Resource Allocation in Multi-User Cellular Networks:A Transformer-Based Deep Reinforcement Learning Approach

To meet the communication services with diverse requirements,dynamic resource allocation has shown increasing importance.In this paper,we consider the multi-slot and multi-user resource allocation(MSMU-RA)in a downlink cellular scenario with the aim of maximizing system spectral efficiency while guaranteeing user fairness.We first model the MSMURA problem as a dual-sequence decision-making process,and then solve it by a novel Transformerbased deep reinforcement learning(TDRL)approach.Specifically,the proposed TDRL approach can be achieved based on two aspects:1)To adapt to the dynamic wireless environment,the proximal policy optimization(PPO)algorithm is used to optimize the multi-slot RA strategy.2)To avoid co-channel interference,the Transformer-based PPO algorithm is presented to obtain the optimal multi-user RA scheme by exploring the mapping between user sequence and resource sequence.Experimental results show that:i)the proposed approach outperforms both the traditional and DRL methods in spectral efficiency and user fairness,ii)the proposed algorithm is superior to DRL approaches in terms of convergence speed and generalization performance.

Resource Allocation for IRS Assistedmm Wave Wireless Powered Sensor Networks with User Cooperation

In this paper,we investigate IRS-aided user cooperation(UC)scheme in millimeter wave(mmWave)wirelesspowered sensor networks(WPSN),where two single-antenna users are wireless powered in the wireless energy transfer(WET)phase first and then cooperatively transmit information to a hybrid access point(AP)in the wireless information transmission(WIT)phase,following which the IRS is deployed to enhance the system performance of theWET andWIT.We maximized the weighted sum-rate problem by jointly optimizing the transmit time slots,power allocations,and the phase shifts of the IRS.Due to the non-convexity of the original problem,a semidefinite programming relaxation-based approach is proposed to convert the formulated problem to a convex optimization framework,which can obtain the optimal global solution.Simulation results demonstrate that the weighted sum throughput of the proposed UC scheme outperforms the non-UC scheme whether equipped with IRS or not.

A comparative study on users' satisfaction of library resource sharing under the circumstance of network in Nanjing

From the perspective of information resource sharing, the authors investigated the ratio and degree of users' satisfaction with resource sharing among college libraries, public libraries and research libraries in Nanjing city. Based on the analysis and discussion about the findings, the authors summarized the library resource sharing system in satisfying users' information requirements, including its pros and cons. At last, further considerations have been provided on how to improve library resource sharing system in the region.

Two-Timescale Online Learning of Joint User Association and Resource Scheduling in Dynamic Mobile Edge Computing

For the mobile edge computing network consisting of multiple base stations and resourceconstrained user devices,network cost in terms of energy and delay will incur during task offloading from the user to the edge server.With the limitations imposed on transmission capacity,computing resource,and connection capacity,the per-slot online learning algorithm is first proposed to minimize the time-averaged network cost.In particular,by leveraging the theories of stochastic gradient descent and minimum cost maximum flow,the user association is jointly optimized with resource scheduling in each time slot.The theoretical analysis proves that the proposed approach can achieve asymptotic optimality without any prior knowledge of the network environment.Moreover,to alleviate the high network overhead incurred during user handover and task migration,a two-timescale optimization approach is proposed to avoid frequent changes in user association.With user association executed on a large timescale and the resource scheduling decided on the single time slot,the asymptotic optimality is preserved.Simulation results verify the effectiveness of the proposed online learning algorithms.

Context-Oriented Multi-RAT User Association and Resource Allocation with Triple Decision in 5G Heterogeneous Networks

In the upcoming 5 G heterogeneous networks, leveraging multiple radio access technologies(RATs) shows to be a crucial issue in achieving RAT multiplexing gain to meet the explosive traffic demand. For always best connection(ABC), users tend to activate parallel transmission across all available RATs. However from a system-wide perspective, this might not be optimal given the context of network load, interference and diverse service requirements. To intelligently determine how to use these multi-RAT access resources concurrently, this paper proposes a joint multi-RAT user association and resource allocation strategy with triple decision and integrated context awareness of users and networks. A dynamic game based ant colony algorithm(GACA) is designed to simultaneously maximize the system utility and the fairness of resource allocation. Simulation results show that it's more reasonable to make multi-RAT association decision from a system-wide viewpoint than from an individual one. Compared to max-SNR based and ABC based strategies, the proposed method alleviates network congestion and optimizes resource allocation. It obtains 39%~70% performance improvement.

A Distributed User Association and Resource Allocation Method in Cache-Enabled Small Cell Networks

Cache-enabled small cell networks have been regarded as a promising approach for network operators to cope with the explosive data traffic growth in future 5 G networks. However, the user association and resource allocation mechanism has not been thoroughly studied under given content placement situation. In this paper, we formulate the joint optimization problem of user association and resource allocation as a mixed integer nonlinear programming(MINLP) problem aiming at deriving a balance between the total utility of data rates and the total data rates retrieved from caches. To solve this problem, we propose a distributed relaxing-rounding method. Simulation results demonstrate that the distributed relaxing-rounding method outperforms traditional max-SINR method and range-expansion method in terms of both total utility of data rates and total data rates retrieved from caches in practical scenarios. In addition, effects of storage and backhaul capacities on the performance are also studied.

Joint User Selection and Resource Allocation for Fast Federated Edge Learning

By periodically aggregating local learning updates from edge users, federated edge learning (FEEL) is envisioned as a promising means to reap the benefit of local rich da?ta and protect users'privacy. However, the scarce wireless communication resource greatly limits the number of participated users and is regarded as the main bottleneck which hin?ders the development of FEEL. To tackle this issue, we propose a user selection policy based on data importance for FEEL system. In order to quantify the data importance of each user, we first analyze the relationship between the loss decay and the squared norm of gradi?ent. Then, we formulate a combinatorial optimization problem to maximize the learning effi?ciency by jointly considering user selection and communication resource allocation. By problem transformation and relaxation, the optimal user selection policy and resource alloca?tion are derived, and a polynomial-time optimal algorithm is developed. Finally, we deploy two commonly used deep neural network (DNN) models for simulation. The results validate that our proposed algorithm has strong generalization ability and can attain higher learning efficiency compared with other traditional algorithms.

Soft-Computing-Based Multi-constrained Intelligent Resource Allocation Algorithm with User Participation

User participation in resource allocation is the objective request to satisfy the users' individual need, gain the users' satisfaction, and maximize the resource utilization. Taking multiple constraints into account, the resource allocation is much fit to the actual situation, however, the problem complexity increases correspondingly, leading to the adoption of a soft-computing method. An improved genetic algorithm was proposed for solving the problem. The analysis and discussion of the proposed algorithm were explained in detail. It is shown that the algorithm finds near-optimal solutions at very high percentage, and it performs better than the traditional random method.

A Joint User Selection and Resources Optimization Scheduling Algorithm for CoMP

Coordinated Multi-point (CoMP) transmission technology is one of the key techniques in LTE-Advanced, Which can share the channel and data information in multiple cells, and optimize the whole system performance. In order to optimize the average sector throughput and improve the fairness of resource scheduling, a scheduling algorithm based on the resource is mainly investigated. In this algorithm, users in the network are classified firstly and then we combine the fixed resources division and flexible scheduling. System level simulation platform is set up to validate the algorithm and the results turn out that the average throughput is better compared with the traditional scheme.

Adaptive resource allocation for multi-user multi-server power-line communication OFDM systems

The bits and power allocation model of adaptive power-rate mixture for multi-user multi-server power-line communication systems was analyzed with the restrictions of maximal total power,fixed rate for each real time (RT) user,minimal rate for each non-real time (NRT) user,maximal bits and power for each subcarrier in each orthogonal frequency division multiplexing (OFDM) symbol. An algorithm of resource dynamic allocation in the first OFDM symbol of each frame and resource optimal adjustment in the latter OFDM symbol of each frame was proposed. In the first OFDM symbol of every frame,resource is firstly assigned for RT users so as to minimize their total used power until satisfying their fixed rates; secondly the remainder resource of power and subcarriers are assigned for NRT users so as to minimize their total used power until satisfying their minimal rates also; lastly the remainder resource is again assigned for NRT users according to the proportional fairness strategy so as to maximize their total assigning rate. In the latter OFDM symbol of each frame,bits are swapped and power is adjusted for every user based on the resource allocation results of anterior OFDM symbol. The algorithm is tested in the typical power-line channel scenarios and the simulation results indicate that the proposed algorithm has better performances than the classical multi-user resource allocation algorithms and it realizes the multiple aims of multi-user multi-server resource allocation for power-line communication systems.

An empirical study on user satisfaction in relation to those influencing factors for the development of database resources

In taking into full consideration of the technology acceptance model(TAM),this empirical study has made a few assumptions and also has formulated a model for the study on the level of satisfaction of database users. This research project was conducted by collecting relevant data from library users of five universities. Specifically, it aimed to measure database users' level of satisfaction and tried to find factors affecting these graduate students who are using databases regularly at their university libraries. An analysis of the collected data shows that the level of database users' satisfaction could be directly affected by the database service quality, the easiness of accessing the system and user perceived notion of usefulness of those databases that they use often. This study also found that database users' gender could be a significant factor in their perceived notion of easiness of accessing databases, and also in their perceived notion of satisfaction for their successful information retrieval operations. The frequency of accessing databases by these graduate students has an impact on users' perceived notion of easiness of database access. The users' school classifications could make a significant difference in their perceived notion on the extent of usefulness of a particular database.

Qo E-based resource allocation protocols in cognitive OFDMA network with hybrid model

A resource allocation protocol is presented in an orthogonal frequency division multiple access （OFDMA） cognitive radio （CR） network with a hybrid model which combines overlay and underlay models. Without disrupting the primary user （PU） transmissions, the overlay model allows the secondary user （SU） to utilize opportunistically the idle sub-channels; the underlay model allows the SU to occupy the same sub-channels with PU. The proposed protocols are designed for maximizing the quality of experience （QoE） of CR users and switching dynamically between the overlay and underlay models. QoE is measured by the mean opinion score （MOS） rather than simply fulfilling the physical and medium access control （MAC） layer requirements. The simulations considering the file transfer and video stream services show that the proposed resource allocation strategy is spectrum efficient.

Reputation-based joint optimization of user satisfaction and resource utilization in a computing force network

Under the development of computing and network convergence,considering the computing and network resources of multiple providers as a whole in a computing force network(CFN)has gradually become a new trend.However,since each computing and network resource provider(CNRP)considers only its own interest and competes with other CNRPs,introducing multiple CNRPs will result in a lack of trust and difficulty in unified scheduling.In addition,concurrent users have different requirements,so there is an urgent need to study how to optimally match users and CNRPs on a many-to-many basis,to improve user satisfaction and ensure the utilization of limited resources.In this paper,we adopt a reputation model based on the beta distribution function to measure the credibility of CNRPs and propose a performance-based reputation update model.Then,we formalize the problem into a constrained multi-objective optimization problem and find feasible solutions using a modified fast and elitist non-dominated sorting genetic algorithm(NSGA-II).We conduct extensive simulations to evaluate the proposed algorithm.Simulation results demonstrate that the proposed model and the problem formulation are valid,and the NSGA-II is effective and can find the Pareto set of CFN,which increases user satisfaction and resource utilization.Moreover,a set of solutions provided by the Pareto set give us more choices of the many-to-many matching of users and CNRPs according to the actual situation.

A Peer-to-Peer Resource Sharing Scheme Using Trusted Computing Technology

Facing the increasing security issues in P2P networks, a scheme for resource sharing using trusted computing technologies is proposed in this paper. We advance a RS-UCON model with decision continuity and attribute mutability to control the usage process and an architecture to illustrate how TC technologies support policy enforcement with bidirectional attestation. The properties required for attestation should include not only integrity measurement value of platform and related application, but also reputation of users and access history, in order to avoid the limitation of the existing approaches. To make a permission, it is required to evaluate both the authorization and conditions of the subject and the object in resource usage to ensure trustable resources to be transferred to trusted users and platform.

A Fairness Resource Allocation Algorithm for Coverage and Capacity Optimization in Wireless Self-Organized Network

To achieve the higher resource efficiency, Coverage and Capacity Optimization(CCO) as an important role of the network self-healing and self-optimization, has become a focus topic in wireless Self-Organized Network(SON). In this paper, a novel CCO scheme is proposed to maximize utility function of the integrated coverage and capacity. It starts with the analysis on the throughput proportional fairness(PF) algorithm and then proposes the novel Coverage and Capacity Proportional Fairness(CCPF) allocation algorithm along with a proof of the algorithms convergence. This proposed algorithm is applied in a coverage capacity optimization scheme which can guarantee the reasonable network capacity by the coverage range accommodation. Next, we simulate the proposed CCO scheme based on telecom operators' real network data and compare with three typical resource allocation algorithms: round robin(RR), proportional fairness(PF) and max C/I. In comparison of the PF algorithm, the numerical results show that our algorithm increases the average throughput by 1.54 and 1.96 times with constructed theoretical data and derived real network data respectively.

Joint resource allocation for hybrid NOMA-assisted MEC in 6G networks

Multi-access Edge Computing(MEC)is an essential technology for expanding computing power of mobile devices,which can combine the Non-Orthogonal Multiple Access(NOMA)in the power domain to multiplex signals to improve spectral efficiency.We study the integration of the MEC with the NOMA to improve the computation service for the Beyond Fifth-Generation(B5G)and the Sixth-Generation(6G)wireless networks.This paper aims to minimize the energy consumption of a hybrid NOMA-assisted MEC system.In a hybrid NOMA system,a user can offload its task during a time slot shared with another user by the NOMA,and then upload the remaining data during an exclusive time duration served by Orthogonal Multiple Access(OMA).The original energy minimization problem is non-convex.To efficiently solve it,we first assume that the user grouping is given,and focuses on the one group case.Then,a multilevel programming method is proposed to solve the non-convex problem by decomposing it into three subproblems,i.e.,power allocation,time slot scheduling,and offloading task assignment,which are solved optimally by carefully studying their convexity and monotonicity.The derived solution is optimal to the original problem by substituting the closed expressions obtained from those decomposed subproblems.Furthermore,we investigate the multi-user case,in which a close-to-optimal algorithm with lowcomplexity is proposed to form users into different groups with unique time slots.The simulation results verify the superior performance of the proposed scheme compared with some benchmarks,such as OMA and pure NOMA.

Random Access and Resource Allocation for the Coexistence of NOMA-Based and OMA-Based M2M Communications

In the future fifth generation(5G) systems,non-orthogonal multiple access(NOMA) is a promising technology that can greatly enhance the network capacity compared to orthogonal multiple access(OMA) .In this paper,we propose a novel random access(RA) and resource allocation scheme for the coexistence of NOMA-based and OMAbased machine-to-machine(M2M) communications,which aims at improving the number of successful data packet transmissions and guaranteeing the quality of service(Qo S) (e.g.,the minimum data rate requirement) for M2 M communications.The algorithm of joint user equipment(UE) paring and power allocation is proposed for the coexisting RA(i.e.,the coexistence of NOMA-based RA and OMA-based RA) .The resource allocation for the coexisting RA is investigated,thus improving the number of successful data packet transmissions by more efficiently using the radio resources.Simulation results demonstrate that the proposed RA and resource allocation scheme outperforms the conventional RA in terms of the number of successful data packet transmissions,thus is a promising technology in future M2 M communications.

Resource allocation for sum-rate maximization in NOMA-based generalized spatial modulation

The Multiple-Input Multiple-Output(MIMO)Non-Orthogonal Multiple Access(NOMA)based on Spatial Modulation(SM-MIMO-NOMA)system has been proposed to achieve better spectral efficiency with reduced radio frequency chains comparing to the traditional MIMO-NOMA system.To improve the performance of SM-MIMO-NOMA systems,we extend them to generalized spatial modulation scenarios while maintaining moderate complexity and fairness.In this paper,system spectral efficiency and transmission quality improvements are proposed by investigating a sum-rate maximization resource allocation problem that is subject to the total transmitted power,user grouping,and resource block constraints.To solve this non-convex and difficult problem,a graph-based user grouping strategy is proposed initially to maximize the mutual gains of intragroup users.An auxiliary-variable approach is then adopted to transform the power allocation subproblem into a convex one.Simulation results demonstrate that the proposed algorithm has better performance in terms of bit error rate and sum rates.

Resource Allocation Method of Device-to-Device Communication

In this paper, we study D2D (Device-to-Device) communication underlying LTE-Advanced uplink system. Since D2D communication reuses uplink resources with cellular communication in this scenario, it’s hard to avoid the inference between D2D users and cellular users. If there is no restriction for D2D communication on using the whole uplink frequency band, it will have a strong negative impact on cellular communication. In order to overcome this shortage, we propose a resource allocation method that D2D users and cellular users use orthogonal frequency resources. This method will effectively reduce the inference between both kinds of communication. However, an obvious disadvantage of this method is no effective use of uplink resources. Based on this, we propose an optimized resource allocation method that a specific cellular user will be chosen to reuse the RBs (Resource Block) of D2D users. These ideas will be taken into system-level simulation, and from the results of simulation we can see that the optimized method has the ability to improve overall system performance and limit inference for cell-edge users.

Novel Spectrum Allocation Algorithm Based on the Activities of Primary Users for Cognitive Radio Networks

In traditional cognitive radio (CR) network, most existing graph-based spectrum allocation schemes don't take on-off behavior of primary users (PUs) into consideration. In this paper, a novel spectrum allocation algorithm based on the activities of the PUs is proposed. The proposed algorithm mainly focuses on the vacant probability of licensed spectrums. And it allocates the vacant spectrums considering the interference to the neighbor cognitive nodes and the probability fairness of different cognitive nodes during the allocation. Based on the definition of the obtained benefit of cognitive node, new utility functions are formulated to characterize the system total spectrum utilization and fairness performance from the perspective of available probability. The simulation results validate that the proposed algorithm with low system communication cost is more effective than the traditional schemes when the available licensed spectrums are not sufficient, which is effective and meaningful to a real CR system with bad network condition.