We develop a general model to estimate the throughput and goodput between arbitrary pairs of nodes in the presence of interference from other nodes in a wireless network. Our model is based on measurements from the underlying network itself and is thus more accurate than abstract models of RF propagation such as those based on distance. The seed measurements are easy to gather, requiring only O(N) measurements in an N-node networks. Compared to existing measurement-based models, our model advances the state of the art in three important ways. First, it goes beyond pairwise interference and models interference among an arbitrary number of senders. Second, it goes beyond broadcast transmissions and models the more common case of unicast transmissions. Third, it goes beyond homogeneous nodes and models the general case of heterogeneous nodes with different traffic demands and different radio characteristics. Using simulations and measurements from two different wireless testbeds, we show that the predictions of our model are accurate in a wide range of scenarios.

We present a practical, measurement-based model that captures the effect of interference in 802.11-based wireless LAN or mesh networks. The goal is to model capacity of any given link in the presence of any given number of interferers in a deployed network, carrying any specified amount of offered load. Central to our modeling approach is a MAC-layer model for 802.11 that is fed by PHY-layer models for deferral and packet capture behaviors, which in turn are profiled based on measurements. The target network to be evaluated needs only O(N) measurement steps to gather metrics for individual links that seed the models. We provide two solution approaches – one based on direct simulation (slow, but accurate) and the other based on analytical methods (faster, but approximate). We present elaborate validation results for a 12 node 802.11b mesh network using upto 5 interfering transmissions. We demonstrate, using as comparison points three simpler modeling approaches, that the accuracy of our approach is much better, predicting link capacities with errors within 10 % of the base channel datarate for about 90% of the cases.

Complex interference in static multi-hop wireless mesh networks can adversely affect transport protocol performance. Since TCP does not explicitly account for this, starvation and unfairness can result from the use of TCP over such networks. In this paper, we explore mechanisms for achieving fair and efficient congestion control for multi-hop wireless mesh networks. First, we design an AIMD-based rate-control protocol called Wireless Control Protocol (WCP) which recognizes that wireless congestion is a neighborhood phenomenon, not a node-local one, and appropriately reacts to such congestion. Second, we design a distributed rate controller that estimates the available capacity within each neighborhood, and divides this capacity to contending flows, a scheme we call Wireless Control Protocol with Capacity estimation (WCPCap). Using analysis, simulations, and real deployments, we find that our designs yield rates that are both fair and efficient, and achieve near optimal goodputs for all the topologies that we study. WCP achieves this level of performance while being extremely easy to implement. Moreover, WCPCap achieves the max-min rates for our topologies, while still being distributed and amenable to real implementation.

In this paper, we characterize the achievable rate region for any 802.11-scheduled static multi-hop network. To do so, we first characterize the achievable edge-rate region, that is, the set of edge rates that are achievable on the given topology. This requires a careful consideration of the inter-dependence among edges, since neighboring edges collide with and affect the idle time perceived by the edge under study. We approach this problem in two steps. First, we consider two-edge topologies and study the fundamental ways by which they interact. Then, we consider arbitrary multi-hop topologies, compute the effect that each neighboring edge has on the edge under study in isolation, and combine to get the aggregate effect. We then use the characterization of the achievable edge-rate region to characterize the achievable rate region. We verify the accuracy of our analysis by comparing the achievable rate region derived from simulations with the one derived analytically. We make a couple of interesting and somewhat surprising observations while deriving the rate regions. First, the achievable rate region with 802.11 scheduling is not necessarily convex. Second, the performance of 802.11 is surprisingly good. For example, in all the topologies used for model verification, the max-min allocation under 802.11 is at least 64 % of the max-min allocation under a perfect scheduler.

802.11 wireless mesh networks are ubiquitous, but suffer from severe performance degradations due to poor synergy between the 802.11 CSMA MAC protocol and higher layers. Several solutions have been proposed that either involve significant modifications to the 802.11 MAC or legacy higher layer protocols, or rely on 802.11 MAC models seeded with off-line measurements performed during network downtime. We introduce a technique for online optimization of 802.11 wireless mesh networks using rate control at the network layer. The technique is based on a lightweight model that characterizes the feasible rates region of an operational 802.11 wireless mesh network. Unlike existing 802.11 modeling approaches, the parameters of this model can be estimated online, incur minimal overhead and can be realized using standard probing mechanisms at the network layer. Using analysis and extensive measurements over a wireless mesh network testbed, we validate the assumptions on which the model is built, and explain the principles behind the choice and estimation of its parameters. The benefits of the model and its solution in terms of fairness, throughput and stability are demonstrated operationally for a range of multi-hop topologies and configurations.

Abstract — This paper present an throughput analysis of logutility and max-min fairness. Assuming all nodes interfere with each other, completely or partially, log-utility fairness significantly enhances the total throughput compared to max-min fairness since the nodes should have the same throughput in max-min fairness. The improvement is enlarged especially when the effect of cumulated interference from multiple senders cannot be ignored. I. LOG-UTILITY AND MAX-MIN FAIRNESS In this paper, we focus on dense wireless networks using random access protocols such as 802.11 LANs in offices and urban residential areas. We mainly consider slotted-Aloha systems but our analysis is simply extended for CSMA/CS networks with a single carrier-sensing range. In typical urban residential networks, one or few terminals are located closely to their access point and tend to capture a strong signal. The dense distribution of the access points, however, enlarges the effect of cumulative interference on frame reception. For fair bandwidth allocation, there exist two famous fairness schemes: log-utility [1] and max-min fairness. Log-utility or proportional fairness has been well known as a flexible and useful abstraction for multiplexing scarce resources among users and applications. Max-min fairness, however, achieve the complete fair allocation, where all nodes have the same throughput. Consider all nodes are within the single interference range. That is, any overlapped transmissions from the nodes completely collides and that results in transmission errors. Let be the number of nodes. In this case, the nodes have the probability¡£ ¢   attempt in log-utility fairness [2] and the throughput¤¦¥¨§ total is given by:

Abstract — While physical layer capture has been observed in real implementations of wireless devices accessing the channel like 802.11, log-utility fair allocation algorithms based on accurate channel models describing the phenomenon have not been developed. In this paper, using a general physical channel model, we develop an allocation algorithm for log-utility fairness. To maximize the aggregate utility, our algorithm determines channel access attempt probabilities of nodes using partial derivatives of the utility. Our algorithm is verified through extended simulations. The results indicate that our algorithm could quickly achieve allocations close to the optimum with 8.6 % accuracy error on average.

Abstract — Random access protocols, such as Aloha, are commonly modeled in wireless ad-hoc networks by using the protocol model. However, it is well-known that the protocol model is not accurate and particularly it cannot account for aggregate interference from multiple interference sources. In this paper, we use the more accurate physical model, which is based on the signal-to-interference-plus-noise-ratio (SINR), to study optimization-based design in wireless random access systems, where the optimization variables are the transmission probabilities of the users. We focus on throughput maximization, fair resource allocation, and network utility maximization, and show that they entail non-convex optimization problems if the physical model is adopted. We propose two schemes to solve these problems. The first design is centralized and leads to the global optimal solution using a sum-of-squares technique. However, due to its complexity, this approach is only applicable to small-scale networks. The second design is distributed and leads to a closeto-optimal solution using the coordinate ascent method. This approach is applicable to medium-size and large-scale networks. Based on various simulations, we show that it is highly preferable to use the physical model for optimization-based random access design. In this regard, even a sub-optimal design based on the physical model can achieve a significantly better performance than an optimal design based on the inaccurate protocol model. I.

Abstract — In this paper, we characterize the achievable rate region for any 802.11-scheduled static multi-hop network. To do so, we first characterize the achievable edge-rate region, that is, the set of edge rates that are achievable on the given topology. This requires a careful consideration of the inter-dependence among edges, since neighboring edges collide with and affect the idle time perceived by the edge under study. We approach this problem in two steps. First, we consider two-edge topologies and study the fundamental ways by which they interact. Then, we consider arbitrary multi-hop topologies, compute the effect that each neighboring edge has on the edge under study in isolation, and combine to get the aggregate effect. We then use the characterization of the achievable edge-rate region to characterize the achievable rate region. We verify the accuracy of our analysis by comparing the achievable rate region derived from simulations with the one derived analytically. We make a couple of interesting and somewhat surprising observations while deriving the rate regions. First, the achievable rate region with 802.11 scheduling is not necessarily convex. Second, the performance of 802.11 is surprisingly good. For example, in all the topologies used for model verification, the max-min allocation under 802.11 is at least 64 % of the max-min allocation under a perfect scheduler. Index Terms — IEEE 802.11, Capacity Region, Muti-Hop Networks. I.

Abstract—In a slow fading environment, the carrier sense range is not constant, so there is not a constant set of hidden terminals for a mobile station. The probability of capture with a set of interferers is not a fixed value either, and it fundamentally affects the loss rate and throughput of the whole network. We estimate the expectation of the capture probability in a single hop ad hoc network and incorporate it with our previously proposed model for 802.11 DCF that considers the time-varying nature of carrier sensing. The system throughput is then derived from an individual station’s point of view. The model is verified against simulations, and extensive numerical experiments are performed to demonstrate its application. I.