Adaptive subchannel allocation in multiuser multicarrier systems

The problem of multiuser scheduling in multicarrier (MC) systems under practical physical-layer constraints and implementations is considered. Subchannel allocation is an important resource assignment issue in multiuser MC systems. The multiuser scheduler is decoupled into a multiuser selector and a subchannel allocator, which result in a sub-optimum multiuser scheduler with significantly reduced computational complexity. Given an active user subset and a channel set, the multiuser scheduling problem then refers to the optimal subchannel allocation to maximise the instantaneous system throughput subject to certain fairness constraints. Efficient adaptive algorithms are developed for optimal subchannel allocation. The extension of the algorithms for tracking the time-varying optimum, which occurs in non-stationary environments, is also addressed. Simulation results are presented to demonstrate the performance of the proposed algorithms in terms of the throughput maximisation, the fast convergence, the excellent tracking capability in time-varying environments, the achievable throughput of the proposed multiuser scheduler as well as the long-term fairness     

Throughput fairness and efficiency of link adaptation techniques in wireless networks

Link adaptation techniques aim to maximise the quality of service and resource utilisation in wireless networks. However, fairness must be taken into consideration, particularly, in low-mobility environments where the channel dynamic variation is small. The authors propose and analyse three link adaptation techniques [using joint power control (PC) and adaptive coding and modulation (ACM)] for fairness enhancement. In the first technique, called aggregate throughput maximisation with fairness constraint, the authors formulate the fairness problem as a constrained optimisation problem where the authors try to maximise the aggregate throughput subject to the throughput fairness constraint. In order to solve the optimisation problem, the authors convert the constrained optimisation problem to an unconstrained optimisation one using the penalty method. Then, the unconstrained optimisation problem is solved using the steepest descent technique. The second techniques, called individual throughput balancing, tries to equalise the individual throughput by using a higher throughput level for disadvantaged users and using a lower throughput level for advantaged users. Finally, the third technique, called adaptive virtual maximum power constraint, uses virtual maximum power cap, which is lower than the real maximum power cap. The virtual maximum power cap of each user is variable and it adapts based on the user's individual throughput to compensate disadvantaged users. The authors analyse the three proposed techniques in terms of the throughput fairness and the throughput efficiency and compare them with three basic link adaptation techniques (PC, ACM, and joint PC and ACM). The three proposed techniques are shown to be able to enhance the fairness with different degrees and with different levels of aggregate throughput degradation.     

Fast antenna subset selection algorithms for multiple-input multiple-output relay systems

The antenna subset selection technique balances the performance and hardware cost in the multipleinput multiple-output (MIMO) systems, and the problems on the antenna selection in MIMO relay systems have not been fully solved. This paper considers antenna selection on amplify-and-forward (AF) and decode-andforward (DF) MIMO relay systems to maximise capacity. Since the optimal antenna selection algorithm has high complexity, two fast algorithms are proposed. The selection criterion of the algorithm for AF relay is to maximise a lower bound of the capacity, but not the exact capacity. This criterion reduces algorithmic complexity. The algorithm for DF relay is an extension of an existing antenna subset selection algorithm for one-hop MIMO systems. The authors show the derivations of the algorithms in detail, and analyse their complexity in terms of numbers of complex multiplications. Simulation results show that the proposed algorithms for both cases achieve comparable performance to the optimal algorithm under various conditions, and have decreased complexity.     

Interference-limited resource allocation for cognitive radio in orthogonal frequency-division multiplexing networks

Efficient and fair resource allocation strategies are being extensively studied in current research in order to address the requirements of future wireless applications. A novel resource allocation scheme is developed for orthogonal frequency-division multiplexing (OFDM) networks designed to maximise performance while limiting the received interference at each user. This received interference is in essence used as a fairness metric; moreover, by defining different interference tolerances for different sets of users, the proposed allocation scheme can be exploited in various cognitive radio scenarios. As applied to the scheme, the authors investigate a scenario where two cellular OFDM-based networks operate as primary and secondary systems in the same band, and the secondary system benefits by accessing the unused resources of the primary system if additional capacity is required. The primary system benefits either by charging the secondary system for the use of its resources or by some form of reciprocal arrangement allowing it to use the secondary system's licenced bands in a similar manner, when needed. Numerical results show our interference-limited scheduling approach to achieve excellent levels of efficiency and fairness by allocating resources more intelligently than proportional fair scheduling. A further important contribution is the application of sequential quadratic programming to solve the non-convex optimisation problems which arise in such scenarios.     

A Joint Algorithm for Resource Allocation in D2D 5G Wireless Networks

With the rapid development of Internet technology, users have an increasing demand for data. The continuous popularization of traffic-intensive applications such as high-definition video, 3D visualization, and cloud computing has promoted the rapid evolution of the communications industry. In order to cope with the huge traffic demand of today’s users, 5G networks must be fast, flexible, reliable and sustainable. Based on these research backgrounds, the academic community has proposed D2D communication. The main feature of D2D communication is that it enables direct communication between devices, thereby effectively improve resource utilization and reduce the dependence on base stations, so it can effectively improve the throughput of multimedia data. One of the most considerable factor which affects the performance of D2D communication is the co-channel interference which results due to the multiplexing of multiple D2D user using the same channel resource of the cellular user. To solve this problem, this paper proposes a joint algorithm time scheduling and power control. The main idea is to effectively maximize the number of allocated resources in each scheduling period with satisfied quality of service requirements. The constraint problem is decomposed into time scheduling and power control subproblems. The power control subproblem has the characteristics of mixed-integer linear programming of NP-hard. Therefore, we proposed a gradual power control method. The time scheduling subproblem belongs to the NP-hard problem having convex-cordinality, therefore, we proposed a heuristic scheme to optimize resource allocation. Simulation results show that the proposed algorithm effectively improved the resource allocation and overcome the co-channel interference as compared with existing algorithms.     

Fair scheduling with rate control for IEEE 802.16 broadband wireless networks

Although the scheduling problem of the uplink transmission in the IEEE 802.16 broadband wireless access network is extensively discussed, most of the results are limited to the quality of service (QoS) upon throughput and delay requirement. But as in practice only limited wireless resources are made available, a fairness-based scheduling upon each connection?s QoS provides better outcomes. In this study, the authors propose a new fair uplink scheduling for real-time polling service and non-real-time polling service with the proportional sharing of excess bandwidth of the network. To implement the proposed fair scheduling that satisfies the delay requirement and full bandwidth utilisation, the authors introduce the rate control algorithm. With the proposed scheduling, we guarantee the fairness, delay requirement and full bandwidth utilisation which are not fully achieved in the existing results.     

Optimisation of limited feedback design for heterogeneous users in multi-antenna downlinks

The authors study the problem of limited channel information feedback in multiple-input single-output (MISO) broadcast channels serving heterogeneous users. The heterogeneous users have different feedback channel capacities because of their physical constraints and limitations. Our objective is to design an optimised limited feedback strategy for multiple users by using a set of multi-resolution codebooks such that, under varying and quantised channel information feedback, the multiuser system can maximise its achievable sum rate. By exploiting the receive antenna combining technique, the authors further generalise the proposed scheme to the multiuser beamforming case with multi-antenna users. Finally, the authors verify the proposed scheme by numerical methods. Simulation results show that the sum rate performance for heterogeneous users can be effectively improved by using the proposed limited feedback scheme, which is optimised according to only statistic channel information of users.     

克隆选择算法在多用户信号检测中的应用

为了减少计算复杂度,改善CDMA(Code Division Multiple Access)系统性能．将具有解决复杂组合优化问题能力的克隆选择算法(CLONALG)应用于码分多址系统多用户信号检测中．确定每一个用户传输的符号位．仿真结果表明,基于克隆选择算法的多用户检测器不仅具有克服多址干扰,抗“远-近效应”的能力,而且通过调整算法参数可以在检测时间和系统平均误码性能间进行均衡,与最佳多用户检测器相比明显地降低了计算复杂度．     

Quality of service supporting downlink scheduling scheme in worldwide interoperability for microwave access wireless access systems

IEEE 802.16 is a standardisation for a broadband wireless access in metropolitan area networks (MAN). Since the IEEE 802.16 standard defines the concrete quality of service (QoS) requirement, a scheduling scheme is necessary to achieve the QoS requirement. Many scheduling schemes are proposed with the purpose of throughput optimisation and fairness enhancement, however, few scheduling schemes support the delay requirement. In this study, the authors propose a new scheduling scheme reflecting the delay requirement. Specifically, the authors add the delay requirement term in the proportional fair scheduling scheme and the scheduling parameters are optimised with respect to the QoS requirement. Therefore the QoS requirement is achieved without the excessive resource consumption.     

Distributed algorithm for optimal sequence and power allocation in uplink code division multiple access systems

An alternative approach is presented for centralised algorithms that design optimal sequences and powers under quality of service constraints in the uplink of a code division multiple access systems. The authors propose a distributed algorithm, where each user designs its optimal codeword in such a way to transmit minimum power, based on certain feedback information sent from the base station. The authors define the user cost function, which is the user power written as a convex function of user codewords for a defined signal-to-interference plus noise ratio target. For the constrained optimisation problem, optimal codewords are designed based on feasible direction method and the optimal user powers are the minimum values of the user cost functions. For the optimal configuration, the matched filter employed at receiver will have the same performance as the minimum mean squared error filter. Even if the optimal user powers are unique, the optimal codewords do not correspond to a unique allocation, but rather to an entire class of codewords that can be related by unitary transformations. The algorithm works properly in the presence of multiple access interference with white or coloured additive noise and requires no ordering. The proposed algorithm is analysed and illustrated with numerical examples obtained from simulations.     

Fair power control for wireless ad hoc networks using game theory with pricing scheme

Resource allocation in wireless ad hoc networks is usually modelled in a non-cooperative game theoretic framework with the objective of maximising individual utility. However, the selfishness of autonomous users under such framework may lead to throughput unfairness which only benefits certain users. To alleviate this unfairness problem, the authors propose a payment-based power control scheme using game theory where each user announces a set of price coefficients that reflects different compensations paid by other users for the interference they produce. Users who generate higher interference are required to pay more by transmitting at a lower power to give other users a fairer chance of sharing the throughput.Without any incentive to play fairly, users could misbehave by broadcasting high price coefficients to force other users to transmit at a lower power. The authors treat this problem casting it into a price game which resembles a Prisoner's Dilemma game. Users who play this game iteratively will behave cooperatively and broadcast the price coefficients truthfully. Together with analytical proof, the proposed approach is shown to converge to Nash equilibrium where at this point it is able to provide a fairer throughput share among users at the expense of a slight loss in total throughput.     

卷积编码DS/CDMA系统中的迭代多用户接收器

提出了一种用于卷积编码DS/CDMA系统的迭代多用户接收器。该接收器由一个软输入/软输出（SISO）的判决反馈多用户检测器和一组单用户SISO信道译码器组成，每次迭代过程中，多用户检测器和信道译码器都输出外信息作为下一次迭代的光验信息，仿真结果表明，这种接收器能使远近效应影响下弱用户的比特误码性能接受单用户限。     

Scheduling and co-ordination of multi-suppliers single-warehouse-operator single-manufacturer supply chains with variable production rates and storage costs

We study a supply chain scheduling and co-ordination problem comprising multiple suppliers, a single warehouse operator, a single manufacturer, and multiple retailers, where the supply chain has limited production capacity that can take only some of the orders from the retailers. For a decentralised supply chain, the manufacturer is a decision maker that selects the orders and aims to maximise its own profit, where the profit is a function of the order storage time and storage quantity, order sequence-dependent weighted storage costs, and idle time of the orders. On the other hand, for a centralised supply chain, a supply chain co-ordinator exists that aims to maximise the profit of the whole supply chain and allocates the profit among the supply chain members. We first formulate the problem as a two-machine common-due-window flow shop scheduling problem. We then develop a theorem and two algorithms to solve the optimal scheduling problems in both the decentralised and centralised supply chains. With these results, we develop a method that can achieve channel co-ordination based on a profit sharing rule, together with an increase in the production rates and a decrease in the storage costs.     

Multi-rate power-controlled link scheduling for mesh broadband wireless access networks

The problem of multi-rate power-controlled collision-free scheduling in spatial time division multiple access (STDMA) wireless mesh networks is formulated as a mathematical program utilising cross layer information. As these mixed integer linear programs are intractable (NP-hard problems), optimal collision-free schedules can be found only for topologies consisting of a few nodes. To this end, approximation algorithms that are based on linear programming relaxation and randomised rounding are studied. The proposed framework, which aims to maximise the spatial timeslot reuse under predefined signal-to-interference noise ratio thresholds, is suitable for providing centralised scheduling in the mesh mode of the IEEE 802.16 standard. Performance aspects of the approximation algorithms under different scenarios are investigated.     

Job ordering and management of wearing tools

This article considers a scheduling problem arising in flexible manufacturing systems. It is assumed that a computer numerical control machine processes a set of jobs with a set of wearing tools. The tool magazine of the machine has a given capacity and each job requires some subset of tools. The goal is to minimize the average completion time of the jobs by choosing their processing order and the tool management decisions intelligently. Previous studies concerning this problem have either omitted the tool wearing or assumed only one tool type. This study gives a mathematical formulation for the problem when the tool lifetimes are deterministic. It is shown that problems of a practical size cannot be solved to optimality within a reasonable time. Therefore genetic algorithms and local search methods are considered to resolve the problem. When the solutions of these new algorithms are compared against the optimal solutions and lower bounds, they are nearly optimal.     

Re-entrant flow shop scheduling problem with time windows using hybrid genetic algorithm based on auto-tuning strategy

The re-entrant flow shop scheduling problem considering time windows constraint is one of the most important problems in hard-disc drive (HDD) manufacturing systems. In order to maximise the system throughput, the problem of minimising the makespan with zero loss is considered. In this paper, evolutionary techniques are proposed to solve the complex re-entrant scheduling problem with time windows constraint in manufacturing HDD devices with lot size. This problem can be formulated as a deterministic Fm?|?fmls, rcrc, temp?|?C_max problem. A hybrid genetic algorithm was used for constructing chromosomes by checking and repairing time window constraints, and improving chromosomes by a left-shift heuristic as a local search algorithm. An adaptive hybrid genetic algorithm was eventually developed to solve this problem by using fuzzy logic control in order to enhance the search ability of the genetic algorithm. Finally, numerical experiments were carried out to demonstrate the efficiency of the developed approaches.     

Mixed Self-adapting GA Optimal Scheduling Algorithm for a Multiple Resource Job-shop

With an aim at the job-shop scheduling problem of multiple resource constraints, this paper presents mixed self-adapting Genetic Algorithm （ GA ） , and establishes a job-shop optimal scheduling model of multiple resource constraints based on the effect of priority scheduling rules in the heuristic algorithm upon the scheduling target. New coding regulations or rules are designed. The sinusoidal function is adopted as the self-adapting factor, thus making cross probability and variable probability automatically change with group adaptability in such a way as to overcome the shortcoming in the heuristic algorithm and common GA, so that the operation efficiency is improved. The results from real example simulation and comparison with other algorithms indicate that the mixed self-adapting GA algorithm can well solve the job-shop optimal scheduling problem under the constraints of various kinds of production resources such as machine-tools and cutting tools.     

Management of price uncertainty in short-term generation planning

Price uncertainty is faced by gencos in the scheduling of their units in competitive power markets. the proper way to deal with the uncertainty depends on the risk preference of the genco. two important means to manage the price uncertainty are (i) suitable flexible bids and (ii) the use of hedging tools such as forward contracts. the influence of these factors in the genco's short-term generation planning and the corresponding profit performances is studied. the market price is represented by lognormal distribution, genco risk behaviour is represented by exponential utility functions, the bid functions are taken to be flexible and simple contracts for hedging are assumed available. the unit commitment problem is combined with the hedging problem to obtain the optimal solution. formulation of the problem to maximise profit in the spot market and its extension to incorporate the risk behaviour of the gencos and the forward contacts for hedging is presented. solution method based on genetic algorithms is implemented in matlab. it is observed through numerical examples that the flexible outputs and the forward contracts can be used to hedge against price risks to achieve desired profit performance according to gencos' risk behaviour.     

CDMA移动通信系统中基于子空间的信号检测

讨论了线性空间中的子空间投影理论及其在CDMA（码分多址）移动通信系统中的应用,提出了已知各用户特征序列条件下基于子空间斜投影的多用户检测技术以及仅知目标用户特征序列条件下基于伪特征分解和子空间正交投影的盲检测技术,并通过计算机仿真进行了比较研究。     

A multiple-criteria real-time scheduling approach for multiple-load carriers subject to LIFO loading constraints

This paper studies the problem of scheduling a multiple-load carrier subject to last-in-first-out loading constraints in an automobile assembly line. Two scheduling criteria, the throughput of the assembly line and the material handling distance, are considered in order to maximise the profit of the assembly line. Different from other studies, the product mix and weights of the scheduling criteria are considered to be variable. A scheduling approach is proposed for the problem. At moments when the product mix or weights of the scheduling criteria change, the scheduling approach can select an appropriate rule from a set of given rules. In this study, the proposed approach is compared with other approaches by simulation in order to verify the performance of the proposed approach. The results indicate that, when the product mix and weights of the scheduling criteria are variable, the proposed scheduling approach outperforms other approaches.