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Shi, Y., Sagduyu, Y. E., Erpek, T..  2020.  Reinforcement Learning for Dynamic Resource Optimization in 5G Radio Access Network Slicing. 2020 IEEE 25th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD). :1—6.
The paper presents a reinforcement learning solution to dynamic resource allocation for 5G radio access network slicing. Available communication resources (frequency-time blocks and transmit powers) and computational resources (processor usage) are allocated to stochastic arrivals of network slice requests. Each request arrives with priority (weight), throughput, computational resource, and latency (deadline) requirements, and if feasible, it is served with available communication and computational resources allocated over its requested duration. As each decision of resource allocation makes some of the resources temporarily unavailable for future, the myopic solution that can optimize only the current resource allocation becomes ineffective for network slicing. Therefore, a Q-learning solution is presented to maximize the network utility in terms of the total weight of granted network slicing requests over a time horizon subject to communication and computational constraints. Results show that reinforcement learning provides major improvements in the 5G network utility relative to myopic, random, and first come first served solutions. While reinforcement learning sustains scalable performance as the number of served users increases, it can also be effectively used to assign resources to network slices when 5G needs to share the spectrum with incumbent users that may dynamically occupy some of the frequency-time blocks.
Krishna, M. B., Rodrigues, J. J. P. C..  2017.  Two-Phase Incentive-Based Secure Key System for Data Management in Internet of Things. 2017 IEEE International Conference on Communications (ICC). :1–6.

Internet of Things (IoT) distributed secure data management system is characterized by authentication, privacy policies to preserve data integrity. Multi-phase security and privacy policies ensure confidentiality and trust between the users and service providers. In this regard, we present a novel Two-phase Incentive-based Secure Key (TISK) system for distributed data management in IoT. The proposed system classifies the IoT user nodes and assigns low-level, high-level security keys for data transactions. Low-level secure keys are generic light-weight keys used by the data collector nodes and data aggregator nodes for trusted transactions. TISK phase-I Generic Service Manager (GSM-C) module verifies the IoT devices based on self-trust incentive and server-trust incentive levels. High-level secure keys are dedicated special purpose keys utilized by data manager nodes and data expert nodes for authorized transactions. TISK phase-II Dedicated Service Manager (DSM-C) module verifies the certificates issued by GSM-C module. DSM-C module further issues high-level secure keys to data manager nodes and data expert nodes for specific purpose transactions. Simulation results indicate that the proposed TISK system reduces the key complexity and key cost to ensure distributed secure data management in IoT network.

Shafigh, Alireza Shams, Lorenzo, Beatriz, Glisic, Savo, Pérez-Romero, Jordi, DaSilva, Luiz A., MacKenzie, Allen B., Röning, Juha.  2016.  A Framework for Dynamic Network Architecture and Topology Optimization. IEEE/ACM Trans. Netw.. 24:717–730.

A new paradigm in wireless network access is presented and analyzed. In this concept, certain classes of wireless terminals can be turned temporarily into an access point (AP) anytime while connected to the Internet. This creates a dynamic network architecture (DNA) since the number and location of these APs vary in time. In this paper, we present a framework to optimize different aspects of this architecture. First, the dynamic AP association problem is addressed with the aim to optimize the network by choosing the most convenient APs to provide the quality-of-service (QoS) levels demanded by the users with the minimum cost. Then, an economic model is developed to compensate the users for serving as APs and, thus, augmenting the network resources. The users' security investment is also taken into account in the AP selection. A preclustering process of the DNA is proposed to keep the optimization process feasible in a high dense network. To dynamically reconfigure the optimum topology and adjust it to the traffic variations, a new specific encoding of genetic algorithm (GA) is presented. Numerical results show that GA can provide the optimum topology up to two orders of magnitude faster than exhaustive search for network clusters, and the improvement significantly increases with the cluster size.

Luo, Z., Gilimyanov, R., Zhuang, H., Zhang, J..  2015.  Network-Wide Optimization of Uplink Fractional Power Control in LTE Networks. 2015 IEEE 82nd Vehicular Technology Conference (VTC2015-Fall). :1–5.

Next generation cellular networks will provide users better experiences by densely deploying smaller cells, which results in more complicated interferences environment. In order to coordinate interference, power control for uplink is particularly challenging due to random locations of uplink transmitter and dense deployment. In this paper, we address the uplink fractional power control (FPC) optimization problem from network optimization perspective. The relations between FPC parameters and network KPIs (Key Performance Indicators) are investigated. Rather than considering any single KPI in conventional approaches, multi-KPI optimization problem is formulated and solved. By relaxing the discrete optimization problem to a continuous one, the gradients of multiple KPIs with respect to FPC parameters are derived. The gradient enables efficiently searching for optimized FPC parameters which is particularly desirable for dense deployment of large number of cells. Simulation results show that the proposed scheme greatly outperforms the traditional one, in terms of network mean load, call drop & block ratio, and convergence speed.

Quanyan Zhu, University of Illinois at Urbana-Champaign, Carol Fung, Raouf Boutaba, Tamer Başar, University of Illinois at Urbana-Champaign.  2012.  GUIDEX: A Game-Theoretic Incentive-Based Mechanism for Intrusion Detection Networks. IEEE Journal on Selected Areas in Communications. 30(11)

Traditional intrusion detection systems (IDSs) work in isolation and can be easily compromised by unknown threats. An intrusion detection network (IDN) is a collaborative IDS network intended to overcome this weakness by allowing IDS peers to share detection knowledge and experience, and hence improve the overall accuracy of intrusion assessment. In this work, we design an IDN system, called GUIDEX, using gametheoretic modeling and trust management for peers to collaborate truthfully and actively. We first describe the system architecture and its individual components, and then establish a gametheoretic framework for the resource management component of GUIDEX. We establish the existence and uniqueness of a Nash equilibrium under which peers can communicate in a reciprocal incentive compatible manner. Based on the duality of the problem, we develop an iterative algorithm that converges geometrically to the equilibrium. Our numerical experiments and discrete event simulation demonstrate the convergence to the Nash equilibrium and the security features of GUIDEX against free riders, dishonest insiders and DoS attacks