The main aim of future mobile networks is to provide secure,reliable,intelligent,and seamless connectivity.It also enables mobile network operators to ensure their customer’s a better quality of service(QoS).Nowadays...The main aim of future mobile networks is to provide secure,reliable,intelligent,and seamless connectivity.It also enables mobile network operators to ensure their customer’s a better quality of service(QoS).Nowadays,Unmanned Aerial Vehicles(UAVs)are a significant part of the mobile network due to their continuously growing use in various applications.For better coverage,cost-effective,and seamless service connectivity and provisioning,UAVs have emerged as the best choice for telco operators.UAVs can be used as flying base stations,edge servers,and relay nodes in mobile networks.On the other side,Multi-access EdgeComputing(MEC)technology also emerged in the 5G network to provide a better quality of experience(QoE)to users with different QoS requirements.However,UAVs in a mobile network for coverage enhancement and better QoS face several challenges such as trajectory designing,path planning,optimization,QoS assurance,mobilitymanagement,etc.The efficient and proactive path planning and optimization in a highly dynamic environment containing buildings and obstacles are challenging.So,an automated Artificial Intelligence(AI)enabled QoSaware solution is needed for trajectory planning and optimization.Therefore,this work introduces a well-designed AI and MEC-enabled architecture for a UAVs-assisted future network.It has an efficient Deep Reinforcement Learning(DRL)algorithm for real-time and proactive trajectory planning and optimization.It also fulfills QoS-aware service provisioning.A greedypolicy approach is used to maximize the long-term reward for serving more users withQoS.Simulation results reveal the superiority of the proposed DRL mechanism for energy-efficient and QoS-aware trajectory planning over the existing models.展开更多
In order to detect and cancel the self-interference(SI)signal from desired binary phase-shift keying(BPSK)signal,the polarization-based optimal detection(POD)scheme for cancellation of digital SI in a full-duplex(FD)s...In order to detect and cancel the self-interference(SI)signal from desired binary phase-shift keying(BPSK)signal,the polarization-based optimal detection(POD)scheme for cancellation of digital SI in a full-duplex(FD)system is proposed.The POD scheme exploits the polarization domain to isolate the desired signal from the SI signal and then cancel the SI to obtain the interference-free desired signal at the receiver.In FD communication,after antenna and analog cancellation,the receiver still contains residual SI due to non-linearities of hardware imperfections.In POD scheme,a likelihood ratio expression is obtained,which isolates and detects SI bits from the desired bits.After isolation of these signal points,the POD scheme cancels the residual SI.As compared to the conventional schemes,the proposed POD scheme gives significantly low bit error rate(BER),a clear constellation diagram to obtain the boundary between desired and SI signal points,and increases the receiver's SI cancellation performance in low signal to interference ratio(SIR)environment.展开更多
基金This work was supported by the Fundamental Research Funds for the Central Universities(No.2019XD-A07)the Director Fund of Beijing Key Laboratory of Space-ground Interconnection and Convergencethe National Key Laboratory of Science and Technology on Vacuum Electronics.
文摘The main aim of future mobile networks is to provide secure,reliable,intelligent,and seamless connectivity.It also enables mobile network operators to ensure their customer’s a better quality of service(QoS).Nowadays,Unmanned Aerial Vehicles(UAVs)are a significant part of the mobile network due to their continuously growing use in various applications.For better coverage,cost-effective,and seamless service connectivity and provisioning,UAVs have emerged as the best choice for telco operators.UAVs can be used as flying base stations,edge servers,and relay nodes in mobile networks.On the other side,Multi-access EdgeComputing(MEC)technology also emerged in the 5G network to provide a better quality of experience(QoE)to users with different QoS requirements.However,UAVs in a mobile network for coverage enhancement and better QoS face several challenges such as trajectory designing,path planning,optimization,QoS assurance,mobilitymanagement,etc.The efficient and proactive path planning and optimization in a highly dynamic environment containing buildings and obstacles are challenging.So,an automated Artificial Intelligence(AI)enabled QoSaware solution is needed for trajectory planning and optimization.Therefore,this work introduces a well-designed AI and MEC-enabled architecture for a UAVs-assisted future network.It has an efficient Deep Reinforcement Learning(DRL)algorithm for real-time and proactive trajectory planning and optimization.It also fulfills QoS-aware service provisioning.A greedypolicy approach is used to maximize the long-term reward for serving more users withQoS.Simulation results reveal the superiority of the proposed DRL mechanism for energy-efficient and QoS-aware trajectory planning over the existing models.
基金This work was supported by the Fundamental Research Funds for the Central Universities(2019XD-A07)the National Natural Science Foundation of China(61771063)the Huawei Innovation Research Program(HIRP)(2018040300246).
文摘In order to detect and cancel the self-interference(SI)signal from desired binary phase-shift keying(BPSK)signal,the polarization-based optimal detection(POD)scheme for cancellation of digital SI in a full-duplex(FD)system is proposed.The POD scheme exploits the polarization domain to isolate the desired signal from the SI signal and then cancel the SI to obtain the interference-free desired signal at the receiver.In FD communication,after antenna and analog cancellation,the receiver still contains residual SI due to non-linearities of hardware imperfections.In POD scheme,a likelihood ratio expression is obtained,which isolates and detects SI bits from the desired bits.After isolation of these signal points,the POD scheme cancels the residual SI.As compared to the conventional schemes,the proposed POD scheme gives significantly low bit error rate(BER),a clear constellation diagram to obtain the boundary between desired and SI signal points,and increases the receiver's SI cancellation performance in low signal to interference ratio(SIR)environment.
