Information freshness optimization of multiple status update streams in Internet of things:Generation rate control and service rate reservation

The Internet of things(IoT)has become a key infrastructure providing up-to-date and fresh information for policy analysis and decision-making of upper-layer applications.However,there are limited sensing and communication resources in IoT devices,which significantly affects the timeliness and freshness of the updated status.This work proposes two schemes,namely,the generation rate control and service rate reservation schemes,to improve the overall information freshness of multiple status update streams at the receiver.Specifically,using the recently proposed Age of Information(AoI)as the metric for evaluating information freshness,we characterized the overall information freshness,i.e.,the overall average AoI at the receiver for both schemes,by considering the urgency difference of status update and streams.Both schemes for status updates and streams,respectively,were formulated as two optimization problems.We proved that both problems are convex and the optimal generation and service rates for different streams are found by the standard convex optimization algorithm.Moreover,we proposed both approximate optimal generation and approximate optimal service rate for fast deployment in heavy and light load cases.Numerical results verify the theoretical findings and accuracy of the proposed approximate solutions,guiding the design and deployment of IoT.

Efficient Task Completion for Parallel Offloading in Vehicular Fog Computing

In this paper,we investigate vehicular fog computing system and develop an effective parallel offloading scheme.The service time,that addresses task offloading delay,task decomposition and handover cost,is adopted as the metric of offloading performance.We propose an available resource-aware based parallel offloading scheme,which decides target fog nodes by RSU for computation offloading jointly considering effect of vehicles mobility and time-varying computation capability.Based on Hidden Markov model and Markov chain theories,proposed scheme effectively handles the imperfect system state information for fog nodes selection by jointly achieving mobility awareness and computation perception.Simulation results are presented to corroborate the theoretical analysis and validate the effectiveness of the proposed algorithm.

Joint Optimization of Task Caching,Computation Offloading and Resource Allocation for Mobile Edge Computing

Applications with sensitive delay and sizeable data volumes,such as interactive gaming and augmented reality,have become popular in recent years.These applications pose a huge challenge for mobile users with limited resources.Computation offloading is a mainstream technique to reduce execution delay and save energy for mobile users.However,computation offloading requires communication between mobile users and mobile edge computing(MEC) servers.Such a mechanism would difficultly meet users’ demand in some data-hungry and computation-intensive applications because the energy consumption and delay caused by transmissions are considerable expenses for users.Caching task data can effectively reduce the data transmissions when users offload their tasks to the MEC server.The limited caching space at the MEC server calls for judiciously decide which tasks should be cached.Motivated by this,we consider the joint optimization of computation offloading and task caching in a cellular network.In particular,it allows users to proactively cache or offload their tasks at the MEC server.The objective of this paper is to minimize the system cost,which is defined as the weighted sum of task execution delay and energy consumption for all users.Aiming at establishing optimal performance bound for the system design,we formulate an optimization problem by jointly optimizing the task caching,computation offloading,and resource allocation.The problem is a challenging mixed-integer non-linear programming problem and is NP-hard in general.To solve it efficiently,by using convex optimization,Karmarkar ’s algorithm and the proposed fast search algorithm,we obtain an optimal solution of the formulated problem with manageable computational complexity.Extensive simulation results show that in comparison to some representative benchmark methods,the proposed solution can effectively reduce the system cost.

Max-min rate optimization for multi-user MISO-OFDM systems assisted by RIS with a wideband model

Reconfigurable intelligent surfaces(RISs)have the capability to change the wireless environment smartly Considering the attenuation of subchannels and crowding users involved in the wideband system,we introduce RISs into the multi-user multi-input single-output(MU-MISO)system with orthogonal frequency division multiplexing(OFDM)for performance enhancement.Maximizing the minimum rate of dense users in an MU-MISO-OFDM system assisted by RIS with an approximate practical model is formulated as the joint optimization problem involving subcarrier allocation,transmit precoding(TPC)matrices at the base station,and RIS passive beamforming.A coalition-game subcarrier allocation(CSA)algorithm is proposed to solve space–frequency resource allocation on subcarriers,which reforms the interference topology among dense users.Fractional programming and convex optimization method are used to optimize the TPC matrices and the RIS passive beamforming,which improves the spectral efficiency synthetically across all subchannels in the wideband system.Simulation results indicate that the CSA algorithm provides a significant gain for dense users.Besides,the proposed joint optimization method shows the considerable advantage of the RISs in the MU-MISO-OFDM system.

Joint association and beamforming optimization in reconfigurable intelligent surface-enhanced user-centric networks

Fully coordinated Cell-Free(CF)networks can alleviate the Inter-Cell Interference(ICI)for the cell-edge users in cellular networks.Due to the complex topology of the association between the Access Points(APs)and the users in CF networks,it is challenging to deploy CF networks in practical scenarios.In order to make CF networks feasible,we introduce User-Centric(UC)networks enabling each user served by a limited number of APs.As a low-cost and energy-efficient technology,Reconfigurable Intelligent Surface(RIS)can be embedded in UC networks to further improve the system performance.First,we provide a brief survey on the prior works in UC networks for clear comprehension.Then,we formulate a Spectral Efficiency(SE)maximization problem for RIS-enhanced UC networks.For solving the non-convex problem,we divide it into three subproblems and propose a joint optimization framework for optimizing AP-user association,active beamforming of multiple antennas at the APs,and the passive beamforming of the RIS.Besides,a channel gain based association method coupled with the design of RIS is proposed to construct a dynamic and efficient association.The subproblems about optimizing active and passive beamforming are solved with the fractional programming.Simulation results show that the proposed joint optimization framework for RIS-enhanced UC networks can obtain good SE compared with other benchmark schemes.