92 research outputs found
Auction based competition of hybrid small cells for dropped macrocell users
We propose an auction based beamforming and user association algorithm for a wireless
network consisting of a macrocell and multiple small cell access points (SCAs). The SCAs compete for serving the macrocell base station (MBS) users (MUs). The corresponding user association problem is solved by the proposed bid-wait auction (BWA) method. We considered two scenarios. In the first scenario, the MBS initially admits the largest possible set of MUs that it can serve simultaneously and then auctions off the remaining MUs to the SCAs, who are willing to admit guest users (GUs) in addition to their commitments to serve their own host users (HUs). This problem is solved by the proposed forward bid-wait auction (FBWA). In the second scenario,
the MBS aims to offload as many MUs as possible to the SCAs and then admits the largest possible set of remaining MUs. This is solved by the proposed backward bid-wait auction (BBWA). The proposed algorithms provide close to optimum solution as if obtained using a centralised global
optimization
Robust waveform design for multistatic cognitive radars
In this paper we propose robust waveform techniques for multistatic cognitive radars in a signal-dependent clutter environment. In cognitive radar design, certain second order statistics such as the covariance matrix of the clutter, are assumed to be known. However, exact knowledge of the clutter parameters is difficult to obtain in practical scenarios.
Hence we consider the case of waveform design in the presence of uncertainty on the knowledge of the clutter environment
and propose both worst-case and probabilistic robust waveform design techniques. Initially, we tested our multistatic, signaldependent
model against existing worst-case and probabilistic methods. These methods appeared to be over conservative and generic for the considered scenario. We therefore derived a new approach where we assume uncertainty directly on the radar cross-section and Doppler parameters of the clutters.
Accordingly, we propose a clutter-specific stochastic optimization that, by using Taylor series approximations, is able to determine
robust waveforms with specific Signal to Interference and Noise Ratio (SINR) outage constraints
Space-time block coding for four transmit antennas with closed loop feedback over frequency selective fading channels
Orthogonal space-time block coding is a transmit diversity method that has the potential to enhance forward capacity. For a communication system with a complex alphabet, full diversity and full code rate space-time codes are available only for two antennas, and for more than two antennas full diversity is achieved only when the code rate is lower than one. A quasi-orthogonal code could provide full code rate, but at the expense of loss in diversity, which results in degradation of performance. We propose a closed loop feedback scheme for quasi-orthogonal codes which provides full diversity while achieving the full code rate. We investigate, in particular, the performance of this scheme, when the feedback information is quantised and when the fading of the channel is frequency-selective
Multiuser orthogonal space-division multiplexing with iterative water-filling algorithm
The problem of multiuser multiplexing with a
MIMO sub system for each individual user is considered. We
demonstrate that the capacity performance of the null space
based spatial multiplexing schemes can be improved with iterative
power allocation within the iterative design process. We
considered water-filling based local and global power allocation
and demonstrate that both schemes outperform the existing null
space based spatial diversity technique in terms of mean capacity
and outage capacity
Contemporary sequential network attacks prediction using hidden Markov model
Intrusion prediction is a key task for forecasting
network intrusions. Intrusion detection systems have been
primarily deployed as a first line of defence in a network,
however; they often suffer from practical testing and evaluation
due to unavailability of rich datasets. This paper evaluates
the detection accuracy of determining all states (AS), the
current state (CS), and the prediction of next state (NS) of
an observation sequence, using the two conventional Hidden
Markov Model (HMM) training algorithms, namely, Baum
Welch (BW) and Viterbi Training (VT). Both BW and VT were
initialised using uniform, random and count-based parameters
and the experiment evaluation was conducted on the CSE-CICIDS2018 dataset. Results show that the BW and VT countbased initialisation techniques perform better than uniform and
random initialisation when detecting AS and CS. In contrast,
for NS prediction, uniform and random initialisation techniques
perform better than BW and VT count-based approaches
Game theoretic data association for multi-target tracking with varying number of targets
We investigate a game theoretic data association technique for multi-target tracking (MTT) with varying number of targets. The problem of target state-estimate-to-track data association has been considered. We use the SMC-PHD filter to handle the MTT aspect and obtain target state estimates. We model the interaction between target tracks as a game by considering them as players and the set of target state estimates as strategies. Utility functions for the players are defined and a regret-based learning algorithm with a forgetting factor is used to find the equilibrium of the game. Simulation results are presented to demonstrate the performance of the proposed technique
Game theoretic analysis for MIMO radars with multiple targets
This paper considers a distributed beamforming
and resource allocation technique for a radar system in the
presence of multiple targets. The primary objective of each
radar is to minimize its transmission power while attaining an
optimal beamforming strategy and satisfying a certain detection
criterion for each of the targets. Therefore, we use convex
optimization methods together with noncooperative and partially
cooperative game theoretic approaches. Initially, we consider
a strategic noncooperative game (SNG), where there is no
communication between the various radars of the system. Hence
each radar selfishly determines its optimal beamforming and
power allocation. Subsequently, we assume a more coordinated
game theoretic approach incorporating a pricing mechanism.
Introducing a price in the utility function of each radar/player,
enforces beamformers to minimize the interference induced to
other radars and to increase the social fairness of the system.
Furthermore, we formulate a Stackelberg game by adding a
surveillance radar to the system model, which will play the role of
the leader, and hence the remaining radars will be the followers.
The leader applies a pricing policy of interference charged to the followers aiming at maximizing his profit while keeping the
incoming interference under a certain threshold. We also present
a proof of the existence and uniqueness of the Nash Equilibrium
(NE) in both the partially cooperative and noncooperative games.
Finally, the simulation results confirm the convergence of the
algorithm in all three cases
An improved resampling approach for particle filters in tracking
Resampling is an essential step in particle filtering (PF) methods in order to avoid degeneracy. Systematic resampling is one of a number of resampling techniques commonly used due to some of its desirable properties such as ease of implementation and low computational complexity. However, it has a tendency of resampling very low weight particles especially when a large number of resampled particles are required which may affect state estimation. In this paper, we propose an improved version of the systematic resampling technique which addresses this problem and demonstrate performance improvement
Game theoretic distributed waveform design for multistatic radar networks
We examine the interaction of multiple-input multiple-output (MIMO) based clusters of radars within a game theoretic framework, using potential games. The objective is to maximise the signal-to-disturbance ratio (SDR) of the clusters of radars, by selecting most appropriate waveforms. We prove that the proposed game theoretic algorithm converges to a unique Nash equilibrium using discrete concavity and the larger midpoint property. As a result, each cluster can determine the best waveform for illumination (equilibrium) by strategising the actions of the other clusters
Joint Transcoding Task Assignment and Association Control for Fog-assisted Crowdsourced Live Streaming
The rapid development of content delivery networks
and cloud computing has facilitated crowdsourced live-streaming
platforms (CLSP) that enable people to broadcast live videos
which can be watched online by a growing number of viewers.
However, in order to ensure reliable viewer experience, it is
important that the viewers should be provided with multiple
standard video versions. To achieve this, we propose a joint
fog-assisted transcoding and viewer association technique which
can outsource the transcoding load to a fog device pool and
determine the fog device with which each viewer will be
associated, to watch desired videos. The resulting non-convex
integer programming has been solved using a computationally
attractive complementary geometric programming (CGP). The
performance of the proposed algorithm closely matches that of
the globally optimum solution obtained by an exhaustive search.
Furthermore, the trace-driven simulations demonstrate that our
proposed algorithm is able to provide adaptive bit rate (ABR)
services
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