Linear transceiver design for a MIMO interfering broadcast channel achieving max–min fairness

We consider the problem of linear transceiver design to achieve max–min fairness in a downlink MIMO multicell network. This problem can be formulated as maximizing the minimum rate among all the users in an interfering broadcast channel (IBC). In this paper we show that when the number of antennas is at least two at each of the transmitters and the receivers, the min rate maximization problem is NP-hard in the number of users. Moreover, we develop a low-complexity algorithm for this problem by iteratively solving a sequence of convex subproblems. We theoretically establish the global convergence of the proposed algorithm to the set of stationary points, which may be suboptimal due to the non-convexity of the original minimum rate maximization problem. Numerical simulations show that this algorithm is efficient in achieving fairness among all the users.     

A decomposition method for optimization of large-system reliability

The authors consider a decomposition approach for optimization of the reliability of a large system with a general network structure. A three-level methodology is developed for optimal allocation of available resources among subsystems in order to ensure maximization of system reliability. The decentralized nature of this methodology greatly reduces the complexity of the large problem and facilitates seeking the optimal solution. Two examples show that the complexity of a large system can be greatly reduced by solving several smaller-dimensional subproblems iteratively. Subproblems whose dimensions are small can be efficiently solved by an existing nonlinear programming method. Another important feature of the approach is the possible simplification of the objective function during the solution. This leads in some cases to an analytic solution for the lower-level optimization problems in the three-level decomposition solution     

多点协作下行链路单调协同波束形成算法

研究了单基站功率约束条件下的多点协作多输入单输出干扰下行链路系统的和速率最大化非凸优化问题。为有效求解和速率最大化优化问题,首先采用分层优化方法将和速率最大化优化问题分解成发射功率最小化优化问题和单输入单输出干扰信道的和速率最大化优化问题;其次利用二阶锥规划优化方法求解发射功率最小化优化问题;然后利用凸近似和几何规划方法求解单输入单输出干扰信道的和速率最大化优化问题;最后通过交替求解这两个子优化问题,进而提出了一种新颖的单调协同多点波束成形算法;而且利用单有界序列原理证明了所提算法的收敛性。数值仿真表明所提算法只需约四次迭代即可收敛到稳定点,而且所获得的最优性能非常接近穷举搜索算法的最优性能。      

Analog neural optimization for ATM resource management

This paper addresses the issue of how to solve convex programming problems by analog artificial neural networks (ANNs), with applications in asynchronous transfer mode (ATM) resource management. We first show that the essential and difficult optimization problem of dimensioning the system of virtual subnetworks in ATM networks can be modeled as a convex programming task. Here the transformation of the problem into a convex programming task is a nontrivial step. We also present and analyze an analog ANN architecture that is capable of solving such convex programming tasks with time-varying penalty multipliers. The latter property makes it possible to perform quick sensitivity analysis with respect to the constraints in order to identify the bottleneck capacities in the network or those which give the highest return if we invest in extending them     

Call admission control and routing in integrated services networksusing neuro-dynamic programming

We consider the problem of call admission control (CAC) and routing in an integrated services network that handles several classes of calls of different value and with different resource requirements. The problem of maximizing the average value of admitted calls per unit time (or of revenue maximization) is naturally formulated as a dynamic programming problem, but is too complex to allow for an exact solution. We use methods of neuro-dynamic programming (NDP) [reinforcement learning (RL)], together with a decomposition approach, to construct dynamic (state-dependent) call admission control and routing policies. These policies are based on state-dependent link costs, and a simulation-based learning method is employed to tune the parameters that define these link costs. A broad set of experiments shows the robustness of our policy and compares its performance with a commonly used heuristic     

Maximation of the signal-to-interference ratio for a doubly spread target: Problems in nonlinear programming

Three optimization problems concerning the maximization of the signal-to-interference ratio for a doubly spread target via signal design are expressed in terms of equivalent nonlinear programming problems defined on a real space by restricting the transmit and processing waveforms to be complex weighted, uniformly spaced pulse trains. Each subpulse can be different in shape and occupy the entire interpulse spacing interval. The approach taken is analogous to the Rayleigh-Ritz technique. The first two optimization problems involve maximization with respect to the complex weights, The third problem involves maximization with respect to the subpulse parameters (e.g., frequency deviation, swept bandwidth, etc.) and allows one to find optimum frequency hop codes.One need not develop algorithms to solve these problems, but rather, one can simply use standard computer programs or methods which are available for solving nonlinear programming problems.     

Shared Risk Link Group (SRLG)-Diverse Path Provisioning Under Hybrid Service Level Agreements in Wavelength-Routed Optical Mesh Networks

The static provisioning problem in wavelength-routed optical networks has been studied for many years. However, service providers are still facing the challenges arising from the special requirements for provisioning services at the optical layer. In this paper, we incorporate some realistic constraints into the static provisioning problem, and formulate it under different network resource availability conditions. We consider three classes of shared risk link group (SRLG)-diverse path protection schemes: dedicated, shared, and unprotected. We associate with each connection request a lightpath length constraint and a revenue value. When the network resources are not sufficient to accommodate all the connection requests, the static provisioning problem is formulated as a revenue maximization problem, whose objective is maximizing the total revenue value. When the network has sufficient resources, the problem becomes a capacity minimization problem with the objective of minimizing the number of used wavelength-links. We provide integer linear programming (ILP) formulations for these problems. Because solving these ILP problems is extremely time consuming, we propose a tabu search heuristic to solve these problems within a reasonable amount of time. We also develop a rerouting optimization heuristic, which is based on previous work. Experimental results are presented to compare the solutions obtained by the tabu search heuristic and the rerouting optimization heuristic. For both problems, the tabu search heuristic outperforms the rerouting optimization heuristic.     

Distributed Lifetime and Data Rate Optimization with Dynamic Approximation in Wireless Networks

In this paper, we study the problem of jointly maximizing network lifetime and data rate in wireless networks. For this problem, we introduce a general network utility maximization (NUM) cross-layer formulation that accommodates routing, scheduling and stream control from different layers of network with relevant constraints. In particular, based on both Lagrangian approach and Markov Chain Monte Carlo method, we extend our programming model to distributed algorithms that can dynamically approximate the optimal solution to this problem. Finally, we present computational results for the insight that can be gained from the cross-layer optimization and the distributed algorithms.     

解线性及二次型规划问题增广的神经网络

本文提出了一个解线性及二次型规划问题的神经网络模型，证明了该网络是全局稳定于平衡点，而平衡点就是线性及二次型规划问题的解，该网络的优点是能够实时获得问题的精确解，且可以同时获得带等式不式约束的对偶问题解，该网络易于电路实现。     

Energy-efficient transceiver designs for multiuser MIMO cognitive radio networks via interference alignment

This paper studies the transceiver design for multiuser multiple-input multiple-output cognitive radio networks. Different from the conventional methods which aim at maximizing the spectral efficiency, this paper focuses on maximizing the energy efficiency (EE) of the network. First, we formulate the precoding and decoding matrix designs as optimization problems which maximize the EE of the network subject to per-user power and interference constraints. With a higher priority in accessing the spectrum, the primary users (PUs) can design their transmission strategies without awareness of the secondary user (SU) performance. Thus, we apply a full interference alignment technique to eliminate interference between the PUs. Then, the EE maximization problem for the primary network can be reformulated as a tractable concave-convex fractional program which can be solved by the Dinkelbach method. On the other hand, the uncoordinated interference from the PUs to the SUs cannot be completely eliminated due to a limited coordination between the PUs with the SUs. The secondary transceivers are designed to optimize the EE while enforcing zero-interference to the PUs. Since the EE maximization for the secondary network is an intractable fractional programming problem, we develop an iterative algorithm with provable convergence by invoking the difference of convex functions programming along with the Dinkelbach method. In addition, we also derive closed-form expressions for the solutions in each iteration to gain insights into the structures of the optimal transceivers. The simulation results demonstrate that our proposed method outperforms the conventional approaches in terms of the EE.     

Channel assignment for cellular radio using extended dynamic programming

The channel assignment is an important aspect of cellular radio networks. Because of the limitations on the frequency spectrum, the optimal or near-optimal channel assignment has become an essential part of the network operations of wireless personal communication systems. We formulate a new strategy for the channel assignment problem in agreement with the electromagnetic compatibility constraints. We introduce and formulate the extended dynamic programming (EDP), as an extension of dynamic programming for solving the channel assignment problem in a cellular system. Using EDP an algorithm is developed for fixed channel assignment problem and it is tested and compared with other existing methods by solving different problems. In agreement with electromagnetic compatibility constraints, solution strategy based on EDP algorithm finds many valid solutions with minimum possible bandwidth.     

Optimal Link Weights for IP-Based Networks Supporting Hose-Model VPNs

From traffic engineering point of view, hose-model VPNs are much easier to use for customers than pipe-model VPNs. In this paper we explore the optimal weight setting to support hose-model VPN traffic in an IP-based hop-by-hop routing network. We try to answer the following questions: (1) What is the maximum amount of hose-model VPN traffic with bandwidth guarantees that can be admitted to an IP-based hop-by-hop routing network (as opposed to an MPLS-based network), and (2) what is the optimal link weight setting that can achieve that? We first present a mixed-integer programming formulation to compute the optimal link weights that can maximize the ingress and egress VPN traffic admissible to a hop-by-hop routing network. We also present a heuristic algorithm for solving the link weight searching problem for large networks. We show simulation results to demonstrate the effectiveness of the search algorithm.     

Cross-Layer Design for End-to-End Throughput and Fairness Enhancement in Multi-Channel Wireless Mesh Networks

In this paper, we study joint rate control, routing and scheduling in multi-channel wireless mesh networks (WMNs), which are traditionally known as transport layer, network layer and MAC layer issues respectively. Our objective is to find a rate allocation along with a flow allocation and a transmission schedule for a set of end-to-end communication sessions such that the network throughput is maximized, which is formally defined as the maximum throughput rate allocation (MRA) problem. As simple throughput maximization may result in a severe bias on rate allocation, we take account of fairness based on a simplified max-min fairness model and the proportional fairness models. We define the max-min guaranteed maximum throughput rate allocation (MMRA) problem and proportional fair rate allocation (PRA) problem. We present efficient linear programming (LP) and convex programming (CP) based schemes to solve these problems. Numerical results show that proportional fair rate allocation schemes achieves a good tradeoff between throughput and fairness.     

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.     

Computation energy efficiency maximization based resource allocation scheme in wireless powered mobile edge computing network

For wireless powered mobile edge computing (MEC) network,a system computation energy efficiency (CEE) maximization scheme by considering the limited computation capacity at the MEC server side was proposed.Specifically,a CEE maximization optimization problem was formulated by jointly optimizing the computing frequencies and execution time of the MEC server and the edge user(EU),the transmit power and offloading time of each EU,the energy harvesting time and the transmit power of the power beacon.Since the formulated optimization problem was a non-convex fractional optimization problem and hard to solve,the formulated problem was firstly transformed into a non-convex subtraction problem by means of the generalized fractional programming theory and then transform the subtraction problem into an equivalent convex problem by introducing a series of auxiliary variables.On this basis,an iterative algorithm to obtain the optimal solutions was proposed.Simulation results verify the fast convergence of the proposed algorithm and show that the proposed resource allocation scheme can achieve a higher CEE by comparing with other schemes.     

A Location-Routing Problem in Designing Optical Internet Access with WDM Systems

In this paper, we deal with a location-routing problem in designing the optical Internet with WDM systems. This problem arises from the design of broadband local access networks that deliver high-speed access service to residential subscribers. The problem is to find an optimal location of the gateway and optimal routing of traffic demands in the optical access network. We develop mixed-integer programming models for solving the location-routing problem to minimize the total cost of network elements used in the network while carrying the offered traffic. By exploiting the inherent structure of the problem, we derive two effective tabu search procedures. We present promising computational results of the proposed solution procedures.     

无线自组网中最大化网络寿命的速率调整问题

该文针对发送速率可以调整的无线自组网,研究了最大化网络寿命的速率调整问题。将该问题模型化为混合整数非线性规划问题,通过分支限界法可以求出最优解。该文还提出了一个基于贪心策略的分布式最大化网络寿命速率调整算法MNLRAA,其基本思想是根据网络中节点的流量和剩余能量,尽可能为每条链路选择较低传输速率发送分组来节能。模拟实验结果表明,同所有节点使用最高传输速率的经典技术相比,MNLRAA可延长网络寿命20%以上。     

On Neural Network Approach to Computing the Quadratic Programming Problem：a Further Study

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Robust Beamforming via Worst-Case SINR Maximization

Minimum variance beamforming, which uses a weight vector that maximizes the signal-to-interference-plus-noise ratio (SINR), is often sensitive to estimation error and uncertainty in the parameters, steering vector and covariance matrix. Robust beamforming attempts to systematically alleviate this sensitivity by explicitly incorporating a data uncertainty model in the optimization problem. In this paper, we consider robust beamforming via worst-case SINR maximization, that is, the problem of finding a weight vector that maximizes the worst-case SINR over the uncertainty model. We show that with a general convex uncertainty model, the worst-case SINR maximization problem can be solved by using convex optimization. In particular, when the uncertainty model can be represented by linear matrix inequalities, the worst-case SINR maximization problem can be solved via semidefinite programming. The convex formulation result allows us to handle more general uncertainty models than prior work using a special form of uncertainty model. We illustrate the method with a numerical example.     

Neural network optimization for redundancy allocation

System reliability optimization problems such as redundancy allocation are hard to solve exactly. Neural networks offer an alternative computational model for obtaining good approximate solutions for such problems. In this paper we present a neural network for solving the redundancy allocation problem for a n-stage parallel redundant system with separable objective function and constraints. The problem is formulated as a 0–1 integer programming problem and solved using the network. The performance of the network compare favourably with that of the best fit algorithm. The number of iterations taken by the network increases very slowly with increase in number of variables. Hence the network can easily solve large problems.