Power and rate allocation for video conferencing in cellular networks

In this work, we investigate an online service management problem in vehicular edge computing networks. To satisfy the varying service demands of mobile vehicles, a service management framework is required to make decisions on the service lifecycle to maintain good network performance. We describe the service lifecycle consists of creating an instance of a given service (scale-out), moving an instance to a different edge node (migration), and/or termination of an underutilized instance (scale-in). In this paper, we propose an efficient online algorithm to perform service management in each time slot, where performance quality in the current time slot, the service demand in future time slots, and the minimal observed delay by vehicles and the minimal migration delay are considered while making the decisions on service lifecycle. Here, the future service demand is computed from a gated recurrent unit (GRU)based prediction model, and the network performance quality is estimated using a deep reinforcement learning (DRL) model which has the ability to interact with the vehicular environment in real-time. The choice of optimal edge location to deploy a service instance at different times is based on our proposed optimization formulations. Simulation experiments using real-world vehicle trajectories are carried out to evaluate the performance of our proposed demand-prediction based online service management (DOSM) framework against different state-of-the-art solutions using several performance metrics.

In this paper, we study the joint optimization problems to allocate resources for upload and download phases of tactile services which are investigated in cellular networks while queue stability constraint is of interest. In each time slot, the arrival traffic in downlink phase should be proportional to the uplink departure rate at the same time slot, which causes the coupling of the upload and download resource allocation problems. At each upload and download phase, a dynamic resource allocation policy with a stable-queue constraint is proposed to reduce latency. An iterative algorithm for joint power and sub-carrier allocation in both phases of upload and download is designed which is based on Lyapunov drift-plus-penalty algorithm and relaxation method. Via performed simulation under the different system parameters, the performance of the proposed algorithm is evaluated.

We consider the adaptive resource allocation problem in downlink Orthogonal Frequency Division Multiple Access (OFDMA) system with strict packet delay constraints in the range of . In this range of delay constraints, resource optimization has to be simultaneously performed over multiple time slots. Thus optimal allocation decisions require future Channel State Information (CSI) and packet arrival rate information. The causal nature of CSI combined with the increase in the number of optimization variables makes it a very challenging problem. We propose a two-step solution by separating scheduling from subcarrier and power allocation. Our proposed causal scheduler ensures delay guarantees by deriving a minimum data rate out of the user queues while minimizing transmit power in every time slot. The output rates are fed to the resource allocation block and the problem is formulated as a convex optimization problem. The subcarrier and power allocation decisions are made in order to satisfy the demanded rates within the peak power constraint. We address the feasibility of the physical layer resource allocation problem and develop efficient algorithms. When the problem is infeasible we devise a strategy which incurs minimum deviation from the proposed rates for maximum number of users. We show by simulations that our proposed scheme can efficiently utilize time variations as well as multiuser diversity in the system.

We study the problem of admission control and resource (subcarriers, power and bit-loading) allocation in a heterogeneous OFDMA wireless network serving both QoS-constrained high-priority users and best-effort users. By clustering subcarriers, efficient algorithms for cluster and power allocation have been proposed. Our strategy maximizes the total network utility of the best-effort users while satisfying the QoS request for maximum number of high-priority users. We approximate best-effort user utility function as a piece-wise linear function and propose a linear programming based cluster allocation algorithm. The feasibility of the resource allocation problem depends on the number of HP users in the network. Since a large number of demanding HP users would render the resource allocation problem infeasible, a joint admission control and resource allocation scheme could be an efficient way of tackling both problems with less overhead. By incorporating admission control, we propose an efficient and optimal joint admission control and resource allocation algorithm.

SummaryIn this paper, we present a QoS‐driven energy efficient power and subcarrier allocation in green orthogonal frequency division multiple access networks. Our proposed scheme aims at maximizing the effective energy efficiency (EEE), defined as overall effective capacity per total power consumption, subject to given user delay‐QoS requirements and an average sum transmit power constraints. We analytically obtain the uniquely global optimal power and subcarrier using fractional programming, which transforms the fractional program into an equivalent parametric convex program that has a tractable solution. Analytical results show that the optimal solution has the same structure as effective capacity maximization and overall power minimization solutions. Simulation results show that there exists a trade‐off between EEE and delay. When the delay is more stringent, the EEE decreases. Moreover, when the number of subcarriers increase, EEE increases, where a fixed optimal EEE can be achieved regardless of stringency of delay‐QoS requirements. Furthermore, our proposed resource allocation provides significant EEE gains over both the independent resource allocation and overall power minimization schemes. Copyright © 2015 John Wiley & Sons, Ltd.

The resource allocation of Orthogonal Frequency Division Multiple Access (OFDMA) is one of the core issues in the next generation mobile systems. The improvement in the performance and quality of service (QoS) of communication systems is relying upon the efficient utilization of the available communication resources. The resource allocation of the OFDMA systems is mainly depends on both power and subcarrier allocations of each user for different operation scenarios and channel conditions. This paper proposes and applies Firefly Pack Algorithm (FPA) to find the optimal or near optimal power and subcarrier allocations for OFDMA systems. It takes into consideration the power and subcarrier allocations constrains, channel and noise distributions, distance between users equipments and base station, user priority weight to approximate the most of the variables, constrains, and parameters that encounter in the OFDMA systems. Four important cases for the number of subcarriers and users are addressed, simulated, and analyzed with employing the FPA algorithm under specific operation scenarios to meet the standard specifications. The results demonstrate that FPA is an effective algorithm in finding the optimal or near optimal for both subcarrier and power allocation.

In this paper, the problem of subcarrier allocation, and power allocation is addressed in an OFDM-based two-tier femto-cell network comprising of a conventional macro-cell and multiple femto-cells. The macro-cell users near the femto-cell causes significant interference to the femto-cell users as well gets interfered by the femto-cell transmissions. We present a subcarrier and power allocation method in underlay femto-cell network while keeping the total interference to macro-cell users under acceptable levels. We maximize the multiple femto-cell users throughput by jointly allocating subcarriers to users within a femto-cell, and also power allocation, under the constraints of cross-tier interference between macro-cell and multiple femto-cells as well as interference among femto-cells. Then, based on this subcarrier and power allocation the performance of the two-tier network is analysed with capacity as the performance metric.

Resource allocation problem in high-speed railway wireless communication networks is one of the key issues to improve the efficiency of resource utilization. However, traditional resource allocation methods cannot be directly applied to this special communication system. In this paper, we propose a resource allocation approach for high-speed railway downlink orthogonal frequency-division multiplexing (OFDM) system with multiple-input multiple-output (MIMO) antennas. Sub-carriers, antennas, time slots, and power are jointly considered, which is formulated as a mixed-integer nonlinear programming problem. The effect of the moving speed on Doppler shift is analyzed to calculate the inter-carrier interference power. The objective is to maximize the throughput under the constraint of total transmission power. In order to reduce computational complexity, suboptimal solution to the optimization problem is obtained by quantum-behaved particle swarm optimization. Simulation results show that the proposed resource allocation strategy has a better performance compared with an existing one.

In multicarrier nonorthogonal multiple access (MC‐NOMA) systems, resource allocation for user rate fairness is an inherent challenge due to the huge difference in channel conditions among different users. As the general case of proportional fairness, proportional rate constraint is usually adopted in resource allocation optimization problems to ensure that achievable rates of users are distributed proportionally according to a set of arbitrarily predefined portion. In this article, we investigate joint subcarrier and power allocation in MC‐NOMA systems under proportional rate constraints. The formulated resource allocation optimization problem belongs to a mixed integer nonlinear programming (MINLP) problem due to its binary subcarrier allocation variables and nonaffine equality constraints. By introducing an auxiliary variable in proportional rate constraints and substituting power variable with rate variable, we convert this nonconvex problem into a convex optimization problem and solve it by using Karush‐Kuhn‐Tucker (KKT) conditions and the dual decomposition method. Based on the optimal solutions, we divide the resource allocation process into two phases, that is, subcarrier allocation and power allocation, and develop an optimal subcarrier allocation solution based resource allocation algorithm (OSSRA). Furthermore, we also propose a heuristic algorithm, referred to as proportional rate constrained subcarrier allocation algorithm (PCSAA), as a benchmark. Numerical results show that, substantial gains can be achieved by OSSRA over the existing work and the traditional orthogonal multiple access (OMA) schemes in terms of both sum rate and user fairness. Besides, PCSAA has better user fairness performance than OSSRA.

Multiuser orthogonal frequency division multiplexing (OFDM) is a very promising multiple access technique to efficiently utilize limited RF bandwidth and transmit power in wideband transmission over multipath fading channels. When a wideband spectrum is shared by multiple users in multiuser OFDM-based systems, different users may experience different fading conditions at all subcarriers. Each user is assigned a subset of all subcarriers by some allocation algorithm. Thus, multiuser diversity can be achieved by adaptively adjusting subcarrier, bit, and power allocation depending on channel status among users at different locations (Wong et al., 1999a). In (Wong et al., 1999a), Wong applies a Lagrangian optimization technique and an iterative algorithm to solve the subcarrier, bit and power allocation problem. The suboptimal scheme for the NP-hard joint optimization problem is decoupled into two steps while it has a high computational complexity. A sub-optimal algorithm has been proposed to solve a related problem (Wong et al., 1999b). In (Wong et al., 1999b), Wong presents a real-time subcarrier allocation (SA) algorithm. It is a two-phase algorithm, including the constructive initial assignment (CIA) and the subcarrier swapping steps. The initial subcarrier allocation algorithm needs to pre-determine the numbers of subcarriers for each user before the allocation process starts. The performance of the SAbased algorithm will be compared in the simulation. In (Kim et al., 2006), Kim shows that the allocation problem in (Wong et al., 1999a) can be transformed into an integer programming (IP) problem. The branch-and-bound algorithm (Wolsey, 1998) can be employed to find the optimal solution of the allocation problem which has exponential computational complexity in the worse cases. We utilize the approach to obtain the optimal solution as the performance bound for comparison. Evolutionary algorithms (EA) are used to solve extremely complex search and optimization problems which are difficult to solve through simple methods. EAs are intended to provide a better solution, as it is based on the natural theory of evolution. Evolutionary algorithm (EA)-based schemes have been applied to solve subcarrier, bit, and power allocation problems (Wang et al., 2005) (Reddy et al., 2007) (Reddy & Phora, 2007) (Pao & Chen, 2008). In general, chromosomes can be designed with binary, integer, or real representation. The chromosome lengths are related to the number of subcarriers. Each element in the chromosome is a subcarrier allocated to a user. In this research, a subset of subcarriers can be assigned to one user depending upon the availability of subcarriers at a particular time

With the development of wireless communication systems, it is particularly essential to maximize the quality of experience (QoE) of machine-to-machine (M2M) communication. In this paper, we propose a new QoE-oriented uplink rate control and resource allocation scheme for the Internet of Things (IoT) network, by introducing an evaluation model based on mean opinion score (MOS) for different machine-type communication (MTC) devices. The existing works are only dedicated to solving the short-term resource allocation problems by considering the current transmission time slots, which cannot handle long-standing problems. To this end, based on the recently developed Lyapunov optimization, we convert the original long-term optimization problem into the admission rate control subproblem and the resource allocation subproblem in each time slot. To solve the joint power optimization and sub-channel selection subproblems, Gale-Shapley algorithm is utilized to formulate it as a two-dimensional matching problem, and the preference lists are established by the transmission rate and signal to interference plus noise ratio (SINR). In the proposed algorithms, a priority mechanism is employed to ensure fairness. The simulation results demonstrate that without prior knowledge of the data arrivals and sub-channel statistics, the proposed algorithms can significantly improve the overall perceived quality from the users' perspective.

The present work features designing of a model predictive controller for the application of automatic generation control of a two area interconnected power system consisting of thermal, hydro and wind power generation units. Different Automatic Generation Control (AGC) strategies can be used to maintain the system frequency within the prescribed limit. Model Predictive Control (MPC) is such a modern control technique which works on an iterative finite horizon optimization of a model. MPC does not allow current time slot to be optimized while keeping the future time slots in mind. The coordination of control and set points is another unique feature of MPC. This paper presents performance comparison of MPC based automatic generation controller with conventional classical PI and PID automatic generation controllers. Comparative study reveals that proposed MPC provides better dynamic response for the mentioned system in terms of settling time, peak undershoot and peak overshoot.

A real-time decision model for industrial load management in a smart grid

In this paper, we consider simultaneous wireless information and energy transfer in an orthogonal-frequency-division-multiple-access decode-and-forward relay network, in which an energy-constrained relay node harvests energy from a source node and uses the harvested energy to forward information to multiple destination nodes. Our objective is to maximize the end-to-end sum rate by resource allocation, subject to transmit power constraint at the source and energy-harvesting (EH) constraint at the relay. A non-convex and mixed-integer programming (MIP) problem is formulated to optimize time-switching (TS) ratios of EH and information decoding at the relay, TS ratio of information transmission from relay to destinations, subcarrier allocation as well as power allocation (PA) over all subcarriers at source and relay. We propose to decouple this problem into a convex problem and an MIP problem in fractional form. To solve the MIP problem, we transform it into an equivalent optimization problem in subtractive form which has a tractable solution. As a result, we propose a novel scheme to achieve jointly optimal TS ratios, subcarrier allocation and PA. Simulation results verify the optimality of our proposed resource allocation scheme.

The authors describe a protocol that can adapt to the changing demands of a mix of synchronous transfer mode (STM) and asynchronous transfer mode (ATM) applications and efficiently allocate bandwidth to a variety of bursty traffic sources. In the case of a hybrid fiber-coaxial (HFC) network, the protocol resides in customer premises equipment (CPE) and a common head-end/central-office (HE/CO) controller. A medium-access control (MAC) processor provides for dividing the time domain for a given digital bitstream into successive frames, each with multiple STM and ATM time slots. Within the STM region of a frame, variable-length time slots are allocated to calls (e.g., telephony, video telephony) requiring different amounts of bandwidth. In the upstream channels, a contention access signaling time slot is also provided in the STM region for call control and setup requests. Within the ATM region, fixed-length time slots accommodate one individual ATM cell. These ATM time slots may be reserved for a user for either the duration of a call or a burst of successive ATM cells, or shared via a contention process. At least one contention time slot is available for signaling messages related to ATM call control and setup requests. The MAC-layer protocol, its relation to circuit- and ATM-amenable applications, and its performance with respect to throughput, latency, and bandwidth efficiency for several service scenarios are examined.