This paper first formulates the optimal instantaneous resource allocation, including path selection, power allocation and subchannel scheduling with proportional fairness in MIMO, OFDMA and relay-enhanced network. The joint optimization problem is a NP-hard one with non-linear constraints. To simplify this problem, we first propose a water-filling method named 'CP-AP w PF' to adaptively allocate power only among transmitting antennas. Then, a modified iterative water-filling algorithm named 'AP-AP w PF' is proposed to achieve adaptive power allocation on each subchannel by using the Jensen's inequality. Simulation shows that 'AP-AP w PF' algorithm improves the throughput for cell-edge users, and achieve a tradeoff between maximizing system throughput and assuring individual QoS.

In order to reduce cost and improve efficiency, many data centers adopt virtualization solutions. The advent of virtualization allows multiple virtual machines hosted on a single physical server. However, this poses new challenges for resource management. Web workloads which are dominant in data centers are known to vary dynamically with time. In order to meet application's service level agreement (SLA), how to allocate resources for virtual machines has become an important challenge in virtualized server environments, especially when dealing with fluctuating workloads and complex server applications. User experience is an important manifestation of SLA and attracts more attention. In this paper, the SLA is defined by server-side response time. Traditional resource allocation based on resource utilization has some drawbacks. We argue that dynamic resource allocation directly based on real-time user experience is more reasonable and also has practical significance. To address the problem, we propose a system architecture that combines response time measurements and analysis of user experience for resource allocation. An optimization model is introduced to dynamically allocate the resources among virtual machines. When resources are insufficient, we provide service differentiation and firstly guarantee resource requirements of applications that have higher priorities. We evaluate our proposal using TPC-W and Webbench. The experimental results show that our system can judiciously allocate system resources. The system helps stabilize applications' user experience. It can reduce the mean deviation of user experience from desired targets.

In this letter, we propose a framework for scalable video multicast, which exploits the scalability of scalable video and the multiuser diversity of OFDMA systems. We further propose a resource allocation algorithm which guarantees the base-quality video for all users, and improves the transmission efficiency for users with good channel conditions.

In this paper, the resource allocation problem for proportional fairness in hybrid Cognitive Radio (CR) systems is studied. In OFDMA-based CR systems, traditional resource allocation algorithms can not guarantee proportional rates among CR users (CRU) in each OFDM symbol because the number of available subchannels might be smaller than that of CRUs in some OFDM symbols. To deal with this time-varying nature of available spectrum resource, a hybrid CR scheme in which CRUs are allowed to use subchannels in both spectrum holes and primary users (PU) bands is adopted and a resource allocation algorithm is proposed to guarantee proportional rates among CRUs with no undue interference to PUs.

In the MIMO-OFDM multiple access channel (MIMO-OFDM-MAC) uplink scenario, the base station decides the uplink parameters for multiple users based on channel state information (CSI) from each user in the system. The performance of MIMO-OFDM-MAC systems can be significantly improved by using an adaptive transmission and resource allocation schemes which consider the correlation effect of line of sight (LOS) and non line of sight (NLOS) channel conditions for different users in the system. A lot of papers have been published on resource allocation schemes for MIMO-OFDM systems. However, most of these resource allocation schemes have been considered for MIMO-OFDMA systems, where users are separated in the frequency domain and each user uses the same uplink and downlink channels in the same channel conditions. On the other hand, in the mulituser MIMO-OFDM systems, more than one user can be assigned the same frequency and channel conditions for the MIMO-OFDM broadcast channel (downlink) and MIMO-OFDM-MAC channel (uplink) are not the same. Therefore, the same resource allocation schemes for the conventional MIMO-OFDM systems can not be applied to multiuser MIMO-OFDM systems with different uplink and downlink channel conditions. Until now, most of the resource allocation schemes have been considered only for downlink MIMO-OFDM broadcast (MIMO-OFDM-BC) channel and very few papers tackle the fairness among users. Moreover, no paper considers a scheme to realize proportional data rate fairness among users in the MIMO-OFDM-MAC condition. In this paper, we propose a proportional data rate fairness resource allocation scheme with adaptive bit loading for MIMO-ODFM-MAC systems by considering the correlation effects of LOS and NLOS channel conditions in both spatial and frequency domains. Computer simulation results show that the proposed scheme can give larger system capacity while maintaining the proportional data rate fairness requirements among users in the system under the constraint of total transmit power and predetermined target BER.

We formulate the resource allocation problem to maximize the sum rate, subject to constraints on total power, bit error rate, and rate proportionality among the users for proportional resource allocation in orthogonal frequency division multiple access (OFDMA) systems. For this we propose a novel scheduling scheme based on weighted normalized user's channel state information (CSI). We then adopt the linear power distribution method and waterfilling technique to allocate power among the users and across the subcarriers respectively.

The Liquid-crystal display (LCD) overdrive technique has been utilized to reduce motion blur on a display via a reduction in the response time. However, to measure the variation of the pixel amplitudes, it is necessary to store the previous frame using a large frame memory. To downscale the frame memory, block truncation coding (BTC) is commonly employed due to the simplicity of its implementation, even if some visual artifacts may occur for image blocks with high frequency components. In this paper, we present a multimode-multilevel BTC (MBTC) technique that improves performance while maintaining simplicity. To improve the visual quality, we uniquely determine the quantization level and coding mode of each block according to the distribution of the luminance and chrominance amplitudes. For a compression ratio of 6:1, the proposed method demonstrates higher coding efficiency and overdrive performance by up to 3.81 dB in the PSNR compared to other methods.

In this letter, we propose a novel resource allocation scheme to enhance downlink system performance for orthogonal frequency division multiple access (OFDMA) and time division duplex (TDD) based femtocell networks. In the proposed scheme, the macro base station (mBS) and femto base stations (fBSs) service macro user equipments (mUEs) and femto user equipments (fUEs) in inner and outer zones in different periods to reduce interference substantially. Simulations show the proposed scheme outperforms femtocell networks with fractional frequency reuse (FFR) systems in terms of the system capacity and outage probability for mUEs and fUEs.

In this letter, a novel resource allocation method is proposed for Discrete Fourier Transform Spread Orthogonal Frequency Division Multiple Access (DFT-s-OFDMA) systems in Long Term Evolution (LTE). The proposed method is developed based on a minimal metric loss criterion and performs better than the commonly used Recursive Maximum Expansion (RME) method.

This letter studies cellular controlled short-range communication in OFDMA networks. The network needs to decide when to allow direct communication between a closely located device-to-device (D2D) pair instead of conveying data from one device to the other via the base station and when not to, in addition to subchannel and power allocation. Our goal is to maximize the total network throughput while guaranteeing the rate requirements of all users. For that purpose, we formulate an optimization problem subject to subchannel and power constraints. A scheme which combines a joint mode selection and subchannel allocation algorithm based on equal power allocation with a power reallocation scheme is proposed. Simulation results show that our proposed scheme can improve the network throughput and outage probability compared with other schemes.

This paper proposes a utility function-based scheduling algorithm for integrated real-time and non-real-time services in long-term evolution systems. The proposed utility function satisfies the target dropping ratio of real-time users; it uses the delay constraint and increases the throughput of non-real-time users by scheduling real-time users together with non-real-time users. Simulation results show that the proposed scheduling algorithm significantly improves the throughput of non-real-time users without sacrificing the quality of service of real-time users.

This paper proposes a combinatorial auction-based marketplace mechanism for cloud computing services, which allows users to reserve arbitrary combination of services at requested timeslots, prices and quality of service. The proposed mechanism helps enterprise users build workflow applications in a cloud computing environment, specifically on the platform-as-a-service, where the users need to compose multiple types of services at different timeslots. The proposed marketplace mechanism consists of a forward market for an advance reservation and a spot market for immediate allocation of services. Each market employs mixed integer programming to enforce a Pareto optimum allocation with maximized social economic welfare, as well as double-sided auction design to encourage both users and providers to compete for buying and selling the services. The evaluation results show that (1) the proposed forward/combinatorial mechanism outperforms other non-combinatorial and/or non-reservation (spot) mechanisms in both user-centric rationality and global efficiency, and (2) running both a forward market and a spot market improves utilization without disturbing advance reservations depending on the provider's policy.

We study joint rate control and resource allocation with a packet collision constraint that maximizes the total utility of secondary users in cognitive radio networks. We formulate and decouple the original optimization problem into separable subproblems and then develop an algorithm that converges to optimal rate control and resource allocation. The proposed algorithm can operate on different time-scales to reduce the amortized time complexity.

We consider Downlink (DL) scheduling for a multi-user cooperative cellular system with fixed relays. The conventional scheduling trend is to avoid interference by allocating orthogonal radio resources to each user, although simultaneous allocation of users on the same resource has been proven to be superior in, e.g., the broadcast channel. Therefore, we design a scheduler where in each frame, two selected relayed users are supported simultaneously through the Superposition Coding (SC) based scheme proposed in this paper. In this scheme, the messages destined to the two users are superposed in the modulation domain into three SC layers, allowing them to benefit from their high quality relayed links, thereby increasing the sum-rate. We derive the optimal power allocation over these three layers that maximizes the sum-rate under an equal rates' constraint. By integrating this scheme into the proposed scheduler, the simulation results show that our proposed SC scheduler provides high throughput and rate outage probability performance, indicating a significant fairness improvement. This validates the approach of simultaneous allocation versus orthogonal allocation in the cooperative cellular system.

Orthogonal frequency division multiple access (OFDMA) is adopted as a multiuser access scheme in recent cellular systems such as long term evolution (LTE) and WiMAX. In those systems, the performance improvement on cell-edge users is crucial to provide high-speed services. We propose a new resource allocation scheme based on multiple input multiple output – orthogonal frequency division multiple access – code division multiplexing (MIMO-OFDMA-CDM) to achieve performance improvements in terms of cell-edge user throughput, bit error rate, and fairness among users. The proposed scheme adopts code division multiplexing for MIMO-OFDMA and a modified proportional fairness algorithm for CDM, which enables the fairness among users and a higher throughput. The performance improvements are clarified by theoretical analysis and simulations.

In this paper, we evaluate the downlink performance of Transparent mode (T-mode) and Non-Transparent mode (NT-mode) in a two-hop cellular system based on IEEE 802.16j. In particular, we evaluate the performance in terms of the system capacity, optimal resource allocation, and outage probability using Monte Carlo simulation with various system parameters such as different Frequency Reuse Factors (FRFs) and the distance between Base Station (BS) and Relay Station (RS). To analyze the Signal to Interference and Noise Ratio (SINR) of the access and relay links, an SINR model is introduced for cellular multihop systems considering intra- and inter-cell interferences. Then, we present a method of optimal resource allocation for the Access Zone (AZ) and Relay Zone (RZ) to maximize the system capacity. Consequently, the simulation results provide an insight into choosing the appropriate RS position and optimal resource allocation. Through numerical examples, it is found that the FRFs of two and three are good choices to achieve the highest capacity with low outage in T- and NT-modes, respectively.

We investigate the problem of joint frequency and power allocation in wireless mesh networks, using a self-pricing game based solution. In traditional pricing game models, the price factor is determined from the global information of the network, which causes heavy communication overhead. To overcome this problem, we propose a self-pricing game model, in which the price factor is determined by the distributed access points processing their individual information; moreover, it is implemented in an autonomous and distributed fashion. The existence and the efficiency of Nash equilibrium (NE) of the proposed game are studied. It is shown that the proposed game based solution achieves near cooperative network throughput while it reduces the communication overhead significantly. Also, a forcing convergence algorithm is proposed to counter the vibration of channel selection. Simulation results verify the effectiveness and robustness of the proposed scheme.

This paper proposes a new resource allocation algorithm for uplink OFDMA-based cooperative relay networks, assuming multiple user nodes, multiple relay nodes and a single destination. The aim is to maximise the total sum of the users' data rates, while guaranteeing fairness among them with different QoS requirements. Assuming perfect channel state information (CSI) at the resource allocation controller, the optimisation problem is formulated such that each user is assigned a weight factor based on its QoS requirements. The ones with higher weights are given higher priorities to select their resources (relay stations and subcarriers) first. Once the required QoS is achieved for all users, the weight factor for all users is reduced to a small uniform value. The remaining resources are then allocated to the users with higher instantaneous rates in order of magnitude. The results show that the proposed algorithm outperforms the greedy and static algorithms in terms of outage probability and fairness, and at the same time outperforms Jeong's algorithm by 58% in terms of total sum rate, with an average 74% reduction in system complexity.

This paper proposes a resource allocation method based on TCP (Transmission Control Protocol) throughput for base station diversity systems. A goal of this study is to achieve high throughput wireless Internet access by utilizing multiple wireless links effectively. The conventional work showed that base station diversity techniques can improve TCP performance. However, the improvement depends on the wireless environment of the wireless terminal. The proposed resource allocation method allocates wireless links to a wireless terminal based on its estimated TCP throughput and current traffic of each base station. Our method can take account of some network protocols such as TCP and UDP (User Datagram Protocol) by measuring the current traffic of each base station. In addition, wireless links are preferentially assigned to the wireless terminal that has the largest performance improvement per wireless link. Therefore, the proposal provides better overall system performance than the previous technique.

Most existing works on resource allocation in cooperative OFDMA systems have focused on homogeneous users with same service and demand. In this paper, we focus on resource allocation in a service differentiated cooperative OFDMA system where each user has a different QoS requirement. We investigate joint power allocation, relay selection and subcarrier assignment to maximize overall system rates with consideration of QoS guarantees and service support. By introducing QoS price, this combinatorial problem with exponential complexity is converted into a convex one, and a two-level dual-primal decomposition based QoS-aware resource allocation (QARA) algorithm is proposed to tackle the problem. Simulation results reveal that our proposed algorithm significantly outperforms previous works in terms of both services support and QoS satisfaction.