Energy-efficient control of rate and power in DS-CDMA systems

The quality of service in direct-sequence code-division multiple access (DS-CDMA) can be controlled by a suitable selection of processing gain and transmission powers. In this paper, distributed control of rate and power for best effort data services is considered. In particular, we elaborate on the problem of how to control the transmission rates for maximizing system throughput while simultaneously minimizing the transmission powers. We assume a practical scenario, where every user has a finite set of discrete transmission rates and propose a simple heuristic rate allocation scheme, greedy rate packing (GRP), applicable in both up- and downlink. The scheme can be interpreted as a practical form of water-filling, in the sense that high transmission rates are allocated to users having high link gains and low interference. We show that under certain conditions, GRP will give maximum throughput and that it can be extended to guarantee a minimum data rate while maximizing network excess capacity. We suggest and analyze a distributed power control algorithm to control the intercell interference when GRP is applied to a multicellular system. Numerical results show that the proposed transmission scheme can significantly decrease the power levels while maintaining high throughput.     

A Cooperative Nearest Neighbours Topology Control Algorithm for Wireless Ad Hoc Networks

In this article, we introduce a simple distributed algorithm that assigns appropriate individual transmission powers to devices in a wireless ad hoc network. In contrast to many other proposed algorithms, it does without special hardware. It requires only local neighbourhood information and therefore avoids flooding information throughout the network. Finally, the cooperative nature of the algorithm avoids that devices cause excessive interference by using unnecessarily high transmission powers. By means of simulation, we show that the topologies created by this algorithm without any global knowledge are as effective as topologies resulting from a good choice of a common transmission power (which would require global knowledge) in terms of the achievable throughput. This work was supported in part by the German Federal Ministry of Education and Research (BMBF) as part of the IPonAir project.     

Cross-Layer Design for Lifetime Maximization in Interference-Limited Wireless Sensor Networks

We consider the joint optimal design of the physical, medium access control (MAC), and routing layers to maximize the lifetime of energy-constrained wireless sensor networks. The problem of computing lifetime-optimal routing flow, link schedule, and link transmission powers for all active time slots is formulated as a non-linear optimization problem. We first restrict the link schedules to the class of interference-free time division multiple access (TDMA) schedules. In this special case, we formulate the optimization problem as a mixed integerconvex program, which can be solved using standard techniques. Moreover, when the slots lengths are variable, the optimization problem is convex and can be solved efficiently and exactly using interior point methods. For general non-orthogonal link schedules, we propose an iterative algorithm that alternates between adaptive link scheduling and computation of optimal link rates and transmission powers for a fixed link schedule. The performance of this algorithm is compared to other design approaches for several network topologies. The results illustrate the advantages of load balancing, multihop routing, frequency reuse, and interference mitigation in increasing the lifetime of energy-constrained networks. We also briefly discuss computational approaches to extend this algorithm to large networks     

Adaptive Optimization-based Routing in Wireless Mesh Networks

Routing is a critical component in wireless mesh networks. The inherent shared-medium nature of the wireless mesh networks, however, poses fundamental challenges to the design of effective routing policies that are optimal with respect to the resource utilization. Node churns and traffic fluctuations exacerbate such a problem. In this paper, we propose a novel adaptive routing algorithm for multiple subscribers in wireless mesh networks. We view a mesh network with multiple nodes as an entity that optimizes some global utility function constrained by the underlying MAC layer interference. By solving the optimization problem, the network is driven to an efficient operating point with a certain routing policies for each node. We then use this operating point information to adaptively find better paths, which is able to gear the network towards optimal routing. Further, we take the fluctuations of the network into consideration and thus render our algorithm more robust for a variety of network situations. Simulations demonstrate the efficiency and efficacy of our algorithm.     

Channel allocation algorithms for WLANs using distributed optimization

The performance of a wireless local area network (WLAN) depends on the channel assignments among interfering access points (APs). Due to the limited number of non-overlapping channels, severe interference scenarios may arise if no appropriated spectrum planning is employed. In our study we focus on WLANs scenarios where APs may belong to different administrative domains, which is actually a very common situation in dense urban deployments. In such cases the use of centralized algorithms is not feasible and the already proposed distributed methods does not guarantee optimal channel assignment. In this paper, therefore, we formalize the channel allocation as a distributed constraint optimization problem (DCOP) and propose a new distributed channel assignment algorithm named DCAA-O, which can find the optimal solution to the channel assignment problem for a group of APs. A suboptimal strategy denoted DCAA-S is also investigated, which aims at reducing the number of control messages to be exchanged between APs, while still achieving a suboptimal solution which is very close to the optimal one. The simulation results show that the proposed algorithms are able to outperform the best known techniques both in terms of solution quality and number of exchanged messages.     

Load-balance cell association for improving network capacity in heterogeneous cellular networks

Heterogeneous cellular networks improve the spectrum efficiency and coverage of wireless communication networks by deploying low power base station （BS） overlapping the conventional macro cell. But due to the disparity between the transmit powers of the macro BS and the low power BS, cell association strategy developed for the conventional homogeneous networks may lead to a highly unbalanced traffic loading with most of the traffic concentrated on the macro BS. In this paper, we propose a load-balance cell association scheme for heterogeneous cellular network aiming to maximize the network capacity. By relaxing the association constraints, we can get the upper bound of optimal solution and convert the primal problem into a convex optimization problem. Furthermore we propose a Lagrange multipliers based distributed algorithm by using Lagrange dual theory to solve the convex optimization, which converges to an optimal solution with a theoretical performance guarantee. With the proposed algorithm, mobile terminals （MTs） need to jointly consider their traffic type, received signal-to-interference-noise-ratios （SINRs） from BSs, and the load of BSs when they choose server BS. Simulation results show that the load balance between macro and pico BS is achieved and network capacity is improved significantly by our proposed cell association algorithm.     

Evaluating a cross-layer approach for routing in Wireless Mesh Networks

Routing in Wireless Mesh Networks is challenging due to the unreliable characteristics of the wireless medium. Traditional routing paradigms are not able to propose an efficient solution to this problem. Further, Gupta et al. demonstrated that the average throughput capacity per node of a wireless multi-hop network decreases as 1/n, where n is the number of nodes in the network. Recent studies have shown that a cross-layer approach is a promising solution to get closer to the theoretic throughput capacity bound. Cross-layer solutions have been already proposed either for specific TDMA/CDMA networks or for power-efficient routing protocols. These proposals are strongly MAC dependent, or suffer from targeting a steady state offering the best trade-off performance. In this paper, the problem we tackle in a more general context, disregarding the specific MAC and Physical layers technologies, can be formulated as follows: How to design a routing algorithm able to increase the average throughput capacity experienced by Wireless Mesh Networks? Starting from a theoretic result, we analyze the gain that a cross-layer approach can deliver, the metrics suitable to improve throughput capacity, and the power control policy that reduces interference. We take a MAC independent approach, focusing on the general characteristics of wireless links, targeting the improvement of throughput capacity in Wireless Mesh Networks. Our proposal performs path selection and power optimization based on three metrics, namely physical transmission rate, interference, and packet error rate. Performances are thoroughly analyzed and evaluated by extensive simulations, with both TCP and UDP traffic, and compared to other multi-hop routing protocols. For both kind of traffic, the simple heuristic we propose here allows to double the average throughput the network is able to route.     

Power Control for Link Quality Protection in Cellular DS-CDMA Networks with Integrated (Packet and Circuit) Services

The problem of admission control in a DS-CDMA network carrying a heterogeneous mix of traffic is addressed. In an interference limited system such as DS-CDMA, admission of a new user impacts the performance of all other users, as well as the system capacity. The admission process is concerned with two factors: (1) maintaining the QoS of active users, (2) allocating bandwidth to new users. We propose a simple power control algorithm and prove that it is optimal in the sense of maintaining active link quality while maximizing free capacity for new admissions. The algorithm scales the powers of active links appropriately to achieve link protection and improved tolerance of the link to new interference from bursty sources. This algorithm can be used to overlay bursty packet data services without compromising QoS of active circuits.     

Interference management using basestation coordination in broadband wireless access networks

This paper proposes a transmission‐scheduling algorithm for interference management in broadband wireless access networks. The algorithm aims to minimize the cochannel interference using basestation coordination while still maintaining the other quality of service (QoS) requirements such as packet delay, throughput and packet loss. The interference reduction is achieved by avoiding (or minimizing) concurrent transmission of potential dominant interferers. Dynamic slot allocation based on traffic information in other cells/sectors is employed. In order to implement the algorithm in a distributed manner, basestations (BSs) have to exchange traffic information. Both real‐time and non‐real‐time services are considered in this work. Results show that significant reduction in the packet error rate can be achieved without increasing the packet delay at low to medium loading values and with a higher but acceptable packet delay at high loading values. Since ARQ schemes can also be used for packet error rate reduction, we compare the performance of the proposed scheme with that of ARQ. Results indicate that although ARQ is more effective in reducing packet error rate, the proposed algorithm incurs much less packet delay particularly at medium to high loading. Copyright © 2006 John Wiley & Sons, Ltd.     

A generalized algorithm for constrained power control withcapability of temporary removal

The distributed constrained power control algorithm (DCPC) (Grandhi et al. 1995) has become one of the most widely accepted quality-based power control algorithms by the academic community. The DCPC has a property that the power may reach the maximum level when a user is experiencing degradation of channel quality. Unfortunately, using maximum transmitter power may not lead to sufficient improvement of channel quality and will thereto generate severe interference, affecting other users. This undesirable phenomenon happens more often when the system is congested. In this paper, we revisit and generalize the DCPC algorithm in order not to necessarily utilize the maximum power when the channel quality is poor. Under poor quality conditions, rather than combating the interference by maximizing power, we propose the concept of reducing the powers and temporarily removing users from the channel. We show that the energy consumption can be reduced through our generalized algorithm and we prove its convergence properties. Because of the decreased interference power, the capacity of the system is expected to increase. To validate this, we made computational experiments suggesting that the proposed algorithm can support more users in a congested system as compared to the previously suggested distributed removal algorithm gradual removals restricted (GRR)-DCPC (Andersin et al. 1996)     

Near-optimal reinforcement learning framework for energy-aware sensor communications

We consider the problem of average throughput maximization per total consumed energy in packetized sensor communications. Our study results in a near-optimal transmission strategy that chooses the optimal modulation level and transmit power while adapting to the incoming traffic rate, buffer condition, and the channel condition. We investigate the point-to-point and multinode communication scenarios. Many solutions of the previous works require the state transition probability, which may be hard to obtain in a practical situation. Therefore, we are motivated to propose and utilize a class of learning algorithms [called reinforcement learning (RL)] to obtain the near-optimal policy in point-to-point communication and a good transmission strategy in multinode scenario. For comparison purpose, we develop the stochastic models to obtain the optimal strategy in the point-to-point communication. We show that the learned policy is close to the optimal policy. We further extend the algorithm to solve the optimization problem in a multinode scenario by independent learning. We compare the learned policy to a simple policy, where the agent chooses the highest possible modulation and selects the transmit power that achieves a predefined signal-to-interference ratio (SIR) given one particular modulation. The proposed learning algorithm achieves more than twice the throughput per energy compared with the simple policy, particularly, in high packet arrival regime. Beside the good performance, the RL algorithm results in a simple, systematic, self-organized, and distributed way to decide the transmission strategy.     

An algorithm for global optimization of optical communication systems

We present a global optimization algorithm especially appropriate for the optimization of optical communication systems. It is independent of external parameters and converges to the global optimum, considerably reducing the simulation time. The algorithm is used to optimize the fiber launch powers and dispersion map of a single-channel OOK, DPSK and DQPSK at several data rates.     

Adaptive Counting Rule for Cooperative Spectrum Sensing Under Correlated Environments

We propose in this paper a dual-antenna-array (with transmitter antenna array and receiver antenna array) architecture, where the antenna elements are divided into several antenna element sets and each traffic channel is transmitted over an antenna element set, to realize the multiple traffic channels set up by a user. A SINR feedback based algorithm, which can regulate the transmission rate by iteratively adjusting the power on each traffic channel, is proposed to execute the rate control for the proposed dual-antenna-array architecture under cochannel interference. It is shown that the proposed algorithm can make the throughput meet the throughput requirement or achieve the weighted bandwidth sharing for certain fairness. In addition, we further propose a traffic channel configuration algorithm to help the SINR feedback based algorithm find the optimal traffic channel configuration that can meet the throughput requirement for each traffic channel or results in the maximal total throughput for each user.     

Topology control for multi-channel multi-radio wireless mesh networks using directional antennas

Directional antennas are widely used technologies for reducing signal interference and increasing spatial reuse. In this paper, we propose a topology control method for multi-channel multi-radio wireless mesh networks that use directional antennas. We are given a set of mesh routers installed in a region and some of them are gateway nodes that are connected to the Internet via wired lines. Each router has a traffic demand (Internet access traffic) generated from the end-users. The problem is how to adjust antenna orientations of radios and assign channels to them to construct a logical network topology, such that the minimum delivery ratio of traffic demands of routers is maximized. We first formulate the problem to an equivalent optimization problem with a clearer measurable metric, which is to minimize the largest interfering traffic of links in the network. We then propose a three-step solution to solve the problem. Firstly, we construct a set of routing trees, with the objective to balance the traffic among tree links. Secondly, we assign the radios of a node to the links it needs to serve, such that the total traffic load of the links that each radio serves is as balanced as possible. Thirdly, we do a fine-grained adjustment of antenna orientations and assign channels to them, such that the transmission area of each antenna will cover all the links it serves and the largest interfering traffic of links is minimized.     

Nonlinear optimization algorithm using monotonically increasing quantization resolution

We propose a quantized gradient search algorithm that can achieve global optimization by monotonically reducing the quantization step with respect to time when quantization is composed of integer or fixed-point fractional values applied to an optimization algorithm. According to the white noise hypothesis states, a quantization step is sufficiently small and the quantization is well defined, the round-off error caused by quantization can be regarded as a random variable with identically independent distribution. Thus, we rewrite the searching equation based on a gradient descent as a stochastic differential equation and obtain the monotonically decreasing rate of the quantization step, enabling the global optimization by stochastic analysis for deriving an objective function. Consequently, when the search equation is quantized by a monotonically decreasing quantization step, which suitably reduces the round-off error, we can derive the searching algorithm evolving from an optimization algorithm. Numerical simulations indicate that due to the property of quantization-based global optimization, the proposed algorithm shows better optimization performance on a search space to each iteration than the conventional algorithm with a higher success rate and fewer iterations.     

Delay and capacity in MANETs under random walk mobility model

The closed-form results for delay and capacity in mobile ad hoc networks are important for the performance analysis of different transmission protocols. Most existing works focus on independent and identically distributed mobility model, which is always regarded as an idealized global model. In this paper, we extend the investigation to the random walk model, which characterizes practical situations more accurately. Some local movements cause a series of complicated probabilistic problem, we develop a method to calculate the meeting probability between two randomly selected nodes under random walk mobility model. Targeting at the most commonly used routing schemes which are modeled by 2HR-f algorithm, we obtain the closed-form solutions for delay and capacity, where the wireless interference and medium access contention among nodes are considered. Extensive simulations demonstrate the accuracy of our theoretical results.     

Distributed congestion control strategy using network utility maximization theory in VANET

Cooperative vehicle safety system (CVSS) rely on periodical beacons to track neighboring vehicles.High traffic density often causes channel congestion,seriously damaging the performance of CVSS.Existing congestion control strategies aim to ensure the performance in network layer,without considering the service requirements of vehicles in different driving contexts.To solve the problem,a distributed congestion control strategy using network utility maximization (NUM) theory was proposed.First of all,the NUM model for channel resource allocation was introduced.A utility function reflecting vehicle’s safety requirements was proposed in the model.Then under the condition of fixed transmit powers,a optimization problem of channel resource allocation was proposed.Lastly,to solve the optimization problem,a distributed congestion control algorithm named utility-based rate congestion control (UBRCC) algorithm was designed,the algorithm worked out the optimal beaconing rate by updating vehicle’s congestion price,realizing the resource allocation according to vehicle’s safety requirements.Simulation results validate that UBRCC algorithm can efficiently control channel congestion,reduce transmission delay,ensure reliable data transmission and satisfies the requirements of safety applications.     

Optimal backpressure routing for wireless networks with multi-receiver diversity

We consider the problem of optimal scheduling and routing in an ad-hoc wireless network with multiple traffic streams and time varying channel reliability. Each packet transmission can be overheard by a subset of receiver nodes, with a transmission success probability that may vary from receiver to receiver and may also vary with time. We develop a simple backpressure routing algorithm that maximizes network throughput and expends an average power that can be pushed arbitrarily close to the minimum average power required for network stability, with a corresponding tradeoff in network delay. When channels are orthogonal, the algorithm can be implemented in a distributed manner using only local link error probability information, and supports a “blind transmission” mode (where error probabilities are not required) in special cases when the power metric is neglected and when there is only a single destination for all traffic streams. For networks with general inter-channel interference, we present a distributed algorithm with constant-factor optimality guarantees.     

A Multi-Objective Optimization Scheme for Multicast Routing: A Multitree Approach

In this paper, we propose a multi-objective traffic engineering scheme using different distribution trees to multicast several flows. The aim is to combine into a single aggregated metric, the following weighting objectives: the maximum link utilization, the hop count, the total bandwidth consumption, and the total end-to-end delay. Moreover, our proposal solves the traffic split ratio for multiple trees. We formulate this multi-objective function as one with Non Linear programming with discontinuous derivatives (DNLP). Results obtained using SNOPT solver show that several weighting objectives are decreased and the maximum link utilization is minimized. The problem is NP-hard, therefore, a novel SPT algorithm is proposed for optimizing the different objectives. The behavior we get using this algorithm is similar to what we get with SNOPT solver. The proposed approach can be applied in MPLS networks by allowing the establishment of explicit routes in multicast events. The main contributions of this paper are the optimization model and the formulation of the multi-objective function; and that the algorithm proposed shows polynomial complexity.     

Gateway Placement for Throughput Optimization in Wireless Mesh Networks

In this paper, we address the problem of gateway placement for throughput optimization in multi-hop wireless mesh networks. Assume that each mesh node in the mesh network has a traffic demand. Given the number of gateways to be deployed (denoted by k) and the interference model in the network, we study where to place exactly k gateways in the mesh network such that the total throughput is maximized while it also ensures a certain fairness among all mesh nodes. We propose a novel grid-based gateway deployment method using a cross-layer throughput optimization, and prove that the achieved throughput by our method is a constant times of the optimal. Simulation results demonstrate that our method can effectively exploit the available resources and perform much better than random and fixed deployment methods. In addition, the proposed method can also be extended to work with multi-channel and multi-radio mesh networks under different interference models.