Joint admission control and resource allocation in cognitive code-division networks

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.

In this paper, we study the problem of joint admission control and power allocation for cognitive radio networks. In such a scenario, the quality of service for primary and secondary users is needed to be guaranteed, which can be translated into the following two constraints: the inference temperature constraint for primary users and the minimum signal-to-interference-plus-noise ratio (SINR) constraint for secondary users. Due to the high density or the mobility of the secondary users, not all the secondary users are supportable. The problem of our interest is to select the maximum subset of secondary users given that the above constraints are satisfied. Moreover, because different secondary users have different revenue outputs, the problem becomes how we can find a subset of the secondary users such that the total revenue output of the networks is maximized. It can be shown that finding the optimal removal set is a NP hard problem. Therefore, we transform the original problem into a smooth optimization problem, and solve it by using a gradient descent based algorithm. This algorithm solves the power allocation and admission control jointly, and its superior performance over the existing algorithms is demonstrated through simulations.

Load shedding and distributed resource control of stream processing networks

Cognitive radio technology is proposed to improve spectrum utilization efficiency through exploiting the unused portions of spectrum in an opportunistic manner. In cognitive radio networks (CRNs), primary users (PUs) are allowed to lease out their unused spectrum to share with cognitive users (CUs). In a CRN consisting of multiple PUs and CUs, the spectrum pricing and resource allocation scheme have to be optimally designed, so that the efficient spectrum sharing among PUs and CUs can be supported and the QoS requirements of both PUs and CUs can be guaranteed. In this paper, we consider the joint spectrum pricing and resource allocation problem of a CRN with imperfect channel condition, i.e., channel state information of the PUs is not fully accessible to the CUs. To characterize the willingness of PUs to share subchannels and the intention of CU on accessing subchannels, a pricing scheme for both PUs and CUs is designed. Addressing the competition of CUs in channel accessing and power allocation, a non-cooperative Game model is formulated. Through solving the Nash equilibrium solution of the Game model, the optimal pricing and resource allocation strategy can be obtained. Numerical results demonstrate the efficiency of the proposed algorithm.

The problem of joint radio and cloud resources allocation is studied for heterogeneous mobile cloud computing networks. The objective of the proposed joint resource allocation schemes is to maximize the total utility of users as well as satisfy the required quality of service (QoS) such as the end-to-end response latency experienced by each user. We formulate the problem of joint resource allocation as a combinatorial optimization problem. Three evolutionary approaches are considered to solve the problem: genetic algorithm (GA), ant colony optimization with genetic algorithm (ACO-GA), and quantum genetic algorithm (QGA). To decrease the time complexity, we propose a mapping process between the resource allocation matrix and the chromosome of GA, ACO-GA, and QGA, search the available radio and cloud resource pairs based on the resource availability matrixes for ACO-GA, and encode the difference value between the allocated resources and the minimum resource requirement for QGA. Extensive simulation results show that our proposed methods greatly outperform the existing algorithms in terms of running time, the accuracy of final results, the total utility, resource utilization and the end-to-end response latency guaranteeing.

In this work, two novel virtual user association and resource allocation algorithms are introduced for a wireless virtualized heterogeneous network with hybrid energy supply. In the considered system, macro base stations (MBSs) are supplied by the grid power and small base stations (SBSs) have the energy harvesting capability in addition to the grid power supplement. Multiple infrastructure providers (InPs) own the physical resources, i.e., BSs and radio resources. The Mobile Virtual Network Operators (MVNOs) are able to recent these resources from the InPs and operate the virtualized resources for providing services to different users. In particular, aiming to maximize the overall utility for the MVNOs, a joint resource (spectrum and power) allocation and user association problem is presented. First, we present an alternating direction method of multipliers (ADMM)-based algorithm solution to find the near-optimal solution in a static manner. Moreover, we also utilize deep reinforcement learning to design the optimal policy without knowing a priori knowledge of the dynamic nature of networks. We have conducted extensive simulation and the performance evaluation demonstrate the advantages and effectiveness of the proposed schemes.

In this paper, we propose a joint power control and resource allocation approach for co-channel deployed high dense small-cells in macro-cell networks, denoted as Two-tier Optimized Resource and Power Allocation approach (TORPA). The ultimate goal of our contribution is to mitigate the co-tier and cross-tier interference faced by indoor and outdoor users in downlink communications and efficiently satisfy their resource requirements. With this in mind, we formulate the resulting optimization problem as a Mixed-Integer Linear Program (MILP) that aims to minimize the total transmit power and achieve an efficient two-tier physical resource allocation. Then, we propose a semi-centralized scheme to solve it. Extensive simulations show that TORPA can efficiently allocate power and distribute spectrum resources to both Small-cell Users (SUs) and Macro-Users (MUs) compared to the distributed resource allocation scheme and a prominent state of the art gametheoretic approach. In particular, TORPA improves the SU’s throughput by up to 9 times and 22% in comparison to these both schemes.

In this paper, we study the joint user pairing and resource allocation problem for uplink single carrier frequency-division multiple access (SC-FDMA) system with virtual multiple-input multiple-output (V-MIMO) implemented. Different from existing relevant researches, we consider a universal solution based on profit function for this problem instead of studying a particular scheduling policy, and then we explain that it is a MAX SNP-hard problem with high complexity. After that, we will propose a low-complexity heuristic algorithm called First User First (FuF), which is based on the idea of allocating resource to one user first. Moreover, in order to get a more accurate theoretical approximation to this problem, we will propose a constant factor approximation algorithm with polynomial-time complexity using local ratio technique. The simulation results of our algorithms show that the B-LRT algorithm has robust performance compared with other popular algorithms, while the FuF algorithm has lowest computation complexity at the expense of a little performance loss.

Spectrum access system (SAS) is a spectrum sharing framework proposed to share the spectrum between the incumbent users and the citizen broadband radio service devices, i.e. Priority access users and general authorized access (GAA) users. In this paper, we propose an interfering angle based method for the joint resource (channel and transmit power) allocation problem to the mobile and fixed GAA users. With mobile GAA users, the set of GAA users that can hear each other will change at different time instants making the resource allocation problem more challenging. The resource allocation of fixed and mobile GAA users is done considering coexistence with priority users, as well as coexistence between mobile and fixed GAA users. For the conflict-free resource allocation to fixed and mobile GAA users, we propose to use the maximum allowed transmit power for the beams of fixed GAA users that lie within the interference range of mobile GAA users. The simulation results show improved capacity from our proposed method while satisfying a predetermined interference constraint.

Device-to-device (D2D) communications can help in achieving the higher data rate targets in emerging wireless networks. The use of D2D communication imposes certain challenges such as interference with the cellular and D2D users. A well-designed joint admission control, network mode selection, and power allocation technique in a cellular network with D2D capability can improve overall throughput. The proposed technique jointly maximizes the total throughput and number of admitted users in cellular networks under quality-of-service (QoS) and interference constraints. The joint admission control, mode selection, and power allocation problem (JACMSPA) falls into a class of mixed-integer nonlinear constraint optimization problems that are generally NP-hard. Due to the combinatorial nature of the problem, its optimal solution needs exhaustive search of integer variables whose complexity increases exponentially with the number of user pairs. In this paper, we invoke outer approximation approach (OAA)-based linearization technique to solve the JACMSPA. The proposed method gives guaranteed $\varepsilon$ - optimal solution with reasonable computational complexity. Simulation results verify the effectiveness of the proposed approach method.

Narrow-Band Internet of Things (NB-IoT) is 3GPPs cellular technology designed for Low-Power Wide Area Network (LPWN) and it is a promising approach that NB-IoT combines with NOMA which is designed for accommodating more devices in the 5G era. Previous works mainly focus on uplink channel resource allocation to achieve connectivity maximization in NB-IoT with NOMA; however, few articles consider NB-IoT downside issues and downlink resource allocation problem to achieve maximum system throughput has not been studied in NB-IoT with NOMA. Thus, in this paper to provide a reliable and seamless service for NB-IoT users (NUs) and maximizing network downlink throughput, we propose a resource allocation algorithm for joint equipment QoS requirements and resource allocation fairness. In this scheme, we design algorithm to implement the mapping between NUs and subchannels for suboptimal system throughput. Then we convert the power allocation problem of the NUs on the same subchannel into a DC problem and we design algorithm to solve it to get suboptimal solution. Numerical results show that the proposed scheme achieves a better performance compared with exiting schemes in terms of the system throughput.

In this paper, we propose a multi-objective optimization framework to address the joint mobile association and resource allocation problem in a video transmitted wireless heterogeneous network. We consider user quality of experience (QoE) as one of the design objectives together with two other performance metrics to characterize the design tradeoffs among perceived video quality, power consumption, and network resource consumption. In order to find the Pareto optimal solutions for a multi-objective optimization problem, we first apply weighted Tchebycheff approach to aggregate multiple objectives and minimize the Tchebycheff distance between the optimal solution and utopia solution. Dual decomposition technique is then introduced to decompose the above mentioned problem into a series of similar subproblems. By using linear relaxation and variable transformation, standard convex optimization method is applied to solve these subproblems efficiently and the optimal mobile association and resource allocation are obtained. System level simulation studies numerically demonstrate the tradeoffs among three design objectives and provide an insightful understanding on the performance compromise under multi-objective conditions.

In the upcoming fifth-generation (5G) wireless communication system, the massive MIMO and mmWave technologies will play a key role to improve the system performance. With the rapid development of the network communications, optimizing radio resources of wireless communication has been one of the challenging issues in 5G networks. In order to effectively improve network performance, an efficient resource allocation and power optimization algorithm is necessary. In this paper, we study joint resource allocation and power optimization (JRAPO) problem in multiuser mmWave massive MIMO system, which target at increasing energy efficiency (EE) and decreasing power consumption of systems. First, the problem of JRAPO is formulated as a multivariate mixed integer nonlinear programming problem. Second, because the non-convexity of this problem, we transformed it into a convex optimization problem by relaxing the resource block (RB) allocation indicator. Finally, we used Lagrangian dual decomposition algorithm and water-filling scheme to resolve this joint optimization problem. Simulation results demonstrated the effectiveness of the proposed algorithm.

Optimal resource allocation in cloud systems is NP-hard due to the involvement of several conflicting objectives and unpredictable cloud traffic. To improve user satisfaction and resource utilization while minimizing end-user cost, the joint allocation of cloud resources is inevitable. In this work, we model end-user cost in cloud as the optimization objective using bandwidth and compute allocation as the decision variables. To solve the joint Virtual Machine Placement (VMP) problem we propose a single point, greedy, software-defined network (SDN)-based solution that minimizes end-user cost by making certain changes to the fat-tree datacenter architecture. Mathematically, we show that the overall objective function is convex hence solving it using a weighted-sum greedy approach will induce solutions that are Pareto-optimal. Experimental evaluations confirm up to 15% reduction in the response time and up to 14% increase in the efficiency of resources. To analyse the risks involved in deploying delay-sensitive applications over cloud and to show the effects of resource allocation approaches, we perform a risk analysis of delay-sensitivity in cloud using real-time CVD detection. The results confirm the reduced response time due to the proposed approach while maintaining the efficiency of detection.

This paper investigates the power control and resource allocation problem in a simultaneously wireless information and power transfer (SWIPT)-based cognitive two-way relay network, in which two secondary users exchange information through a power splitting (PS) energy harvesting (EH) cognitive relay node underlay in a primary network. To enhance the secondary networks’s transmission ability without detriment to the primary network, we formulate an optimization to maximize the minimum transmission rates of the cognitive users by jointly optimizing power allocation at the sources, the time allocation of transmission frames and power splitting at the relay, under the constraint that the transmission power of the cognitive network is set not to exceed the primary user interference threshold to ensure primary work performance. To efficiently solve this problem, a sub-optimal algorithm named the joint power control and resource allocation (JPCRA) scheme is proposed, in which we decouple the non-convex problem into convex problems and use alternative steps in the optimization algorithm to get final solutions. Numerical results reveal that the proposed scheme enhances transmission fairness and outperforms three traditional schemes.