Network Utility Maximization Problem Research Articles

Machine Learning Aided Context-Aware Self-Healing Management for Ultra Dense Networks With QoS Provisions

The self-organizing network is envisioned as a key technology to future wireless networks, especially for densely deployed small cell scenarios. Self-healing (SH) is an essential functionality to allow the networks to automatically detect and compensate for cell outages, which typically occur when unexpected network failures arise. In this paper, reaping the benefits of machine learning, we propose a novel SH framework in ultra dense small cell networks for meeting the demands of low-cost and fast network operation, quality of service (QoS), and energy efficiency. The proposed SH scheme comprises small cell outage detection (SCOD) and small cell outage compensation (SCOC) to enable self-healing in ultra dense small cell networks. Based on the context information of the partial key performance indicator (KPI) statistics, we propose a novel SCOD algorithm to detect the outage by applying support vector data description (SVDD) approach. The SCOD algorithm detects a small cell outage efficiently considering two situations: KPIs available situation and non-KPIs available situation. Furthermore, in order to compensate the small cell outage, SCOC is formulated as a network utility maximization problem to optimally compensate for the outaged zone in small cell network. A distributed compensation algorithm with low computational complexity is developed to balance the load of small cell networks, considering the QoS provision for users. Simulation results demonstrate that the proposed SH scheme can detect the small cell outage efficiently and can achieve an optimized QoS performance when compensating for the detected small cell outage.

Resource allocation for hybrid energy powered cloud radio access network with battery leakage

This study proposes a resource allocation policy for hybrid energy powered cloud radio access network with battery leakage. To optimise the network utility, the authors first formulate a network utility maximisation problem while jointly considering multiple random processes, which include energy harvesting, data arrival, wireless channel condition, and grid energy prices. To tackle this problem, they exploit the Lyapunov optimisation technique to develop an online dynamic resource allocation framework, which contains four subproblems, i.e. data admission, hybrid energy management, power allocation and route scheduling. Based on the solutions of these subproblems, a network utility optimisation resource allocation (ORA) algorithm is proposed. Specifically, the ORA algorithm only needs to track the current system states without requiring a prior knowledge about channel and energy conditions. Theoretical performance analyses and simulation results verify that the proposed algorithm can achieve close-to-optimal utility with bounded data buffer and battery capacity.

Dynamic Radio Resource Slicing for a Two-Tier Heterogeneous Wireless Network

In this paper, a dynamic radio resource slicing framework is presented for a two-tier heterogeneous wireless network. Through software-defined networking-enabled wireless network function virtualization, radio spectrum resources of heterogeneous wireless networks are partitioned into different bandwidth slices for different base stations (BSs). This framework facilitates spectrum sharing among heterogeneous BSs and achieves differentiated quality-of-service (QoS) provisioning for data service and machine-to-machine service in the presence of network load dynamics. To determine the set of optimal bandwidth slicing ratios and optimal BS-device association patterns, a network utility maximization problem is formulated with the consideration of different traffic statistics and QoS requirements, location distribution for end devices, varying device locations, load conditions in each cell, and intercell interference. For tractability, the optimization problem is transformed to a biconcave maximization problem. An alternative concave search (ACS) algorithm is then designed to solve for a set of partial optimal solutions. Simulation results verify the convergence property and display low complexity of the ACS algorithm. It is demonstrated that the proposed radio resource slicing framework outperforms the two other resource slicing schemes in terms of low communication overhead, high spectrum utilization, and high aggregate network utility.

Network Utility Maximization Based on an Incentive Mechanism for Truthful Reporting of Local Information

Classic network utility maximization (NUM) problems are usually solved assuming all information is available, implying that information not locally available is always truthfully reported. This may not be practical in all scenarios, especially in distributed/semidistributed networks. In this paper, incentive for truthful reporting in network optimizations with local information is studied. A novel general model for extending NUM problems to incorporate local information is proposed, which allows each user to choose its own objective locally and/or privately. Two specific problems, i.e., a user-centric problem (UCP) and a network-centric problem (NCP), are studied. In the UCP, a network center aims to maximize the collective benefit of all users, and truthful reporting from the users regarding their local information is necessary for finding the solution. We show that the widely adopted dual pricing cannot guarantee truthful information reporting from a user unless the resource is oversupplied or the price is too high for this user to afford. In the NCP, the network center has its own objective and preferred solution, and incentive needs to be provided for the users to adopt the solution it prefers. Truthful reporting from the users is necessary for the center to determine the incentives and achieve its solution. For two-user and multiuser cases, we propose two mechanisms to motivate truthful reporting from the users, while guaranteeing nonnegative utility gains for both the users and the center. A case study on underlay device-to-device (D2D) communication illustrates the application of the UCP and the NCP. Simulations are conducted for the D2D application to validate the analytical results and demonstrate the proposed mechanisms.

Heavy-Traffic Analysis of QoE Optimality for On-Demand Video Streams Over Fading Channels

This paper proposes online scheduling policies to optimize quality of experience (QoE) for video-on-demand applications in wireless networks. We consider wireless systems, where an access point transmits video content to clients over fading channels. The QoE of each flow is measured by its duration of video playback interruption. We are specifically interested in systems operating in the heavy-traffic regime. We first consider a special case of ON–OFF channels plus constant-bit-rate videos and establish a scheduling policy that achieves every point in the capacity region under heavy-traffic conditions. This policy is then extended for more general fading channels and variable-bit-rate videos, and we prove that it remains optimal under some mild conditions. We then formulate a network utility maximization problem based on the QoE of each flow. We demonstrate that our policies achieve the optimal overall utility when their parameters are chosen properly. Finally, we compare our policies against three popular policies. Simulation and experimental results validate that the proposed policies indeed outperform existing policies.

Convergence Analysis of Quantized Primal-Dual Algorithms in Network Utility Maximization Problems

This paper investigates the asymptotic and nonasymptotic behavior of the quantized primal-dual (PD) algorithm in network utility maximization (NUM) problems, in which a group of agents maximizes the sum of their individual concave objective functions under linear constraints. In the asymptotic scenario, we use the information-theoretic notion of differential entropy power to establish universal bounds on the maximum exponential convergence rates of joint PD, primal and dual variables under optimum-achieving quantization schemes. These results provide tradeoffs between the speed of exponential convergence, the agents' objective functions, the communication bit rates, and the number of agents and constraints. In the nonasymptotic scenario, we obtain lower bounds on the mean square distance of joint PD, primal and dual variables from the optimal solution at any time instant. These bounds hold regardless of the quantization scheme used.

Association and Load Optimization With User Priorities in Load-Coupled Heterogeneous Networks

In this paper, we consider the network utility maximization problem with various user priorities via jointly optimizing user association, load distribution, and power control in a load-coupled heterogeneous network. In order to tackle the nonconvexity of the problem, we first analyze the problem by obtaining the optimal resource allocation strategy in closed form and characterizing the optimal base station load distribution pattern. Both observations are shown essential in simplifying the original problem and making it possible to transform the nonconvex load distribution and power control problem into convex reformulation via exponential variable transformation. An iterative algorithm with low complexity is accordingly presented to obtain a suboptimal solution to the joint optimization problem. Simulation results show that the proposed algorithm achieves better performance than conventional approaches.

User Perceived Qos Provisioning for Video Streaming in Wireless OFDMA Systems: Admission Control and Resource Allocation

In this paper, we investigate a joint admission control (AC) and resource allocation (RA) scheme for providing quality of service (QoS) guarantees for video streaming in OFDMA networks. The QoS provisioning is posed as a network utility maximization (NUM) problem. We prove that the QoS requirements are mainly dominated by the average data rate with the aid of diffusion approximation approach, allowing us to decompose the NUM problem into two tractable sub-problems, namely, the AC and the RA sub-problems. We develop an AC algorithm, which enables us to select a maximum user set such that all selected users can meet their QoS requirements related to user perceived video quality during the video playback period. In addition, in order to handle the computational complexity, we further split the RA sub-problem into the constrained Markov decision process associated problems performed at users together with a transmission problem performed at the base station. Correspondingly, we develop a power and sub-carrier allocation algorithm to minimize the average power consumption. The decision of AC is made in a long timescale and depends on the average channel state information (CSI), whereas the decision of RA is made in a short timescale and is subject to both instantaneous CSI and queue state information. Simulation results demonstrate the advantages of the proposed algorithms.

Joint User Association and Power Allocation for Cell-Free Visible Light Communication Networks

As a complementary technology for conventional radio frequency communication, visible light communication (VLC) is a potential form of the optical wireless communication, which can provide both communication and illumination simultaneously. Since load balancing and power control for interference management are key challenges in the network deployment, we consider a joint user association and power allocation scheme in a cell-free VLC network to improve the system performance. It is mathematically formulated as a non-convex network utility maximization problem in consideration of the user fairness, load balancing, and power control. To tackle this non-convex problem, we divide it into two subproblems (i.e., the user association subproblem and the power allocation subproblem) and solve them with the dual projected gradient algorithm and successive convex approximation algorithm iteratively until a stationary point is found. Simulation results verify that significant gain can be achieved with the proposed scheme compared with the user association schemes without consideration of the power control.

Energy efficient scheduling mechanism using wireless sensor networks

Wireless sensor networks are an electro mechanical system which is used to transfer information to the base station from the beacon node. The objective of our project is to increase the lifetime of a beacon node, parallelly decreasing the energy using WSN by using appropriate scheduling mechanism. Each sensor performs different types of operation such as collecting the information from the sensors, processing it and communicating the source information when required, so energy consumption of these nodes is high. This can be reduced by using centralized approach and average consensus based Distributed algorithm. The lifetime of sensor is first increased by modifying M2CIC into multimodal set coverage problem and using this to produce NP-completeness. Another problem here is the network utility maximization problem which consists of Mixed Integer programming. This is solved by splitting the multi period problem into a single period problem. It is future reduced to Pure Integer programming. This Pure Integer programming can be solved by a centralized way.

Resource optimization and power allocation in in-band full duplex-enabled non-orthogonal multiple access networks

In this paper, the problem of uplink (UL) and downlink (DL) resource optimization, mode selection, and power allocation is studied for wireless cellular networks under the assumption of in-band full duplex base stations, non-orthogonal multiple access (NOMA) operation, and queue stability constraints. The problem is formulated as a network utility maximization problem for which a Lyapunov framework is used to decompose it into two disjoint subproblems of auxiliary variable selection and rate maximization. The latter is further decoupled into a user association and mode selection (UAMS) problem and a UL/DL power optimization (UDPO) problem that are solved concurrently. The UAMS problem is modeled as a many-to-one matching problem whose goal is to associate users to small cell base stations and select transmission mode (half-/full-duplex and orthogonal/NOMA). Then, an algorithm is proposed to solve the problem by finding a pairwise stable matching. Subsequently, the UDPO problem is formulated as a sequence of convex problems and is solved using the concave–convex procedure. Simulation results demonstrate that the proposed scheme is effective in allocating UL and DL power levels after dynamically selecting the operating mode and the served users, under different traffic intensity conditions, network density, and self-interference cancellation capability. The proposed scheme is shown to achieve up to 63% and 73% of gains in UL and DL packet throughput, and 21% and 17% in UL and DL cell edge throughput, respectively, compared with the existing baseline schemes.

Fairness-constrained optimized time-window controllers for secondary-users with primary-user reliability guarantees

In this paper, we design and test a primary–secondary user resource-management controller in cognitive radio vehicular networks, under hard and soft collision constraints. We cast the resource-management problem into a stochastic network utility maximization problem and derive the optimal steady-state controllers, which adaptively allocate the access time-windows to the secondary-users. We derive closed form expressions for the throughput-gain of the general controller with respect to the memoryless one, discussing conditions of applicability and advantages of each subclass. We prove that the hard controllers are able to make the outage-probability vanishing, without any throughput-loss with respect to the soft controllers. Finally, we generalize the obtained results to probabilistic fairness constrained problems and to controllers exploiting cognitive data-fusion techniques. We investigate about possible distributed implementations and show as controllers are able to adapt to unpredictable and abrupt changes of the network, without requiring any a priori knowledge of the vehicular traffic patterns.

Network utility maximization in uplink multiuser wireless LANs

Multiuser Multiple-Input Multiple-Output (MU-MIMO) enables several simultaneous transmission from the stations toward a multiple antennas access point of a WLAN and thereby boosts the network throughput. However, this throughput enhancement greatly depends on which stations are transmitting concurrently, which in turn necessitates the design of an efficient scheduling algorithm. In this paper, we model uplink MU-MIMO scheduling as a network utility maximization problem that is a non-convex problem due to the wideband channel aware transmission rate function and scheduling constraints. Relying on the zero duality gap, a stochastic learning algorithm is designed to solve the dual of that problem. Determination of stochastic subgradient in the stochastic learning algorithm involves an NP- complete problem, hence imperialist competitive algorithm is exploited to solve this problem. The simulation results substantiate significant throughput improvement and airtime fairness superiority of this scheme compared to the existing ones. The results also show the proposed scheme is not able to guarantee the optimal throughput due to employing imperialist competitive algorithm.

Jointly optimized congestion control, forwarding strategy, and link scheduling in a named-data multihop wireless network

As a promising future network architecture, named data networking (NDN) has been widely considered as a very appropriate network protocol for the multihop wireless network (MWN). In named-data MWNs, congestion control is a critical issue. Independent optimization for congestion control may cause severe performance degradation if it can not cooperate well with protocols in other layers. Cross-layer congestion control is a potential method to enhance performance. There have been many cross-layer congestion control mechanisms for MWN with Internet Protocol (IP). However, these cross-layer mechanisms for MWNs with IP are not applicable to named-data MWNs because the communication characteristics of NDN are different from those of IP. In this paper, we study the joint congestion control, forwarding strategy, and link scheduling problem for named-data MWNs. The problem is modeled as a network utility maximization (NUM) problem. Based on the approximate subgradient algorithm, we propose an algorithm called ‘jointly optimized congestion control, forwarding strategy, and link scheduling (JOCFS)’ to solve the NUM problem distributively and iteratively. To the best of our knowledge, our proposal is the first cross-layer congestion control mechanism for named-dataMWNs. By comparison with the existing congestion control mechanism, JOCFS can achieve a better performance in terms of network throughput, fairness, and the pending interest table (PIT) size.

Distributed Newton Methods for Strictly Convex Consensus Optimization Problems in Multi-Agent Networks

Various distributed optimization methods have been developed for consensus optimization problems in multi-agent networks. Most of these methods only use gradient or subgradient information of the objective functions, which suffer from slow convergence rate. Recently, a distributed Newton method whose appeal stems from the use of second-order information and its fast convergence rate has been devised for the network utility maximization (NUM) problem. This paper contributes to this method by adjusting it to a special kind of consensus optimization problem in two different multi-agent networks. For networks with Hamilton path, the distributed Newton method is modified by exploiting a novel matrix splitting techniques. For general connected multi-agent networks, the algorithm is trimmed by combining the matrix splitting technique and the spanning tree for this consensus optimization problems. The convergence analyses show that both modified distributed Newton methods enable the nodes across the network to achieve a global optimal solution in a distributed manner. Finally, the distributed Newton method is applied to solve a problem which is motivated by the Kuramoto model of coupled nonlinear oscillators and the numerical results illustrate the performance of the proposed algorithm.

Receding Horizon Control for an Online Cross-Layer Design of Wireless Networks Over Time-Varying Stochastic Channels

In this paper, we formulate a network utility maximization (NUM) problem, targeting an optimal cross-layer network operation, while considering time-varying and random possibly non-stationary wireless channels. As indicated in the literature, this problem imposes scalability constraints when the time horizon of the network control increases, impeding an online (i.e., real-time) application of its solution during the network operation. To achieve an online network control, in this paper, we leverage on model predictive control (MPC) or receding horizon control (RHC) for the solution of the NUM problem. Furthermore, MPC/RHC allows for the adaptation of the optimal controls in dynamic and evolving network conditions, in our case with respect to the wireless channels, the modeling parameters of which are estimated in an online fashion. We present and analyze the NUM problem, while we appropriately reformulate it for applying MPC/RHC. Then, we describe the MPC/RHC-based algorithmic solution, which determines the decisions for the online network control including power control, scheduling, routing, and congestion control, while we discuss stability and optimality issues. Finally, we evaluate the proposed methodology via numerical results and we show that the performance lies very close to the optimal one even for relatively small receding horizon lengths that significantly reduce the computational time complexity.

Near Optimal Data Gathering in Rechargeable Sensor Networks with a Mobile Sink

We study data gathering problem in Rechargeable Sensor Networks (RSNs) with a mobile sink, where rechargeable sensors are deployed into a region of interest to monitor the environment and a mobile sink travels along a pre-defined path to collect data from sensors periodically. In such RSNs, the optimal data gathering is challenging because the required energy consumption for data transmission changes with the movement of the mobile sink and the available energy is time-varying. In this paper, we formulate data gathering problem as a network utility maximization problem, which aims at maximizing the total amount of data collected by the mobile sink while maintaining the fairness of network. Since the instantaneous optimal data gathering scheme changes with time, in order to obtain the globally optimal solution, we first transform the primal problem into an approximate network utility maximization problem by shifting the energy consumption conservation and analyzing necessary conditions for the optimal solution. As a result, each sensor does not need to estimate the amount of harvested energy and the problem dimension is reduced. Then, we propose a Distributed Data Gathering Approach (DDGA), which can be operated distributively by sensors, to obtain the optimal data gathering scheme. Extensive simulations are performed to demonstrate the efficiency of the proposed algorithm.

Network Utility Maximization in Wireless Networks Over Fading Channels With Uncertain Distribution

In this the network utility maximization problem is investigated for wireless networks when only channel mean and variance are known. Due to the randomness of wireless channel, link outage will happen and a traffic flow’s rate will gradually decrease along its routing path. When only channel mean and variance are known, channel distribution is uncertain and it is harder to control the rate loss caused by link outage. We take into account this feature of rate loss and target at maximizing the network utility of multiple traffic flows’ rates at their destinations. An optimization problem is formulated, which is non-convex. Global optimal solution is achieved with the aid of bilevel optimization method and monotonic optimization method. Numerical results are presented to verify the effectiveness of our proposed method.

Distributed Optimization of Hierarchical Small Cell Networks: A GNEP Framework

Deployment of small cell base stations (SBSs) overlaying the coverage area of a macrocell BS (MBS) results in a two-tier hierarchical small cell network. Cross-tier and inter-tier interference not only jeopardize primary macrocell communication but also limit the spectral efficiency of small cell communication. This paper focuses on distributed interference management for downlink small cell networks. We address the optimization of transmit strategies from both the game theoretical and the network utility maximization (NUM) perspectives and show that they can be unified in a generalized Nash equilibrium problem (GNEP) framework. Specifically, the small cell network design is first formulated as a GNEP, where the SBSs and MBS compete for the spectral resources by maximizing their own rates while satisfying global quality of service (QoS) constraints. We analyze the GNEP via variational inequality theory and propose distributed algorithms, which only require the broadcasting of some pricing information, to achieve a generalized Nash equilibrium (GNE). Then, we also consider a nonconvex NUM problem that aims to maximize the sum rate of all BSs subject to global QoS constraints. We establish the connection between the NUM problem and a penalized GNEP and show that its stationary solution can be obtained via a fixed point iteration of the GNE. We propose GNEP-based distributed algorithms that achieve a stationary solution of the NUM problem at the expense of additional signaling overhead and complexity. The convergence of the proposed algorithms is proved and guaranteed for properly chosen algorithm parameters. The proposed GNEP framework can scale from a QoS constrained game to an NUM design for small cell networks by trading off signaling overhead and complexity.

Jointly Optimal Congestion Control, Forwarding Strategy and Power Control for Named-Data Multihop Wireless Network

Named data networking (NDN) as a promising future network architecture has been widely considered as a very appropriate network protocol for multihop wireless network (MWN). In named-data MWN, congestion control, forwarding strategy, and power control are three coupled critical issues. There are already extensive works about cross-layer design for MWN with IP and they have demonstrated the advantage of cross-layer design. Enlightened by cross-layer designs for MWN with IP, in this paper, the joint congestion control, forwarding strategy, and power control problem for named-data MWN are studied, and it is modeled as a named-data network utility maximization (NDNUM) problem. In our NDNUM, we maximize data receiving rate related network utility minus power consumption, while consider NDN’s communication characteristics that are different from IP. Then a distributed iterative algorithm based on subgradient method is proposed in order to solve NDNUM. To the best of our knowledge, our algorithm is the first cross-layer design for congestion control and power control for named-data MWN. We show that network utility maximization not only can be used to facilitate cross-layer design for networks with end-to-end protocols but also can be used to design cross-layer mechanisms for information-centric network. From simulation, our algorithm outperforms existing congestion control mechanism in network throughput, resource utilization efficiency, and stabilization PIT size.