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CSMA/CD for Wi-Fi
Carrier Sense Multiple Access with Collision Detection (CSMA/CD) is a technique used in wired networks like Ethernet (IEEE 802.3) to improve network performance by efficient medium access. When a collision is detected, the colliding nodes terminate their transmissions to keep the collision time as short as possible. This effectively improves the utilization of the transmission medium, since less time is spent in collisions and the time between transmission attempts is reduced.
In wireless networks, however, CSMA/CD is generally assumed to be impractical due to the physical characteristics of the wireless channel. In fact, the power of a signal degrades by orders of magnitudes on its way from transmitter to receiver due to free space path loss and signal propagation effects, such as attenuation and reflections. Therefore, even if a transmitter was equipped with a separate receive antenna, its own transmission would typically drown out the weak signals from other transmitters, which would render the detection of weak signals impossible. Nevertheless, recent research has demonstrated that self-interference cancellation techniques become feasible, which allows to design full-duplex radios . This might effectively be key to the design of CSMA/CD for IEEE 802.11-based networks, allowing for enhanced network performance under high load conditions .
 Mayank Jain, Jung Il Choi, Taemin Kim, Dinesh Bharadia, Siddharth Seth, Kannan Srinivasan, Philip Levis, Sachin Katti, and Prasun Sinha. “Practical, Real-Time, Full Duplex Wireless”, 17th annual international conference on Mobile computing and networking (ACM MobiCom ‘11). Las Vegas, Nevada, USA, 2011, pp. 301-312.
 Konstantinos Voulgaris, Athanasios Gkelias, Imran Ashraf, Mischa Dohler and A. H. Aghvami. “Throughput Analysis of Wireless CSMA/CD for a Finite User Population”, IEEE Vehicular Technology Conference, Montreal, Quebec, CA, 2006, pp. 1-5.
Literature review: Review different self-interference cancellation techniques and assess their suitability for 802.11-based networks. Also review literature relating to channel access techniques.
CSMA/CD design: Make a conceptual design of a fully-fledged CSMA/CD mechanism, which also takes practical limitations into account, such as settling times of gain controls. Your design may also employ correlation techniques to detect weak signals from far-away nodes.
Implementation: Implement your CSMA/CD design on a software-defined radio. Self-interference cancellation might require a combination of well-considered antenna placement on the device, analog cancellation in the RF band, and digital cancellation in the baseband. Your implementation may be based on GNU Radio and USRP, or on WARP.
Evaluation: Evaluate the performance of individual components of your implementation (e.g., the self-interference cancellation gain), as well as the overall performance of CSMA/CD nodes in a real network, as compared to conventional CSMA/CA.
Protocol Design for Energy-Efficient Broadcast Tree Contruction in Wireless Ad-Hoc Networks
Low-Latency Flooding in IEEE 802.11g Networks through Concurrent Broadcasting with Wireless Synchronization using WARP Software-Defined Radios
Separation of Channel Coefficients in Concurrent Wi-Fi Transmissions using Deep Neural Networks
Separation of Channel Coefficients with Deep Neural Networks
The separation of channel coefficients is a time-consuming operation. In this thesis project, we are going to explore the suitability of deep neural networks (DNNs) to speed up a specific PHY-related optimization task
The goal of this project is to explore the suitability of DNNs to separate channel coefficients. The project main goals are:
Research the literature about uses of DNNs in other optimization problems
Explore suitable DNN configurations for the envisioned task
Evaluate the DNN’s performance in terms of accuracy and speed
NEAT-TCP: Generation of TCP Congestion Control through Neuroevolution of Augmenting Topologies for Wireless Multi-Hop Networks
TCP performance in wireless multi-hop networks (WMNs) is hard to achieve due to losses on the wireless channel, interferences and limited resources at individual nodes. Recent research has proposed a simple neural network (NN) structure with one input layer, two hidden layers, and one output layer that efficiently applies congestion control and that results in significant performance improvements compared to conventional TCP variants .
Further, NeuroEvolution of Augmenting Topologies (NEAT) is a method based on evolutionary algorithms that can outperform fixed-topology NNs in reinforcement learning tasks. We expect that NEAT may improve the performance of manually crafted NNs like iTCP even further.
The goal of this project is to assess the ability of NEAT to further improve the performance of an iTCP-based congestion control algorithm in the context of WMNs. The project main goals are:
Implement iTCP in a network simulation environment (ns-3)
Use NEAT to generate a modified NN structure for congestion control
Compare the performance of the modified congestion control to the initial iTCP-based version
 A. B. M. Alim Al Islam and Vijay Raghunathan, “iTCP: an intelligent TCP with neural network based end-to-end congestion control for ad-hoc multi-hop wireless mesh networks”, Wireless Networks, Volume 21, Issue 2, pp. 581–610, February 2015. doi: 10.1007/s11276-014-0799-6
 Kenneth O. Stanley and Risto Miikkulainen, “Evolving Neural Networks through Augmenting Topologies”, Evolutionary Computation 10:2, pp. 99-127, MIT Press, 2002. doi: 10.1162/106365602320169811
Practical Broadcast Tree Construction with Potential Game for Energy-Efficient Data Dissemination in Ad-Hoc Networks
This project addresses the problem of energy-efficient data dissemination from a source node to all other nodes in a wireless multi-hop network. Mahdi Mousavi et al. from the Communications Engineering Lab at TU Darmstadt have devised a decentralized algorithm towards this goal that is based on game theory . While simulation results have shown that this mechanism significantly outperforms other conventional flooding mechanisms, its practical applicability still remains unexplored.
The goal of this thesis project is to design a practical protocol that runs the game theoretical algorithm in  and to evaluate its performance in a network simulation environment. The project main goals are:
Analyze the game theoretical algorithm  for limiting assumptions
Devise a practical protocol for broadcast tree construction that is based on 
Implement this protocol in a simulation environment (ns-3)
Evaluate the energy efficiency of the constructed broadcast tree in comparison to conventional flooding techniques while taking the protocol overhead into account
 Mahdi Mousavi, Hussein Al-Shatri, Matthias Wichtlhuber, David Hausheer and Anja Klein, “Energy-Efficient Data Dissemination in Ad Hoc Networks: Mechanism Design with Potential Game”, 2015 International Symposium on Wireless Communication Systems (ISWCS), Brussels, 2015, pp. 616-620. doi: 10.1109/ISWCS.2015.7454421
Estimating Global MANET Metrics Based on Locally Observed Information
Knowledge of global network state is crucial for several innovative network optimization techniques. Essentially, incorporating knowledge about the overall network state into locally made decisions at decentralized nodes might improve the overall network performance. A node might for instance perform transitions between network mechanisms that are optimized for certain network conditions. However, an individual node’s scope of the network is limited in practice since it is able to overhear the wireless channel only locally, and explicit notification about global network state would result in large overhead. Therefore, we seek to extend a node’s view into the network by means of machine learning techniques.
The goal of this thesis is to estimate global metrics of a mobile ad-hoc network (MANET) by means of locally overheard information in a network simulation environment.
Literature review: Identify network optimization techniques that rely on global network knowledge and extract their requirements.
Define metrics: Make a list of global network properties that should be classified or estimated.
Identification of features: Identify potential features that can be obtained by traffic monitoring. Features that comprise relevant information about distant nodes might for instance be obtained by inspecting packet headers of the higher layers (e.g., network layer and transport layer).
Feature engineering and machine learning: Select and engineer features that can be obtained by overhearing the wireless channel.
Implementation: Run experiments with the ns-3 network simulator and evaluate the estimator’s performance.
Collide, Collate, Collect: Recognizing Senders in Wireless Collisions
With wireless mobile IEEE 802.11a/g networks, collisions are currently inevitable despite effective counter measures. This work proposes an approach to detect the MAC addresses of transmitting stations in case of a collision, and measures its practical feasibility. Recognizing senders using cross-correlation in the time domain worked surprisingly well in simulations using Additive White Gaussian Noise (AWGN) and standard Matlab channel models.
Real-world experiments using software-defined radios also showed promising results in spite of decreased accuracy due to channel effects. During the experiments, various Modulation and Coding Schemes (MCSs) and scrambler initialization values were compared. Knowledge about which senders were transmitting leading up to a collision could help develop new improvements to the 802.11 MAC coordination function, or serve as a feature for learning-based algorithms.
Collisions on wireless networks most likely lead to packet losses. Current network protocols typically recover from these situations by retransmissions. In doing so, the overall network capacity is reduced and the network delay increases with the amount and duration of collisions. However, collided frames may still reveal valuable information that might be suitable for advanced protocol designs.
Detect frame alignments of collided frames at the PHY.
Devise techniques to detect known data, such as MAC header fields.
Analyze real network scenarios with respect to collisions, classify observed events (e.g., pairs of hidden terminals) and generate statistics.
Location Privacy of Digital Trunked Radio