Spatial reuse in wireless LAN networks

International audienceThe absence of a radio carrier reuse pattern in wireless LAN systems necessiti- es to apply a collision avoidance mechanism in order to manage all collisions that can produce on the medium sharing. Previous studies concerning cellular radio networks are in general focused on the basis of frequency cell partition- . We re-investigate this frequency reuse topic for wireless LANs where all nodes use the same channel and are randomly placed. Several measurements are presented to compute all radio parameters in a local radio network. Then, theoretical study and simulation results are introduced to deduce the distance that permits a complete carrier reuse by using a carrier sense mechanism

Efficient Vertical Handoffs in Wireless Overlay Networks

Mobile IP is used to keep track of location information and make the data available to the mobile device anytime, anywhere. It is designed to address the macro-mobility management, it does not address micro-level mobility issues such as handoff latency and packet loss. In this paper, we propose a mobility management scheme to handle the movements of mobile nodes among different wireless network technologies. Our scheme combines: (a) A hierarchical mobility management architecture to hide mobility of mobile nodes within the foreign domain from the home agent; (b) A handoff protocol to reduce packet loss during the transition from one cell to another; (c) The use of our proposed virtual cells in order to reduce the upward vertical handoff latency and disruption as much as possible. Our design is based on the Internet Protocol (IP) and is compatible with the Mobile IP standard (MIP). We also present simulation results showing that our handoff scheme is very fast to meet the requirements of an interactive communication session such as Internet telephony and avoiding packet loss

On Optimality of Myopic Sensing Policy with Imperfect Sensing in Multi-channel Opportunistic Access

We consider the channel access problem under imperfect sensing of channel state in a multi-channel opportunistic communication system, where the state of each channel evolves as an independent and identically distributed Markov process. The considered problem can be cast into a restless multi-armed bandit (RMAB) problem that is of fundamental importance in decision theory. It is well-known that solving the RMAB problem is PSPACE-hard, with the optimal policy usually intractable due to the exponential computation complexity. A natural alternative is to consider the easily implementable myopic policy that maximizes the immediate reward but ignores the impact of the current strategy on the future reward. In this paper, we perform an analytical study on the optimality of the myopic policy under imperfect sensing for the considered RMAB problem. Specifically, for a family of generic and practically important utility functions, we establish the closed-form conditions under which the myopic policy is guaranteed to be optimal even under imperfect sensing. Despite our focus on the opportunistic channel access, the obtained results are generic in nature and are widely applicable in a wide range of engineering domains.Comment: 21 pages regular pape

On Optimality of Myopic Policy for Restless Multi-armed Bandit Problem with Non i.i.d. Arms and Imperfect Detection

We consider the channel access problem in a multi-channel opportunistic communication system with imperfect channel sensing, where the state of each channel evolves as a non independent and identically distributed Markov process. This problem can be cast into a restless multi-armed bandit (RMAB) problem that is intractable for its exponential computation complexity. A natural alternative is to consider the easily implementable myopic policy that maximizes the immediate reward but ignores the impact of the current strategy on the future reward. In particular, we develop three axioms characterizing a family of generic and practically important functions termed as $g$-regular functions which includes a wide spectrum of utility functions in engineering. By pursuing a mathematical analysis based on the axioms, we establish a set of closed-form structural conditions for the optimality of myopic policy.Comment: Second version, 16 page

Analysis of a Priority Stack Random Access Protocol In W-CDMA Systems

The stack protocol (called also tree protocol) can be used in order to introduce a priority mechanism on the random access stage in W-CDMA. Indeed, after second generation networks supporting voice service only, the third generation systems (UMTS) should offer more services with quality and priority. However, all priorities in the UMTS system are based on the dedicated channel and after the random access mechanism that use the weak access protocol: slotted aloha. In this paper, we analyze the possibility to apply the tree random access protocol for the W-CDMA part in the UTRA radio interface proposition. We study also a priority system applied on the random access directly. The analytical model use generating functions and an algebraic method in order to show the stack protocol performance. Also, numerical and simulation results are presented and show the predominance of this protocol compared with the slotted aloha mechanism

A Survey on Malware Detection with Graph Representation Learning

Malware detection has become a major concern due to the increasing number and complexity of malware. Traditional detection methods based on signatures and heuristics are used for malware detection, but unfortunately, they suffer from poor generalization to unknown attacks and can be easily circumvented using obfuscation techniques. In recent years, Machine Learning (ML) and notably Deep Learning (DL) achieved impressive results in malware detection by learning useful representations from data and have become a solution preferred over traditional methods. More recently, the application of such techniques on graph-structured data has achieved state-of-the-art performance in various domains and demonstrates promising results in learning more robust representations from malware. Yet, no literature review focusing on graph-based deep learning for malware detection exists. In this survey, we provide an in-depth literature review to summarize and unify existing works under the common approaches and architectures. We notably demonstrate that Graph Neural Networks (GNNs) reach competitive results in learning robust embeddings from malware represented as expressive graph structures, leading to an efficient detection by downstream classifiers. This paper also reviews adversarial attacks that are utilized to fool graph-based detection methods. Challenges and future research directions are discussed at the end of the paper.Comment: Preprint, submitted to ACM Computing Surveys on March 2023. For any suggestions or improvements, please contact me directly by e-mai

Quality of service aspect for BRAIN architecture

Session 4.1International audienceIn this paper, we present different aspects of Quality of Service that should be adapted to the BRAIN architecture. Several parameters and policies of QoS are depicted. Also, the paper shows the dynamic adaptation of these parameters in the context of BRAIN

Evaluation of Multicasting Schemes based on Joint Multiple Description and Network Coding

International audienceThis paper considers a multicast scenario and compares the average reception quality obtained when combining multiple description coding (MDC) and network coding (NC). Plain (single description) network coding (NC-SDC) serves as reference. In the considered scenario, a single source is multicast to several receivers with various channel conditions. Contrary to a NC-SDC scheme, unable to recover the coded packets when not enough combinations of packets have been received, NC of MDC packets allows a more progressive quality improvement with the number of received packets, and a reduction of the effect of the quantization noise when MDC is performed via frame expansion before quantization. Considering a probability distribution for the bit transition probability during transmission to any user in the multicast group, the expected signal-to-noise ratio is evaluated. Performance comparisons are made for various error distributions, field sizes, and MDC methods (via frame expansion and correlating transform)

On Delay Fairness for Multiple Network Coding Transmissions

This paper studies the unfairness issues of network coding in multi hop wireless networks. Most of the work on network coding focuses on the obtained throughput gain. They show that mixing lineally the packets at the intermediate nodes is capacity-achieving. However, network coding schemes designed only to maximize the throughput could be unfairly biased. The reason is that by mixing different flows, packets destined to one destination in order to be decoded need to wait for the reception of the whole mixed set of encoded packets that may be totally independent in terms of final destination. This may lead to highly unfair delay for small block data. To mitigate this unfairness, relay nodes may mix only packets going to the same destination. We call this strategy FairMix. Although FairMix may limit the maximum attainable throughput, it aims to make distinct for decoding delay of each destination corresponding to the size of the data block. In order to investigate this trade off, we compare the FairMix performance with a naive network coding which mixes packets destined to different destinations. The simulation under lossy wireless links, limited memory and bandwidth resources, and different block sizes shows that FairMix is effective in improving fairness among destinations in comparison to naive network coding