Distributed Stochastic Power Control in Ad-hoc Networks: A Nonconvex Case

Utility-based power allocation in wireless ad-hoc networks is inherently nonconvex because of the global coupling induced by the co-channel interference. To tackle this challenge, we first show that the globally optimal point lies on the boundary of the feasible region, which is utilized as a basis to transform the utility maximization problem into an equivalent max-min problem with more structure. By using extended duality theory, penalty multipliers are introduced for penalizing the constraint violations, and the minimum weighted utility maximization problem is then decomposed into subproblems for individual users to devise a distributed stochastic power control algorithm, where each user stochastically adjusts its target utility to improve the total utility by simulated annealing. The proposed distributed power control algorithm can guarantee global optimality at the cost of slow convergence due to simulated annealing involved in the global optimization. The geometric cooling scheme and suitable penalty parameters are used to improve the convergence rate. Next, by integrating the stochastic power control approach with the back-pressure algorithm, we develop a joint scheduling and power allocation policy to stabilize the queueing systems. Finally, we generalize the above distributed power control algorithms to multicast communications, and show their global optimality for multicast traffic.Comment: Contains 12 pages, 10 figures, and 2 tables; work submitted to IEEE Transactions on Mobile Computin

On the Complexity of Scheduling in Wireless Networks

We consider the problem of throughput-optimal scheduling in wireless networks subject to interference constraints. We model the interference using a family of K-hop interference models, under which no two links within a K-hop distance can successfully transmit at the same time. For a given K, we can obtain a throughput-optimal scheduling policy by solving the well-known maximum weighted matching problem. We show that for K > 1, the resulting problems are NP-Hard that cannot be approximated within a factor that grows polynomially with the number of nodes. Interestingly, for geometric unit-disk graphs that can be used to describe a wide range of wireless networks, the problems admit polynomial time approximation schemes within a factor arbitrarily close to 1. In these network settings, we also show that a simple greedy algorithm can provide a 49-approximation, and the maximal matching scheduling policy, which can be easily implemented in a distributed fashion, achieves a guaranteed fraction of the capacity region for "all K." The geometric constraints are crucial to obtain these throughput guarantees. These results are encouraging as they suggest that one can develop low-complexity distributed algorithms to achieve near-optimal throughput for a wide range of wireless networksopen1

Joint-optimal probing and scheduling in wireless systems

Consider a wireless system where a sender can transmit data to various users with independent and varying channel conditions. To maximize its long-term transmission rate, the sender should always transmit to the user with the best channel. To discover which user has the best channel, it has to spend time to probe channels, and this reduces the time available for effective transmission. This paper aims at identifying optimal joint probing and scheduling strategies. These strategies realize the best trade-off between the channel state acquisition and effective transmission. We first provide general structural properties of optimal strategies, and then exactly characterize these strategies in particular but relevant cases. Finally we propose extensions of this problem, e.g., to impose fairness among the users, we investigate how to maximize system utility rather than throughput

Adaptive network coding and scheduling for maximizing throughput in wireless networks

Recently, network coding emerged as a promising technology that can provide significant improvements in throughput and energy efficiency of wireless networks, even for unicast communication. Often, network coding schemes are designed as an autonomous layer, independent of the underlying Phy and MAC capabilities and algorithms. Consequently, these schemes are greedy, in the sense that all opportunities of broadcasting combinations of packets are exploited. We demonstrate that this greedy design principle may in fact reduce the network throughput. This begets the need for adaptive network coding schemes. We further show that designing appropriate MAC scheduling algorithms is critical for achieving the throughput gains expected, from network coding. In this paper, we propose a general framework to develop optimal and adaptive joint network coding and scheduling schemes. Optimality is shown for various Phy and MAC constraints. We apply this framework to two different network coding architectures: COPE, a scheme recently proposed in [7], and XOR-Sym, a new scheme we present here. XOR-Sym is designed to achieve a lower implementation complexity than that of COPE, and yet to provide similar throughput gains

Channel partitioning and relay placement in Multi-hop cellular networks

Relay based multihop cellular system is a promising technique to improve the signal quality in wireless networks. Here, we propose the channel partitioning and relay positioning schemes so as to maximize the number of users admitted in a cell. A user is admitted if the required rate can be provided to it. Based on this scheme, Signal to Interference and Noise Ratio (SINR) received by Base Station (BS) and Fixed Relay Nodes (FRN) are analyzed with emphasis on users at disadvantaged places (worst case SINR). The results show that, optimally deploying FRNs reduced the call blocking rates at least by 30% and improve the SINR significantly

Proportionally Fair Resource Allocation in Multirate WLANs

Since IEEE has a standardized 802.11 protocol for wireless local-area networks (WLANs), significant work has been done to develop rate adaptation algorithms. Most of the rate adaptation algorithms proposed till now are heuristic, suboptimal, and are competitive in nature. Even though these algorithms have the advantage of being implemented in distributed fashion, their throughput performance will be low as these schemes may converge to inefficient Nash equilibrium. On the other hand, users may cooperatively choose their rates so that a social optimum can be achieved, but there are no known algorithms that do rate adaptation cooperatively and can still be implemented in a distributed fashion. In this paper, we design a centralized algorithm that achieves a social optimum (by conducting rate adaptation cooperatively) while guaranteeing proportional fairness. We show that it converges in, at most, N iterations and has time complexity O(N-2), where N is the number of users in the system. Furthermore, we propose a distributed algorithm that also serves the same purpose as the centralized algorithm

Joint Cell Zooming and Channel Allocation Using C-SAP for Large Action Sets

Optimizing energy consumption is paramount to sustain the growth of cellular networks. One of the approaches to reduce energy consumption is traffic dependent operation of networks. The traffic demand experienced by the network fluctuates over the duration of a day. Therefore, during the periods of low traffic, we may re-configure the network to trade the excess capacity for energy reduction. For example, we may modulate the BS transmit power (Cell Zooming) so as to maintain the desired QoS. However, determining an optimal network configuration is known to be computationally hard. Along with Cell Zooming, it is essential to also consider channel re-assignment, for, when the transmit powers of BSs are changed, the channels allocated to BSs must also be suitably changed. Considering therefore channel allocations also as state variables, the search space over which the optimization should be performed blows up, further complicating the problem. In this paper, we propose a framework to address this problem. The proposed algorithmis suitable for such large search spaces, while the framework is general enough to admit several QoS requirements and sophisticated power consumption models. The underlying mathematical formulation is also applicable in other contexts, and is of independent interest as well

A network architecture for providing per-flow delay guarantees with scalable core

Many real-time applications demand delay guarantees from the network. A network architecture designed to support these applications should be robust and scalable. The IntServ architecture provides per-flow QoS at the cost of robustness and scalability. The DiffServ architecture is robust and scalable but can provide QoS at a class level and not at a flow level. In this paper, our aim is to design architectures that are scalable and robust like DiffServ and at the same time able to provide per-flow QoS like IntServ. We propose a non work-conserving and a work-conserving architecture to achieve this goal. The guaranteeable delay regions of these architectures are the same as those of GPS based policies with rate proportional resource allocation. We also propose a scheme to provide meaningful throughput and responsiveness to best effort traffic even in the presence of heavy QoS load

Power Optimal Signaling for Fading Multi-Access Channel in Presence of Coding Gap

In a multi-access fading channel, dynamic allocation of bandwidth, transmission power and rates is an important aspect to counter the detrimental effect of time-varying nature of the channel. Most of the existing work on dynamic resource allocation assumes capacity achieving codes for various signaling schemes like TDMA, FDMA, CDMA and successive decoding. For the capacity achieving codes, the rate achievable by the user is log(1 + SNR), where SNR denotes the signal to noise ratio of the user at the receiver side. However, codes that are used in practice have a finite gap to capacity, i.e., the achievable rate is log(1 + SNR/Gamma) for Gamma > 1. The exact value of Gamma depends on the coding strategy and the desired bit error rate. Many existing resource allocation techniques that are optimal for capacity achieving codes perform sub-optimally in presence of the coding gap. For example, successive decoding does not always minimize the sum power required for providing the desired rate to each of the users for Gamma > 1. The problem of minimizing the sum power while guaranteeing the required rate to each of the users is important for both real-time and non real-time applications, and is addressed here. We obtain the resource allocation that is optimal for the above problem in presence of the coding gap