In this paper, we focus on resource allocation schemes for minimizing the energy consumption of subscriber stations (SSs) in uplink flows of the IEEE 802.16 OFDMA systems. The resource allocation schemes assign subcarriers, powers, and data rates to each SS based on the measured signal to noise ratio (SNR) of the uplink channel and predefined modulation and coding scheme as system parameters. Previous research efforts to optimize resource allocation focus on the rate and throughput maximizations, and develop suboptimal heuristic algorithms. However, this paper intends to reduce the energy consumption of SSs by considering the relationship between energy efficiency and resource allocation. In order to clearly formulate the relationship, we use the Multiple Choice Knapsack (MCK) problem, which is proved to be an NP-hard problem. We propose two heuristic schemes to solve the NP-hard problem, which adaptively use the modulation and coding scheme, defined in the IEEE 802.16 OFDMA systems to minimize the required transmission power of each SS. Our simulation results show that the proposed schemes can reduce the energy consumption by up to 53% compared to the channel state information (CSI) scheme, which determines the modulation and coding level only considering the channel state information.

In this paper, we focus on the adaptive resource allocation issue for uplink OFDMA systems. The resources are allocated according to a proportional fairness criterion, which can strike an alterable balance between fairness and efficiency. Optimization theory is used to analyze the multi-constraint resource allocation problem and some heuristic characteristics about the optimal solution are obtained. To deal with the cohesiveness of the necessary conditions, we resort to bargaining theory that has been deeply investigated in game theory. Firstly, we summarize some assumptions about bargaining theory and show their similarities with the resource allocation process. Then we propose a priority-ranked bargaining model, whose primary contribution is applying the economic thought to the resource allocation process. A priority-ranked bargaining algorithm (PRBA) is subsequently proposed to permit the base station to auction the subcarriers one by one according to the users' current priority. By adjusting the predefined rate ratio flexibly, PRBA can achieve different degrees of fairness among the users' capacity. Simulation results show that PRBA can achieve similar performance of the max-min scheme and the NBS scheme in the case of appropriate predefined rate ratio.

In this paper, we analyze the extended real-time Polling Service (ertPS) algorithm in IEEE 802.16e systems, which is designed to support Voice-over-Internet-Protocol (VoIP) services with data packets of various sizes and silence suppression. The analysis uses a two-dimensional Markov Chain, where the grant size and the voice packet state are considered, and an approximation formula for the total throughput in the ertPS algorithm is derived. Next, to improve the performance of the ertPS algorithm, we propose an enhanced uplink resource allocation algorithm, called the e 2rtPS algorithm, for VoIP services in IEEE 802.16e systems. The e 2rtPS algorithm considers the queue status information and tries to alleviate the queue congestion as soon as possible by using remaining network resources. Numerical results are provided to show the accuracy of the approximation analysis for the ertPS algorithm and to verify the effectiveness of the e 2rtPS algorithm.

The 3GPP LTE-Advanced has been attracting much attention recently, where the channel bandwidth would be beyond the maximum bandwidth of LTE, 20 MHz. In LTE, single carrier-frequency division multiple access (SC-FDMA) was accepted as the uplink access scheme due to its advantage of very low cubic metric (CM). For LTE-A wideband transmission, multicarrier access would be more effective than single carrier access to make use of multi-user diversity and can maintain the physical channel structure of LTE, where the control information is transmitted on the edges of each 20 MHz. In this paper, we discuss the access schemes in bandwidth under 20 MHz as well as over 20 MHz. In the case of bandwidth under 20 MHz, we propose the access schemes allowing discontinuous resource allocation to enhance average throughput while maintaining cell-edge user throughput, that is, DFT-spread-OFDM with spectrum division control (SDC) and adaptive selection of SC-FDMA and OFDM (SC+OFDM). The number of discontinuous spectrums is denoted as spectrum division (SD). For DFT-S-OFDM, we define a parameter max SD as the upper limit of SD. We evaluate our proposed schemes in bandwidth under 20 MHz and find that SC+OFDM as well as SDC with common max SD or UE-specific max SD can improve average throughput while their cell-edge user throughput can approach that of SC-FDMA. In the case of bandwidth over 20 MHz, we consider key factors to decide a feasible access scheme for aggregating several 20 MHz-wide bands.

In this paper, we propose a subcarrier resource allocation algorithm for managing the video quality degradation for multiuser orthogonal frequency division multiplex (OFDM) systems. The proposed algorithm exploits the unequal importance existing in different picture types for video coding and the diversity of subcarriers for multiuser systems. A model-based performance metric is first derived considering the error concealment and error propagation properties of the H.264 video coding structure. Based on the information on video quality enhancement existing in a packet to be transmitted, we propose the distortion management algorithm for balancing the subcarriers and power usages for each user and minimizing the overall video quality degradation. In the simulation results, the proposed algorithm demonstrates a more gradual video quality degradation for different numbers of users compared with other resource allocation schemes.

The integration of multihop relays with orthogonal frequency-division multiple access (OFDMA) cellular infrastructures can meet the growing demands for better coverage and higher throughput. Resource allocation in the OFDMA two-hop relay system is more complex than that in the conventional single-hop OFDMA system. With time division between transmissions from the base station (BS) and those from relay stations (RSs), fixed partitioning of the BS subframe and RS subframes can not adapt to various traffic demands. Moreover, single-hop scheduling algorithms can not be used directly in the two-hop system. Therefore, we propose a semi-distributed algorithm called ASP to adjust the length of every subframe adaptively, and suggest two ways to extend single-hop scheduling algorithms into multihop scenarios: link-based and end-to-end approaches. Simulation results indicate that the ASP algorithm increases system utilization and fairness. The max carrier-to-interference ratio (Max C/I) and proportional fairness (PF) scheduling algorithms extended using the end-to-end approach obtain higher throughput than those using the link-based approach, but at the expense of more overhead for information exchange between the BS and RSs. The resource allocation scheme using ASP and end-to-end PF scheduling achieves a tradeoff between system throughput maximization and fairness.

We propose Adaptive Resource Allocation for the Partial Block MC-CDMA (ARA-PB/MC-CDMA) system. The ARA-PB/MC-CDMA system aims to improve total throughput performance and frequency efficiency across various channel conditions. It adaptively changes the number of blocks to improve the throughput performance and frequency efficiency according to the Signal to Interference Ratio (SIR). Therefore, the proposed system supports various Quality of Service (QoS) requirements for various SIR values.

In this paper, we propose a computationally efficient method to solve the large dimension Adaptive Subcarrier Assignment and Bit Allocation (ASABA) problem of multiuser orthogonal frequency division multiplexing system. Our algorithm consists of three Ordinal Optimization (OO) stages to find a good enough solution to the considered problem. First of all, we reformulate the considered problem to separate it into subcarrier assignment and bit allocation problem such that the objective function of a feasible subcarrier assignment pattern is the corresponding optimal bit allocation for minimizing the total consumed power. Then in the first stage, we develop an approximate objective function to evaluate the performance of a subcarrier assignment pattern and use a genetic algorithm to search through the huge solution space and select s best subcarrier assignment patterns based on the approximate objective values. In the second stage, we employ an off-line trained artificial neural network to estimate the objective values of the s subcarrier assignment patterns obtained in stage 1 and select the l best patterns. In the third stage, we use the exact objective function to evaluate the l subcarrier assignment patterns obtained in stage 2, and the best one associated with the corresponding optimal bit allocation is the good enough solution that we seek. We apply our algorithm to numerous cases of large-dimension ASABA problems and compare the results with those obtained by four existing algorithms. The test results show that our algorithm is the best in both aspects of solution quality and computational efficiency.

In real-time embedded systems, there is requirement for adapting both energy consumption and Quality of Services (QoS) of tasks according to their importance. This paper proposes an adaptive power-aware resource allocation method to resolve a trade-off between the energy consumption and QoS levels according to their importance with guaranteeing fairness. The proposed resource allocator consists of two components: the total resource optimizer to search for the optimal total resource and QoS-fairness-based allocator to allocate resource to tasks guaranteeing the fairness. These components adaptively achieve the optimal resource allocation formulated by a nonlinear optimization problem with the time complexity O(n) for the number of tasks n even if tasks' characteristics cannot be identified precisely. The simulation result shows that the rapidness of the convergence of the resource allocation to the optimal one is suitable for real-time systems with large number of tasks.

In this letter, we propose an adaptive transmission method for an OFDMA system supporting both band-AMC and diversity modes in a frame, simultaneously. In the proposed method, users are classified into the two groups preferring the band-AMC mode or the diversity mode based on their channel parameters. Then the BS performs resource allocation to maximize the throughput. It is observed that the proposed adaptive transmission method can reduce the feedback overhead with negligible performance loss.

A consensus problem has been studied in many fundamental and application fields to analyze coordinated behavior in multi-agent systems. In a consensus problem, it is usually assumed that a state of each agent is scalar and all agents have an identical linear consensus protocol. We present a consensus problem of multi-agent systems where each agent has multiple state variables and a performance value evaluated by a nonlinear performance function according to its current state. We derive sufficient conditions for agents to achieve consensus on the performance value using an algebraic graph theory and the mean value theorem. We also consider an application of a performance consensus problem to resource allocation in soft real-time systems so as to achieve a fair QoS (Quality of Service) level.

We present a TCP flow level performance evaluation on error rate aware scheduling algorithms in Evolved UTRA and UTRAN networks. With the introduction of the error rate, which is the probability of transmission failure under a given wireless condition and the instantaneous transmission rate, the transmission efficiency can be improved without sacrificing the balance between system performance and user fairness. The performance comparison with and without error rate awareness is carried out dependant on various TCP traffic models, user channel conditions, schedulers with different fairness constraints, and automatic repeat request (ARQ) types. The results indicate that error rate awareness can make the resource allocation more reasonable and effectively improve the system and individual performance, especially for poor channel condition users.

In the subcarrier and power allocation schemes in Multi-Input Multi-Output and Orthogonal Frequency Division Multiple Access (MIMO-OFDMA) systems, only equal fairness among users has been considered and no scheme for proportional data rate fairness has been considered. In this paper, a subcarrier, bit and power allocation scheme is proposed to maximize the total throughput under the constraints of total power and proportional data rate fairness among users. In the proposed scheme, joint subchannel allocation and adaptive bit loading is firstly performed by using singular value decomposition (SVD) of channel matrix under the constraint of users' data throughput requirements, and then adaptive power loading is applied. Simulation results show that effective performance of the system has been improved as well as each throughput is proportionally distributed among users in MIMO-OFDMA systems.

Recently, there has been extensive research on resource allocation schemes for multicast services that would satisfy the requirements of multimedia traffic. Although several schemes have been proposed to improve the performance of individual multicast groups, it is not easy to achieve both throughput efficiency and user fairness. In this study, we propose a new multicast scheduling scheme for achieving proportional fair (PF) allocation in wireless cellular systems. The basic idea of PF is to schedule the user whose corresponding instantaneous channel quality is the highest relative to the average channel condition over a given time scale. We first extend the PF metric to the extent that the scheduler can reflect the user's varying channel gain, and fairness, not only in the unicast case, but also in multicast transmissions. A multicast PF scheme maximizes the summation of the logarithmic average rate of all multicasting users. Thus, it improves the fairness to mobile users when compared to max-rate allocation, because the logarithmic rate gives more weight to lower rate users, while achieving high throughput. Moreover, the proposed scheme is less complex than max-rate allocation.

Mesh networking technologies for both high-rate and low-rate wireless personal area networks (WPANs) are under development by several standardization bodies. They are considering to adopt distributed TDMA MAC protocols to provide seamless user mobility as well as a good peer-to-peer QoS in WPAN mesh. It has been, however, pointed out that the absence of a central controller in the wireless TDMA MAC may cause a severe performance degradation: e.g., fair allocation, service differentiation, and admission control may be hard to achieve or can not be provided. In this paper, we suggest a new framework of resource allocation for the distributed MAC protocols in WPANs. Simulation results show that our algorithm achieves both a fair resource allocation and flexible service differentiations in a fully distributed way for mesh WPANs where the devices have high mobility and various requirements. We also provide an analytical modeling to discuss about its unique equilibrium and to compute the lengths of reserved time slots at the stable point.

In this letter, we propose an efficient resource allocation scheme that improves the system performance by reducing the signaling overhead for voice over Internet protocol (VoIP) services in the IEEE 802.16e OFDMA system. We theoretically analyze the performance of the proposed allocation scheme and carry out a computer simulation to evaluate the FA scheme.

Simple-relay aided resource allocation (SRARA) schemes are incorporated with throughput guarantee scheduling (TGS) in IEEE 802.16 type time division duplex--orthogonal frequency division multiple access (TDD-OFDMA) downlink in order to enhance service coverage, where the amount of resources at each relay is limited due to either its available power which is much smaller than base station (BS) power or the required overhead. The performance of SRARA schemes is evaluated with both proportional fair (PF) and TGS schedulers at 64 kbps and 128 kbps user throughput requirements when total RS power is set to 500 mW or 1 W. For SRARA with RSs of relatively lower power (500 mW), schemes that put total power into only one subchannel offer larger coverage than when both subchnnels are used with equal power allocation, while the RS with evenly power-allocated two subchannels could provide larger coverage gain for a relatively higher power (1 W). Depending upon the target throughputs it is shown which of the relay scheme or scheduler design would play more important role in improving coverage. In a lower target (64 kbps), more improvement comes from relay scheme rather than scheduler design. For a relatively higher level (128 kbps), it comes from scheduler design rather than relay due to the fact that simple relay can't help using strictly limited amount of resources.

The time division duplex cellular system can support various downlink and uplink traffic ratios by setting the downlink and uplink transmission periods appropriately. However, it causes severe co-channel interference problem when some cells are active in the downlink while the others are in the uplink [2]. To mitigate this problem, a resource allocation scheme combined with sectorization is proposed for orthogonal frequency division multiple access. Simulations demonstrate that the proposed scheme improves both spectral efficiency and outage performance compared to the conventional allocation schemes.

In real-time systems, deadline misses of the tasks cause a degradation in the quality of their results. To improve the quality, we have to allocate CPU utilization for each task adaptively. Recently, Buttazzo et al. address a feedback scheduling algorithm, which dynamically adjusts task periods based on the current workloads by applying a linear elastic task model. In their model, the utilization allocated to each task is treated as the length of a linear spring and its flexibility is described by a constant elastic coefficient. In this paper, we first consider a nonlinear elastic task model, where the elastic coefficient depends on the utilization allocated to the task. We propose a simple iterative method for calculating the desired allocated resource and derive a sufficient condition for the convergence of the method. Next, we apply the nonlinear elastic model to an adaptive fair sharing controller. Finally, we show the effectiveness of the proposed method by computer simulation.

Viable techniques such as dynamic voltage scaling (DVS) provide a new design technique to balance system performance and energy saving. In this paper, we extend previous works on task assignment problems for a set of linear-pipeline tasks over a set of processors. Different from previous works, we revisit the problems with two additional system factors: deadline and energy-consumption, which are key factors in real-time and power-aware computation. We propose an O(nm2) time complexity algorithm to determine optimal task-assignment and speed-setting schemes leading to minimal energy consumption, for a given set of m real-time tasks running on n identical processors (with or without DVS supports). The same result can be extended to a restricted form of heterogeneous processor model. Meanwhile, we show that on homogeneous processor model more efficient algorithms can be applied and result in time complexity of O(m2) when m ≤ n. For completeness, we also discuss cases without contiguity constraints. We show under such cases the problems become at least as hard as NP-hard.