RSMA to improve the transmission quality.The non-convex weighted sum rate(WSR)problem formulated based on this model is usually optimized by computational burdened weighted minimum mean square error(WMMSE)algorithm.We propose to apply deep unfolding to solve the optimization problem,which maps WMMSE iterations into a layer-wise network and could achieve better performance within limited iterations.We also incorporate momentum accelerated projection gradient descent(PGD)algorithm to circumvent the complicated operations in WMMSE that are not amenable for unfolding and mapping.The momentum and step size in deep unfolding network are selected as trainable parameters for training.As shown in the simulation results,deep unfolding scheme has WSR and convergence speed advantages over original WMMSE algorithm.
文摘As the demands of massive connections and vast coverage rapidly grow in the next wireless communication networks, rate splitting multiple access(RSMA) is considered to be the new promising access scheme since it can provide higher efficiency with limited spectrum resources. In this paper, combining spectrum splitting with rate splitting, we propose to allocate resources with traffic offloading in hybrid satellite terrestrial networks. A novel deep reinforcement learning method is adopted to solve this challenging non-convex problem. However, the neverending learning process could prohibit its practical implementation. Therefore, we introduce the switch mechanism to avoid unnecessary learning. Additionally, the QoS constraint in the scheme can rule out unsuccessful transmission. The simulation results validates the energy efficiency performance and the convergence speed of the proposed algorithm.
