Improved reduced-complexity bit and power allocation algorithms for multicarrier systems

Based on the iterative bit-filling procedure, a computationally efficient bit and power allocation algorithm is presented. The algorithm improves the conventional bit-filling algorithms by maintaining only a subset of subcarriers for computation in each iteration, which reduces the complexity without any performance degradation. Moreover, a modified algorithm with even lower complexity is developed, and equal power allocation is introduced as an initial allocation to accelerate its convergence. Simulation results show that the modified algorithm achieves a considerable complexity reduction while causing only a minor drop in performance.

A Robust Resource Allocation Scheme for Device-to-Device Communications Based on Q-Learning

One of the most effective technology for the 5G mobile communications is Device-to-device(D2D)communication which is also called terminal pass-through technology.It can directly communicate between devices under the control of a base station and does not require a base station to forward it.The advantages of applying D2D communication technology to cellular networks are:It can increase the communication system capacity,improve the system spectrum efficiency,increase the data transmission rate,and reduce the base station load.Aiming at the problem of co-channel interference between the D2D and cellular users,this paper proposes an efficient algorithm for resource allocation based on the idea of Q-learning,which creates multi-agent learners from multiple D2D users,and the system throughput is determined from the corresponding state-learning of the Q value list and the maximum Q action is obtained through dynamic power for control for D2D users.The mutual interference between the D2D users and base stations and exact channel state information is not required during the Q-learning process and symmetric data transmission mechanism is adopted.The proposed algorithm maximizes the system throughput by controlling the power of D2D users while guaranteeing the quality-of-service of the cellular users.Simulation results show that the proposed algorithm effectively improves system performance as compared with existing algorithms.

Dynamic bandwidth allocation algorithm for full-band utilization

To improve and optimize the bandwidth utilization for multi-service packet transporting system, a kind of Dynamic Full Bandwidth Utilized （DFBU） allocation algorithm allowing a single link to use far beyond its fair share bandwidth is presented. Three important parameters as the bound on max and minimum bandwidth, the maximum packet delay and the minimum bandwidth utilization are discussed and analyzed. Results of experiments show that the DFBU-algorithm is capable of making a single link in the system use all the spare bandwidth （up to full-bandwidth） while the performance of fairness and QoS requirement is still guaranteed.

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.

Register Allocation Compilation Technique for ASIP in 5G Micro Base Stations

The currently available compilation techniques are for general computing and are not optimized for physical layer computing in 5G micro base stations.In such cases,the foreseeable data sizes and small code size are application specific opportunities for baseband algorithm optimizations.Therefore,the special attention can be paid,for example,the specific register allocation algorithm has not been studied so far.The compilation for kernel sub-routines of baseband in 5G micro base stations is our focusing point.For applications of known and fixed data size,we proposed a compilation scheme of parallel data accessing,while operands can be mainly allocated and stored in registers.Based on a small register group(48×32b),the target of our compilation scheme is the optimization of baseband algorithms based on 4×4 or smaller matrices,maximizing the utilization of register files,and eliminating the extra register data exchanging.Meanwhile,when data is allocated into register files,we used VLIW(Very Long Instruction Word)machine to hide the time of data accessing and minimize the cost of data accessing,thus the total execution time is minimum.Experiments indicate that for algorithms with small data size,the cost of data accessing and extra addressing can be minimized.

DYNAMIC BANDWIDTH ALLOCATION ALGORITHM UTILIZING FULL BAND

A kind of Dynamic Full Bandwidth Utilized (DFBU) allocation algorithm is introduced. This algorithm allows a single link to use bandwidth far beyond its fair share bandwidth in a multi-service packet transporting system. Three important parameters as the bound on maximum and minimum bandwidth, the maximum packet delay and the minimum band width utilization are discussed and analyzed. Results of experiments show that the DFBU-algorithm is capable of making a single link in the system to use all the spare bandwidth (up to full-bandwidth) while the performance of fairness and QoS requirement is still guaranteed.

AVirtual Cloud Storage Architecture for Enhanced Data Security

The sensitive data stored in the public cloud by privileged users,such as corporate companies and government agencies are highly vulnerable in the hands of cloud providers and hackers.The proposed Virtual Cloud Storage Archi-tecture is primarily concerned with data integrity and confidentiality,as well as availability.To provide confidentiality and availability,thefile to be stored in cloud storage should be encrypted using an auto-generated key and then encoded into distinct chunks.Hashing the encoded chunks ensured thefile integrity,and a newly proposed Circular Shift Chunk Allocation technique was used to determine the order of chunk storage.Thefile could be retrieved by performing the opera-tions in reverse.Using the regenerating code,the model could regenerate the missing and corrupted chunks from the cloud.The proposed architecture adds an extra layer of security while maintaining a reasonable response time and sto-rage capacity.Experimental results analysis show that the proposed model has been tested with storage space and response time for storage and retrieval.The VCSA model consumes 1.5x(150%)storage space.It was found that total storage required for the VCSA model is very low when compared with 2x Replication and completely satisfies the CIA model.The response time VCSA model was tested with different sizedfiles starting from 2 to 16 MB.The response time for storing and retrieving a 2 MBfile is 4.96 and 3.77 s respectively,and for a 16 MBfile,the response times are 11.06 s for storage and 5.6 s for retrieval.

Randomized MILP framework for Securing Virtual Machines from Malware Attacks

Cloud computing involves remote server deployments with public net-work infrastructures that allow clients to access computational resources.Virtual Machines(VMs)are supplied on requests and launched without interactions from service providers.Intruders can target these servers and establish malicious con-nections on VMs for carrying out attacks on other clustered VMs.The existing system has issues with execution time and false-positive rates.Hence,the overall system performance is degraded considerably.The proposed approach is designed to eliminate Cross-VM side attacks and VM escape and hide the server’s position so that the opponent cannot track the target server beyond a certain point.Every request is passed from source to destination via one broadcast domain to confuse the opponent and avoid them from tracking the server’s position.Allocation of SECURITY Resources accepts a safety game in a simple format as input andfinds the best coverage vector for the opponent using a Stackelberg Equilibrium(SSE)technique.A Mixed Integer Linear Programming(MILP)framework is used in the algorithm.The VM challenge is reduced by afirewall-based controlling mechanism combining behavior-based detection and signature-based virus detection.The pro-posed method is focused on detecting malware attacks effectively and providing better security for the VMs.Finally,the experimental results indicate that the pro-posed security method is efficient.It consumes minimum execution time,better false positive rate,accuracy,and memory usage than the conventional approach.

Hybrid orthogonal and non-orthogonal pilot distribution based channel estimation in massive MIMO system

How to obtain accurate channel state information(CSI)at the transmitter with less pilot overhead for frequency division duplexing(FDD) massive multiple-input multiple-output(MIMO)system is a challenging issue due to the large number of antennas. To reduce the overwhelming pilot overhead, a hybrid orthogonal and non-orthogonal pilot distribution at the base station(BS),which is a generalization of the existing pilot distribution scheme,is proposed by exploiting the common sparsity of channel due to the compact antenna arrangement. Then the block sparsity for antennas with hybrid pilot distribution is derived respectively and can be used to obtain channel impulse response. By employing the theoretical analysis of block sparse recovery, the total coherence criterion is proposed to optimize the sensing matrix composed by orthogonal pilots. Due to the huge complexity of optimal pilot acquisition, a genetic algorithm based pilot allocation(GAPA) algorithm is proposed to acquire optimal pilot distribution locations with fast convergence. Furthermore, the Cramer Rao lower bound is derived for non-orthogonal pilot-based channel estimation and can be asymptotically approached by the prior support set, especially when the optimized pilot is employed.

AN IMPROVED EPON UPSTREAM TRANSMISSION SCHEME WITH ZERO INTER-PERIOD IDLE LOSS

Existing Ethernet Passive Optical Network(EPON) Dynamic Bandwidth Allocation(DBA) algorithms suffer from the disadvantage of idle time loss,which lower the upstream bandwidth utili-zation.This letter proposes an improved upstream transmission scheme with idle-time eliminating mechanism.Theoretical analysis and numerical calculation prove that the improved scheme can ef-fectively eliminate the idle time and enhance the efficiency of upstream link utilization.Simulation results have shown that the bandwidth utilization can be raised up to 15% in heavy-load scenarios while the time delay performance of Assured Forwarding(AF) and Best Effort(BE) services are improved simultaneously.

Heuristic Algorithm for Minimizing the Electricity Cost of Smart House

This framework proposes a heuristic algorithm based on LP （linear programming） for optimizing the electricity cost in large residential buildings, in a smart grid environment. Our heuristic tackles large multi-objective energy allocation problem （large number of appliances and high time resolution）. The primary goal is to reduce the electricity bills, and discomfort factor. Also, increase the utilization of domestic renewable energy, and reduce the running time of the optimization algorithm. Our heuristic algorithm uses linear programming relaxation, and two rounding strategies. The first technique, called CR （cumulative rounding）, is designed for thermostatic appliances such as air conditioners and electric heaters, and the second approach, called MCR （minimum cost rounding）, is designed for other interruptible appliances. The results show that the proposed heuristic algorithm can be used to solve large MILP （mixed integer linear programming） problems and gives a decent suboptimal solution in polynomial time.

Uncertain multiobjective redundancy allocation problem of repairable systems based on artificial bee colony algorithm

Based on the uncertainty theory, this paper is devoted to the redundancy allocation problem in repairable parallel-series systems with uncertain factors, where the failure rate, repair rate and other relative coefficients involved are considered as uncertain variables. The availability of the system and the corresponding designing cost are considered as two optimization objectives. A crisp multiobjective optimization formulation is presented on the basis of uncertainty theory to solve this resultant problem. For solving this problem efficiently, a new multiobjective artificial bee colony algorithm is proposed to search the Pareto efficient set, which introduces rank value and crowding distance in the greedy selection strategy, applies fast non-dominated sort procedure in the exploitation search and inserts tournament selection in the onlooker bee phase. It shows that the proposed algorithm outperforms NSGA-II greatly and can solve multiobjective redundancy allocation problem efficiently. Finally, a numerical example is provided to illustrate this approach.

An Improved Dynamic Bandwidth Allocation Algorithm for Ethernet PON

This paper proposes an improved Dynamic Bandwidth Allocation (DBA) algorithm for EPON, which combines static and traditional dynamic allocation schemes. Simulation result shows that the proposed algorithm may effectively improve the performance of packet delay.

Optimal Rate Allocation Algorithm for Multiple Source Video Streaming

Video streaming is one of the most important applications used in the best-effort Internet. This paper presents a new scheme for multiple source video streaming in which the traditional fine granular scal-able coding was rebuilt into a multiple sub-streams based transmission model. A peak signal to noise ratio based stream rate allocation algorithm was then developed based on the transmission model. In tests, the algorithm performance is about 1 dB higher than that of a uniform rate allocation algorithm. Therefore, this scheme can overcome bottlenecks along a single link and smooth jitter to achieve high quality and stable video.