Basics of coalitional games with applications to communications and networking

Game theory is the study of decision making in an interactive environment. Coalitional games fulfill the promise of group efficient solutions to problems involving strategic actions. Formulation of optimal player behavior is a fundamental element in this theory. This paper comprises a self-instructive didactic means to study basics of coalitional games indicating how coalitional game theory tools can provide a framework to tackle different problems in communications and networking. We show that coalitional game approaches achieve an improved performance compare to non-cooperative game theoretical solutions

Predicting Performance of Channel Assignments in Wireless Mesh Networks through Statistical Interference Estimation

Wireless Mesh Network (WMN) deployments are poised to reduce the reliance on wired infrastructure especially with the advent of the multi-radio multi-channel (MRMC) WMN architecture. But the benefits that MRMC WMNs offer viz., augmented network capacity, uninterrupted connectivity and reduced latency, are depreciated by the detrimental effect of prevalent interference. Interference mitigation is thus a prime objective in WMN deployments. It is often accomplished through prudent channel allocation (CA) schemes which minimize the adverse impact of interference and enhance the network performance. However, a multitude of CA schemes have been proposed in research literature and absence of a CA performance prediction metric, which could aid in the selection of an efficient CA scheme for a given WMN, is often felt. In this work, we offer a fresh characterization of the interference endemic in wireless networks. We then propose a reliable CA performance prediction metric, which employs a statistical interference estimation approach. We carry out a rigorous quantitative assessment of the proposed metric by validating its CA performance predictions with experimental results, recorded from extensive simulations run on an ns-3 802.11g environment

Reliable Prediction of Channel Assignment Performance in Wireless Mesh Networks

The advancements in wireless mesh networks (WMN), and the surge in multi-radio multi-channel (MRMC) WMN deployments have spawned a multitude of network performance issues. These issues are intricately linked to the adverse impact of endemic interference. Thus, interference mitigation is a primary design objective in WMNs. Interference alleviation is often effected through efficient channel allocation (CA) schemes which fully utilize the potential of MRMC environment and also restrain the detrimental impact of interference. However, numerous CA schemes have been proposed in research literature and there is a lack of CA performance prediction techniques which could assist in choosing a suitable CA for a given WMN. In this work, we propose a reliable interference estimation and CA performance prediction approach. We demonstrate its efficacy by substantiating the CA performance predictions for a given WMN with experimental data obtained through rigorous simulations on an ns-3 802.11g environment.Comment: Accepted in ICACCI-201

Resource allocation in networks via coalitional games

The main goal of this dissertation is to manage resource allocation in network engineering problems and to introduce efficient cooperative algorithms to obtain high performance, ensuring fairness and stability. Specifically, this dissertation introduces new approaches for resource allocation in Orthogonal Frequency Division Multiple Access (OFDMA) wireless networks and in smart power grids by casting the problems to the coalitional game framework and by providing a constructive iterative algorithm based on dynamic learning theory.  Software Engineering (Software)Algorithms and the Foundations of Software technolog

Enabling Efficient, Robust, and Scalable Wireless Multi-Hop Networks: A Cross-Layer Approach Exploiting Cooperative Diversity

The practical performance in terms of throughput, robustness, and scalability of traditional Wireless Multihop Networks (WMNs) is limited. The key problem is that such networks do not allow for advanced physical layers, which typically require (a) spatial diversity via multiple antennas, (b) timely Channel State Information (CSI) feedback, and (c) a central instance that coordinates nodes. We propose Corridor-based Routing to address these issues. Our approach widens traditional hop-by-hop paths to span multiple nodes at each hop, and thus provide spatial diversity. As a result, at each hop, a group of transmitters cooperates at the physical layer to forward data to a group of receivers. We call two subsequent groups of nodes a stage. Since all nodes participating in data forwarding at a certain hop are part of the same fully connected stage, corridors only require one-hop CSI feedback. Further, each stage operates independently. Thus, Corridor-based Routing does not require a network-wide central instance, and is scalable. We design a protocol that builds end-to-end corridors. As expected, this incurs more overhead than finding a traditional WMN path. However, if the resulting corridor provides throughput gains, the overhead compensates after a certain number of transmitted packets. We adapt two physical layers to the aforementioned stage topology, namely, Orthogonal Frequency-Division Multiple Access (OFDMA), and Interference Alignment (IA). In OFDMA, we allocate each subchannel to a link of the current stage which provides good channel conditions. As a result, we avoid deep fades, which enables OFDMA to transmit data robustly in scenarios in which traditional schemes cannot operate. Moreover, it achieves higher throughputs than such schemes. To minimize the transmission time at each stage, we present an allocation mechanism that takes into account both the CSI, and the amount of data that each transmitter needs to transmit. Further, we address practical issues and implement our scheme on software-defined radios. We achieve roughly 30% average throughput gain compared to a WMN not using corridors. We analyze OFDMA in theory, simulation, and practice. Our results match in all three domains. Further, we design a physical layer for corridor stages based on IA in the frequency domain. Our practical experiments show that IA often performs poorly because the decoding process augments noise. We find that the augmentation factor depends only on the channel coefficients of the subchannels that IA uses. We design a mechanism to determine which transmitters should transmit to which receivers on which subchannels to minimize noise. Since the number of possible combinations is very large, we use heuristics that reduce the search space significantly. Based on this design, we present the first practical frequency IA system. Our results show that our approach avoids noise augmentation efficiently, and thus operates robustly. We observe that IA is most suitable for stages with specific CSI and traffic conditions. In such scenarios, the throughput gain compared to a WMN not using corridors is 25% on average, and 150% in the best case. Finally, we design a decision engine which estimates the performance of both OFDMA and IA for a given stage, and chooses the one which achieves the highest throughput. We evaluate corridors with up to five stages, and achieve roughly 20% average throughput gain. We conclude that switching among physical layers to adapt to the particular CSI and traffic conditions of each stage is crucial for efficient and robust operation

Statistical Relationship between Interference Estimates and Network Capacity

Interference is a major impediment to the performance of a wireless network as it has a significant adverse impact on Network Capacity. There has been a gradual and consistent densification of WiFi networks due to Overlapping Basic Service Set (OBSS) deployments. With the upcoming 802.11ax standards, dense and ultra-dense deployments will become the norm and the detrimental impact of Interference on Capacity will only exacerbate. However, the precise nature of the association between Interference and Network Capacity remains to be investigated, a gap we bridge in this work. We employ linear and polynomial regression to find answers to several unexplored questions concerning the Capacity Interference Relationship (CIR). We devise an algorithm to select regression models that best explain this relationship by considering a variety of factors including outlier threshold. We ascertain the statistical significance of their association, and also determine the explainability of variation in Network Capacity when Interference is varied, and vice versa. While the relationship is generally believed to be non-linear, we demonstrate that scenarios exist where a strong linear correlation exists between the two. We also investigate the impact of WMN topology on this relationship by considering four carefully designed Wireless Mesh Network (WMN) topologies in the experiments. To quantify endemic Interference, we consider four popular Theoretical Interference Estimation Metrics (TIEMs) viz., TID, CDALcost, CXLSwt, and CALM. To ensure a sound regression analysis, we consider a large set of 100 Channel Assignment (CA) schemes, a majority of which are generated through a Generic Interference aware CA Generator proposed in this work. Finally, we test the TIEMs in terms of their reliability and the ability to model Interference. We carry out the experiments on IEEE 802.11g/n WMNs simulated in ns-3

A Socio-inspired CALM Approach to Channel Assignment Performance Prediction and WMN Capacity Estimation

A significant amount of research literature is dedicated to interference mitigation in Wireless Mesh Networks (WMNs), with a special emphasis on designing channel allocation (CA) schemes which alleviate the impact of interference on WMN performance. But having countless CA schemes at one's disposal makes the task of choosing a suitable CA for a given WMN extremely tedious and time consuming. In this work, we propose a new interference estimation and CA performance prediction algorithm called CALM, which is inspired by social theory. We borrow the sociological idea of a "sui generis" social reality, and apply it to WMNs with significant success. To achieve this, we devise a novel Sociological Idea Borrowing Mechanism that facilitates easy operationalization of sociological concepts in other domains. Further, we formulate a heuristic Mixed Integer Programming (MIP) model called NETCAP which makes use of link quality estimates generated by CALM to offer a reliable framework for network capacity prediction. We demonstrate the efficacy of CALM by evaluating its theoretical estimates against experimental data obtained through exhaustive simulations on ns-3 802.11g environment, for a comprehensive CA test-set of forty CA schemes. We compare CALM with three existing interference estimation metrics, and demonstrate that it is consistently more reliable. CALM boasts of accuracy of over 90% in performance testing, and in stress testing too it achieves an accuracy of 88%, while the accuracy of other metrics drops to under 75%. It reduces errors in CA performance prediction by as much as 75% when compared to other metrics. Finally, we validate the expected network capacity estimates generated by NETCAP, and show that they are quite accurate, deviating by as low as 6.4% on an average when compared to experimentally recorded results in performance testing

Interference mitigation in wireless mesh networks through radio co-location aware conflict graphs

Wireless Mesh Networks (WMNs) have evolved into a wireless communication technology of immense interest. But technological advancements in WMNs have inadvertently spawned a plethora of network performance bottlenecks, caused primarily by the rise in prevalent interference. Conflict Graphs are indispensable tools used to theoretically represent and estimate the interference in wireless networks. We propose a generic algorithm to generate conflict graphs which is independent of the underlying interference model. Further, we propose the notion of radio co-location interference, which is caused and experienced by spatially co-located radios in multi-radio multi-channel WMNs. We experimentally validate the concept, and propose a new all-encompassing algorithm to create a radio co-location aware conflict graph. Our novel conflict graph generation algorithm is demonstrated to be significantly superior and more efficient than the conventional approach, through theoretical interference estimates and comprehensive experiments. The results of an extensive set of ns-3 simulations run on the IEEE 802.11g platform strongly indicate that the radio co-location aware conflict graphs are a marked improvement over their conventional counterparts. We also question the use of total interference degree as a reliable metric to predict the performance of a Channel Assignment scheme in a given WMN deployment