An estimator of regulator parameters in a stochastic setting

This paper develops a new network provisioning and resource allocation scheme. We introduce the concept of the effective burstiness curve (EBC), which is defined as a percentile of the maximum burstiness curve. For a fixed service rate, EBC represents the size of a buffer for which the probability of buffer overflow is arbitrarily small. We show that EBC is a convex nonincreasing function of the service rate. We also introduce the empirical effective burstiness curve (EEBC), an estimator of EBC, which can be obtained with a water-filling algorithm. For discrete queue size, EEBC can be evaluated with a recursive algorithm. The technique is applied to MPEG4 encoded video traces.     

A recursive estimator of worst-case burstiness

The leaky-bucket regulator has several potential roles in the operation of future transport networks; among them, the bounding of possible source trajectories in implementations of worst-case approaches to network design. It seems plausible that there will be applications whose specific traffic characteristics are known a priori neither to the user nor to the network; in such cases, a recursive algorithm for setting the leaky-bucket parameters may prove useful. We devise such an algorithm here. The leaky-bucket parameters are computed recursively over a limited period of observation of the source behavior. We provide an explicit characterization of the dynamics of the estimator, and the results of a simulation study of performance in the case of real source trajectories     

Delay Aware Link Scheduling for Multi-Hop TDMA Wireless Networks

Time division multiple access (TDMA) based medium access control (MAC) protocols can provide QoS with guaranteed access to the wireless channel. However, in multi-hop wireless networks, these protocols may introduce scheduling delay if, on the same path, an outbound link on a router is scheduled to transmit before an inbound link on that router. The total scheduling delay can be quite large since it accumulates at every hop on a path. This paper presents a method that finds conflict-free TDMA schedules with minimum scheduling delay. We show that the scheduling delay can be interpreted as a cost, in terms of transmission order of the links, collected over a cycle in the conflict graph. We use this observation to formulate an optimization, which finds a transmission order with the min-max delay across a set of multiple paths. The min-max delay optimization is NP-complete since the transmission order of links is a vector of binary integer variables. We devise an algorithm that finds the transmission order with the minimum delay on overlay tree topologies and use it with a modified Bellman-Ford algorithm, to find minimum delay schedules in polynomial time. The simulation results in 802.16 mesh networks confirm that the proposed algorithm can find effective min-max delay schedules.     

Data uploading time estimation for CUBIC TCP in long distance networks

CUBIC is a TCP-friendly algorithm that uses a cubic curve, independent of the round-trip time, to rapidly recover from a packet loss. New releases of Linux use CUBIC for the TCP protocol. In this paper, we show that if the socket buffer size of a sender TCP is small compared with the bandwidth-delay product, Linux TCP window size drops to almost zero every time a packet loss occurs. Using this fact, we estimate data uploading time in long distance networks with packet loss. Also we discuss the improvement of the uploading time by increasing cumulative socket buffer size in two ways: large buffer size or parallel connections.     

Information raining and optimal link-layer design for mobile hotspots

In this paper, we propose a link layer design for mobile hotspots. We design a novel system architecture that enables high-speed Internet access in railway systems. The proposed design uses a number of repeaters placed along the track and multiple antennas installed on the roof of a vehicle. Each packet is decomposed into smaller fragments and relayed to the vehicle via adjacent repeaters. We also use erasure coding to add parity fragments to original data. This approach is called information raining since fragments are rained upon the vehicle from adjacent repeaters. We investigate two instances of information raining. In blind information raining, all repeaters awaken when they sense the presence of the vehicle. The fragments are then blindly transmitted via awakened repeaters. A vehicle station installed inside the train is responsible for aggregating a large enough number of fragments. In the throughput-optimized information raining, the vehicle station selects a bipartite matching between repeaters and roof-top antennas and activates only a subset of the repeaters. It also dictates the amount of transmission power of each activated repeater. Both the bipartite matching and power allocations are individually shown to be NP-complete. Matching heuristics based on the Hungarian algorithm and Gale-Shapley algorithm are proposed. A simplex-type algorithm is proposed as the power allocation heuristics.     

Wideband array processing using a two-sided correlationtransformation

A new method for broadband array processing is proposed. The method is based on unitary transformation of the signal subspaces. We apply a two-sided transformation on the correlation matrices of the array. It is shown that the two-sided correlation transformation (TCT) has a smaller subspace fitting error than the coherent signal-subspace method (CSM). It is also shown that unlike CSM, the TCT algorithm can generate unbiased estimates of the directions-of-arrival, regardless of the bandwidth of the signals. The capability of the TCT and CSM methods for resolving two closely spaced sources is compared. The resolution threshold for the new technique is much smaller than that for CSM     

Parametric localization of distributed sources

Most array processing algorithms are based on the assumption that the signals are generated by point sources. This is a mathematical constraint that is not satisfied in many applications. In this paper, we consider situations where the sources are distributed in space with a parametric angular cross-correlation kernel. We propose an algorithm that estimates the parameters of this model using a generalization of the MUSIC algorithm. The method involves maximizing a cost function that depends on a matrix array manifold and the noise eigenvectors. We study two particular cases: coherent and incoherent spatial source distributions. The spatial correlation function for a uniformly distributed signal is derived. From this, we find the array gain and show that (in contrast to point sources) it does not increase linearly with the number of sources. We compare our method to the conventional (point source) MUSIC algorithm. The simulation studies show that the new method outperforms the MUSIC algorithm by reducing the estimation bias and the standard deviation for scenarios with distributed sources. It is also shown that the threshold signal-to-noise ratio required for resolving two closely spaced distributed sources is considerably smaller for the new method