Sum-utility Maximization Problem Research Articles

Decentralized User Scheduling for Rate-Constrained Sum-Utility Maximization in the MIMO IBC

While the problems of sum-rate maximization and sum-power minimization subject to quality of service (QoS) constraints in the multiple input multiple output interference broadcast channel (MIMO IBC) have been widely studied, most of the proposed solutions have neglected the user scheduling aspect assuming that a feasible set of users has been previously selected. However, ensuring QoS for each user in the MIMO IBC involves the joint optimization of transmit/receive beamforming vectors, transmit powers, and user scheduling variables. To address the full problem, we propose a novel formulation of a rate-constrained sum-utility maximization problem which allows to either deactivate users or minimize the QoS degradation for some scheduled users in infeasible scenarios. Remarkably, this is achieved avoiding the complexity of traditional combinatorial formulations, but rather by introducing a novel expression of the QoS constraints that allows to solve the problem in a continuous domain. We propose centralized and decentralized solutions, where the decentralized solutions focus on practical design and low signaling overhead. The proposed solutions are then compared with benchmarking algorithms, where we show the effectiveness of the joint scheduling and transceiver design as well as the flexibility of the proposed solution performing advantageously in several MIMO IBC scenarios.

A Sum-Utility Maximization Approach for Fairness Resource Allocation in Wireless Powered Body Area Networks

Wireless body area networks (WBANs) are essential for monitoring physiological signals of the human body, but the lifetime of WBANs is limited by battery longevity and it is not convenient or feasible for replacing the batteries of the sensors. The newly emerged energy-harvesting technology provides the potential to break the battery limitation of WBANs. However, the radio resource of a WBAN should be carefully scheduled for the wireless power transfer links and wireless information transmission links; otherwise, severely unfair resource allocation could be incurred due to the difference of channel qualities of the sensors. In this paper, we propose a marginal utility theoretic method to allocate the radio resource to the on-/in-body sensors in a fair and efficient manner. Especially, we consider that the sensors are wireless powered by multiple pre-installed radio-frequency energy sources. First, the utility function for a sensor node is proposed, which can map the achievable throughput to a satisfaction level of network QoS. Then, the fairness resource allocation among the sensor nodes is modeled as a sum-utility maximization problem. By using the dual decomposition method, the optimal solution to the proposed problem can finally be solved in the closed form. In comparison with the sum-throughput maximization and common-throughput maximization methods, the simulation results show that the proposed sum-utility maximization method can bring a fair throughput allocation for the sensors with different channel conditions, and the performance loss to the sum-throughput maximization method is small, while the sum-throughput maximization method is extremely unfair.

Dynamic Spectrum Sharing for Hybrid Access in OFDMA-Based Cognitive Femtocell Networks

Cognitive femtocell has emerged as one of the promising ways to solve the indoor coverage and spectrum shortage problems in cellular networks. However, it is a challenge to motivate both macro base station ( $\text{MBS}$ ) and femtocell access points ( $\text{FAPs}$ ) to adopt this hybrid access policy. In this paper, we propose a systematic dynamic spectrum sharing framework for hybrid access in orthogonal frequency-division multiple-access-based cognitive macro-femtocell networks. In this framework, the $\text{MBS}$ offloads some macro user equipments ( $\text{MUEs}$ ) to $\text{FAP}$ to improve the transmission performance and save energy. In return, the MBS leases a portion of its spectrum to $\text{FAP}$ . Then, the $\text{FAP}$ can use a part of spectrum for its own transmission and use another part to serve $\text{MUEs}$ . We design an auction-based approach where each $\text{FAP}$ receives additional $\text{MUE}$ as bidders, while MBS acts as auctioneer. We formulate the dynamic resource sharing as a sum utility maximization problem. Numerical results demonstrate that both the macro and femtocell could get benefit from our framework.

Dynamic Spectrum Allocation for the Downlink of OFDMA-Based Hybrid-Access Cognitive Femtocell Networks

The cognitive femtocell has emerged as an exciting technology to solve the indoor coverage problem in future cellular networks. Recently, several technical issues for the cognitive femtocell have been studied, e.g., spectrum sharing and interference mitigation. However, the incentive method that is very important for practical hybrid access cognitive femtocell deployment has not been well investigated. In this paper, we propose a new dynamic spectrum allocation method for the hybrid access cognitive femtocell. In the proposed method, the macro base station (BS) allocates a portion of subchannels to the femto access point (FAP) to spur the FAP to serve the macro users (MUs). Then, the FAP allocates the subchannels and power to maximize the femtocell network utility, whereas the throughput of the served MUs is guaranteed. Moreover, we formulate the corresponding resource allocation problem as a sum-utility maximization problem and propose an optimization method to solve it via the dual decomposition method. Simulation results show that both the wireless service provider and the femtocell could benefit from the proposed method.

Decomposition by Successive Convex Approximation: A Unifying Approach for Linear Transceiver Design in Heterogeneous Networks

We study the downlink linear precoder design problem in a multi-cell dense heterogeneous network (HetNet). The problem is formulated as a general sum-utility maximization (SUM) problem, which includes as special cases many practical precoder design problems such as multi-cell coordinated linear precoding, full and partial per-cell coordinated multi-point transmission, zero-forcing precoding and joint BS clustering and beamforming/precoding. The SUM problem is difficult due to its non-convexity and the tight coupling of the users' precoders. In this paper we propose a novel convex approximation technique to approximate the original problem by a series of convex subproblems, each of which decomposes across all the cells. The convexity of the subproblems allows for efficient computation, while their decomposability leads to distributed implementation. {Our approach hinges upon the identification of certain key convexity properties of the sum-utility objective, which allows us to transform the problem into a form that can be solved using a popular algorithmic framework called BSUM (Block Successive Upper-Bound Minimization).} Simulation experiments show that the proposed framework is effective for solving interference management problems in large HetNet.

Resource Allocation and Adaptive Modulation in Uplink SC-FDMA Systems

In this paper, we study joint power and sub-channel allocation, and adaptive modulation in Single Carrier Frequency Division Multiple Access (SC-FDMA) which is adopted as the multiple access scheme for the uplink in the 3GPP-LTE standard. A sum-utility maximization problem is considered. Unlike OFDMA, in addition to the restriction of allocating a sub-channel to one user at most, the multiple sub-channels allocated to a user in SC-FDMA should be consecutive as well. This renders the resource allocation problem prohibitively difficult and the standard optimization tools (e.g., Lagrange dual approach widely used for OFDMA, etc.) can not help towards its optimal solution. We propose a novel optimization framework for the solution of this problem which is inspired from the recently developed canonical duality theory. We first formulate the optimization problem as binary-integer programming problem, and then transform this binary-integer programming problems into a continuous space canonical dual problem that is a concave maximization problem. Based on the solution of the continuous space dual problem, we derive joint power and sub-channel allocation algorithm whose computational complexity is polynomial. We provide conditions under which the proposed algorithms are optimal. We also propose an adaptive modulation scheme which selects an appropriate modulation strategy for each user. We compare the proposed algorithm with the existing algorithms in the literature to assess their performance. The results show a tremendous performance gain.

Distributed Linear Precoder Optimization and Base Station Selection for an Uplink Heterogeneous Network

In a heterogeneous wireless cellular network, each user may be covered by multiple access points such as macro/pico/relay/femto base stations (BS). An effective approach to maximize the sum utility (e.g., system throughput) in such a network is to jointly optimize users' linear procoders as well as their BS associations. In this paper, we first show that this joint optimization problem is NP-hard and thus is difficult to solve to global optimality. To find a locally optimal solution, we formulate the problem as a noncooperative game in which the users and the BSs both act as players. We introduce a set of new utility functions for the players and show that every Nash equilibrium (NE) of the resulting game is a stationary solution of the original sum utility maximization problem. Moreover, we develop a best-response type algorithm that allows the players to distributedly reach a NE of the game. Simulation results show that the proposed distributed algorithm can effectively relieve local BS congestion and simultaneously achieve high throughput and load balancing in a heterogeneous network.

Joint Transmit Precoding for the Relay Interference Broadcast Channel

Relays in cellular systems are interference limited. The highest end-to-end sum rates are achieved when the relays are jointly optimized with the transmit strategy. Unfortunately, interference couples the links together, making joint optimization challenging. Further, the end-to-end multihop performance is sensitive to rate mismatch when some links have a dominant first link, whereas others have a dominant second link. This paper proposes an algorithm for designing the linear transmit precoders at the transmitters and relays of the relay interference broadcast channel, which is a generic model for relay-based cellular systems, to maximize the end-to-end sum rates. First, the relays are designed to maximize the second-hop sum rates. Next, approximate end-to-end rates that depend on the time-sharing fraction and the second-hop rates are used to formulate a sum-utility maximization problem to design the transmitters. This problem is solved by iteratively minimizing the weighted sum of mean square errors (MSEs). Finally, the norms of the transmit precoders at the transmitters are adjusted to eliminate rate mismatch. The proposed algorithm allows for distributed implementation and has fast convergence. Numerical results show that the proposed algorithm outperforms a reasonable application of single-hop interference management strategies separately on two hops.

CRAC: Cognitive Radio Assisted Cooperation for Downlink Transmissions in OFDMA-Based Cellular Networks

In this paper, we propose a novel framework of cognitive radio assisted cooperation (CRAC) for downlink transmissions in orthogonal frequency-division multiple access (OFDMA) - based cellular networks. In the proposed CRAC framework, relay stations are deployed in each cell and have spectrum sensing capability. In turn, they can access unoccupied white space to opportunistically obtain additional sub-channels to assist relaying information for cellular users. One of promising novelties is that the proposed CRAC considers joint resource allocation which includes transmission mode selection, relay station allocation, and transmit power/sub-channel allocation, to cost-effectively provide services and applications. Specifically, we first formulate the joint resource allocation as a sum utility maximization problem with power constraints on the base station and relay stations, which is a mixed integer programming problem. Then, we leverage dual decomposition method and derive a centralized optimal solution. Extensive simulation results are presented and demonstrate that the proposed CRAC can achieve a significant performance improvement in terms of the downlink network throughput while maintaining comparable fairness among cellular users in contrast to the traditional relay-based cooperation approach.

Distributed Power Allocation in Two-Hop Interference Channels: An Implicit-Based Approach

The problem of distributed power allocation for an interference relay network is considered, where multiple source-relay-destination (S-R-D) links communicate concurrently in the same frequency and interfere over two hops, referred to as a two-hop interference channel. The direct source-destination connections are not considered in this work. Power allocation in this scenario is challenging as it necessitates not only performance tradeoff among multiple links but also power distribution for each link along spatial dimensions. We approach the problem from a game-theoretic perspective and propose a novel implicit-based approach to prove the uniqueness of the Nash equilibrium (NE). This technique complements the existing literature on equilibria analysis by revealing the benefits of expressing the best responses in implicit forms. To benchmark the performance of the NE, we have investigated the non-convex sum-utility maximization problem, and have (1) identified the optimal solution structures; (2) proved that linear pricing is locally optimal in maximizing the sum utilities. A simple and distributed pricing algorithm is then proposed. Simulation results show that for a two-user network, the proposed scheme closely approaches the optimal sum rate, while, for a ten-user network, the improvement is up to 340% compared to the non-cooperative game model without pricing.

An Iteratively Weighted MMSE Approach to Distributed Sum-Utility Maximization for a MIMO Interfering Broadcast Channel

Consider the multiple-input multiple-output (MIMO) interfering broadcast channel whereby multiple base stations in a cellular network simultaneously transmit signals to a group of users in their own cells while causing interference to each other. The basic problem is to design linear beamformers that can maximize the system throughput. In this paper, we propose a linear transceiver design algorithm for weighted sum-rate maximization that is based on iterative minimization of weighted mean-square error (MSE). The proposed algorithm only needs local channel knowledge and converges to a stationary point of the weighted sum-rate maximization problem. Furthermore, the algorithm and its convergence can be extended to a general class of sum-utility maximization problem. The effectiveness of the proposed algorithm is validated by numerical experiments.

An efficient coordination scheme between layers of grid architecture

Grid architecture is protocol architecture, with protocols defining the basic mechanisms by which virtual organization (VO) users and resources negotiate, establish, manage, and exploit sharing relationships. A standard based open architecture facilitates extensibility, interoperability, portability, and code sharing; standard protocols make it easy to define standard services that provide enhanced capabilities. Grid architecture organizes into layers. In the paper, a three-layer coordination of grid architecture is proposed. The aim is to optimize the end-to-end quality of the dynamic grid application as well as efficiently utilizing the grid resources. A joint coordination algorithm, which exploits the interlayer coupling of fabric layer, collective layer and application layer, is proposed. The algorithm aims to achieve a system-wide optimization based on the user's preferences. We formulate the integrated coordination of three layers into a constrained optimization problem. Utility functions are used to express grid user' preferences, resource provider's benefit function and system's objectives. The optimization framework provides a layered approach to the sum utility maximization problem. The advantage of the proposed joint three-layer coordination is demonstrated through simulations.

Fabric Level and Application Level QoS Guarantees in Grid Computing

In the paper, a cross-layer optimization between application layer and fabric layer is proposed. The aim is to optimize the end-to-end quality of the dynamic grid application as well as efficiently utilizing the grid resources. The application layer QoS and fabric layer QoS are closely interrelated in Grids since the upper layer service is based on the lower level's capabilities. A fabric level and application level QoS scheduling algorithm is proposed. We formulate the integrated design of resource allocation and user QoS satisfaction control into a constrained optimization problem. The optimization framework provides a layered approach to the sum utility maximization problem. The application layer adaptively adjusts user's resource demand based on the current resource conditions, while the fabric layer adaptively allocates CPU, storage and bandwidth required by the upper layer.