Reduced‐rank space–time adaptive interference suppression for navigation systems based on QR decomposition and approximations of basis functions

A traffic matrix can exhibit the volume of network traffic from origin nodes to destination nodes. It is a critical input parameter to network management and traffic engineering, and thus it is necessary to obtain accurate traffic matrix estimates. Network tomography method is widely used to reconstruct end-to-end network traffic from link loads and routing matrix in a large-scale Internet protocol backbone networks. However, it is a significant challenge because solving network tomography model is an ill-posed and under-constrained inverse problem. Compressive sensing reconstruction algorithms have been well known as efficient and precise approaches to deal with the under-constrained inference problem. Hence, in this paper, we propose a compressive sensing-based network traffic reconstruction algorithm. Taking into account the constraints in compressive sensing theory, we propose an approach for constructing a novel network tomography model that obeys the constraints of compressive sensing. In the proposed network tomography model, a framework of measurement matrix according to routing matrix is proposed. To obtain optimal traffic matrix estimates, we propose an iteration algorithm to solve the proposed model. Numerical results demonstrate that our method is able to pursuit the trace of each origin-destination flow faithfully.where matrices Y, A, and Z denote link loads, routing matrix, and traffic matrix, respectively. Each OD flow is a time series described by a row of traffic matrix Z. Each entry of traffic matrix Z denotes traffic volume of each OD flow. For OD nodes, the node type can affect the granularity of the traffic matrix. The node type consists of link-to-link, router-to-router and PoP-to-PoP (Point of Presence), and so on [7]. In this paper, we address the PoP-to-PoP traffic matrix of the large-scale Internet protocol (IP) backbone network. The routing matrix A, which is a deterministic matrix, is made up of entries 1 and 0. Equation (1) states a linear relationship between link loads and traffic matrix. Figure 1 depicts the framework of the network tomography method, in which one can achieve link loads Y easily from simple network management protocol (SNMP) [7]. SNMP is widely employed in IP networks for network management. In the SNMP system, a cyclic counter records the number of bytes passed on each interface. The recorded link load data is collected in the SNMP management information-base data. Then one can obtain the link load data by an SNMP poller that can circularly request the appropriate SNMP management information-based data. Moreover, routing matrix can be obtained from status information and configuration files of the network [1,7]. Meanwhile, one assumes that the routing matrix A is static unless the network topology changes [7]. Unfortunately, it is extremely difficult to solve this inference problem, because network tomography is a highly ill-posed and under-constrained inverse problem. In other words, the number of links is already much smaller than that of OD pairs in the large-s...

Section: Simulation Results and Analysismentioning

confidence: 99%

“…Then traffic matrix can be estimated by the pseudo-inverse of A Ṽ Σ . Both CS-OMP and PCA use a training set to build the reconstruction model, and draw on idea from 'low rank' [8,24]. Thus, we will compare them in our simulations.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

A compressive sensing‐based network tomography approach to estimating origin–destination flow traffic in large‐scale backbone networks

Nie

Jiang

2014

“…Filter tap weights can be computed on the basis of minimizing the total error between received signal and a training signal, and commonly used adaptive algorithms for this purpose are minimum mean square error, least mean square, recursive least square, and so on. Another kind of adaptive filtering called spatial filtering or beamforming uses antenna arrays to maximize signal‐to‐interference‐plus‐noise ratio (SINR) and to eliminate the effect of interfering or jamming signals. Antennas in the array are weighted such that the beam becomes steered in direction of desired signals with nulls in direction of interfering or jamming signals.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Beamforming employs antenna arrays to focus transmission or reception in a particular direction and to place nulls in direction of jamming signals. Here, we employ beamforming to focus reception toward desired transmitter and place nulls in the direction of jammer.…”

Section: System Modelmentioning

confidence: 99%

An energy‐efficient anti‐jam cognitive system for wireless OFDM communication

Imran

Loan

Mahmood

2013

SUMMARY In this paper, anti‐jamming capabilities are proposed for a generic OFDM‐based wireless communication system. The performance of a wireless system may severely degrade in presence of jamming or intentional interferences. A malicious entity can send strong interference or noisy signals in the same transmit frequency band thus preventing the intended receiver to correctly decode the transmitted data. As a countermeasure, our work proposes algorithms and techniques to restore the system performance in presence of such malicious entities. Proposed anti‐jamming system consists of an adaptive framework that adapts itself to varying jammer behavior and wireless environment, and then, it chooses an optimal strategy to effectively cancel out the effect of jamming and channel fading. Optimization problem is formulated as a multi‐objective criterion for maximization of system throughput and minimization of energy consumption. Simulation results computed in Rayleigh fading channels with different wideband jamming modes and powers show that the proposed system effectively cancels out the jamming and channel fading effects thus maximizing the system performance and minimizing cost of energy consumption. Copyright © 2013 John Wiley & Sons, Ltd.

“…Interference mitigation solutions are critically required for the various types of wireless systems, where the same radio resources are fully shared using multiple transmitters [1][2][3][4]. For example, inter-cell interference (ICI) coordination techniques can be employed to maximize the potential capacity of an interference-limited cellular system, for example, 3GPP Long-term evolution (LTE) networks.…”

Section: Introductionmentioning

confidence: 99%

Inter‐cell coordinated beamforming with opportunistic scheduling

Kim

Kang

2015

SUMMARYThis paper proposes a distributed coordination framework with opportunistic scheduling among multiple users as opposed to the existing works on the multiple-cell cooperative beamforming problem that deals with a single active user in each cell. In this cross-layer design framework that deals with the beamforming in the physical layer and multiuser scheduling in the upper layer, radio resource management and inter-cell coordination issues are jointly considered to improve the cell-edge throughput performance by trading off their individual benefit in an optimal manner. Our simulation results demonstrated that its performance can reach up to 85% of its upper bound at the cell boundary.