This paper considers from the perspective of MAC layer, a throughput maximization problem of NOMA-ALOHA, ALOHA implemented over NOMA (non-orthogonal multiple access), that can decode simultaneously transmitted multiple packets via successive interference cancellation (SIC) if each of the multiple packets has a different power level from those of others. We start with a throughput optimization problem for two power levels and extend it to a problem for a general number of power levels. For the case of two power levels, we find upper and lower bounds of the maximum throughput and suggest a practical sub-optimal transmission probability whose throughput is close to the maximum. The maximum throughput of NOMA-ALOHA is at least 1.774 times higher than that of traditional slotted ALOHA in our numerical example. We extend the optimization problem into a general one with arbitrary number of power levels. We numerically analyze the performance through a relaxed problem of the original one with high complexity. Our numerical analysis shows that the maximum throughput of NOMA-ALOHA with three power levels is 2.33 times higher than simple ALOHA’s maximum throughput and that adding one power level to NOMA-ALOHA with two power levels enhances throughput at least by more than 28 percent.

A variety of models have been proposed and analyzed to understand how a new innovation (e.g., a technology, a product, or even a behavior) diffuses over a social network, broadly classified into either of epidemic-based or game-based ones. In this paper, we consider a game-based model, where each individual makes a selfish, rational choice in terms of its payoff in adopting the new innovation, but with some noise. We address the following two questions on the diffusion speed of a new innovation under the game-based model: (1) what is a good subset of individuals to seed for reducing the diffusion time significantly, i.e., convincing them to preadopt a new innovation and (2) how much diffusion time can be reduced by such a good seeding. For (1), we design near-optimal polynomial-time seeding algorithms for three representative classes of social network models, Erdös-Rényi, planted partition and geometrically structured graphs, and provide their performance guarantees in terms of approximation and complexity. For (2), we asymptotically quantify the diffusion time for these graph topologies; further derive the seed budget threshold above which the diffusion time is dramatically reduced, i.e., phase transition of diffusion time. Furthermore, based on our theoretical findings, we propose a practical seeding algorithm, called Practical Partitioning and Seeding (PrPaS) and demonstrate that PrPaS outperforms other baseline algorithms in terms of the diffusion speed over a real social network topology. We believe that our results provide new insights on how to seed over a social network depending on its connectivity structure, where individuals rationally adopt a new innovation.

Both users and mobile network providers increasingly suffer from explosive growth of mobile traffic. We study so-called delayed Wi-Fi offloading that has been recently proposed as a low-cost solution of alleviating mobile data explosion. Delayed Wi-Fi offloading is a technology that offloads traffic from cellular to Wi-Fi by persuading users into delaying their delay-tolerant traffic and thus enlarging users' chance to meet Wi-Fi. In this paper, we study the economic effects of such user-oriented delayed Wi-Fi offloading in two market models: 1) monopoly and 2) duopoly with one multiservice provider. First, in the monopoly market with a single provider, we model a two-stage game, where the provider selects an Wi-Fi usage price for which users are the price-takers. Second, in the duopoly market with one multiservice provider, we consider a situation that both providers, say A and B, offer cellular service and only A launches a delayed Wi-Fi offloading service as a separate service from the original cellular service, thus allowing the users in B to subscribe to the offloading service from A, but with some incurring dual subscription cost. In those two markets, we study how the Wi-Fi usage price at an equilibrium changes depending on other system parameters such as cellular cost, Wi-Fi density, and the number of subscribers by analytically computing the Nash equilibria and conducting extensive numerical computations under various parameter changes. Our results give us useful insights into how economically viable user-oriented delayed Wi-Fi offloading is.

This paper considers a hybrid peer-to-peer (p2p) system, a dynamic distributed caching system with an authoritative server dispensing contents only if the contents fail to be found by searching an unstructured p2p system. We study the case when some peers may not be fully cooperative in the search process and examine the impact of various noncooperative behaviors in the aspect of scalability, more specifically average server load and average peer load as the peer population size increases. We categorize selfish peers into three classes: impatient peers that directly query the server without searching the p2p system, non-forwarders that refuse to forward query requests, and non-resolvers that refuse to share contents. It is shown that in the hybrid p2p system, impatient and/or non-forwarding behaviors prevent the system from scaling well because of the high server load, while the system scales well under the non-resolving selfish peers. Our study implies that the hybrid p2p system does not mandate an incentive mechanism for content sharing, which is in stark contrast to unstructured p2p systems, where incentivizing peers to share contents is known to be a key factor for the system's scalability.

A variety of models have been proposed and analyzed to understand how a new innovation (e.g., a technology, a product, or even a behavior) diffuses over a social network, broadly classified into either of epidemic-based or game-based ones. In this paper, we consider a game-based model, where each individual makes a selfish, rational choice in terms of its payoff in adopting the new innovation, but with some noise. We study how diffusion effect can be maximized by seeding a subset of individuals (within a given budget), i.e., convincing them to pre-adopt a new innovation. In particular, we aim at finding `good' seeds for minimizing the time to infect all others, i.e., diffusion speed maximization. To this end, we design polynomial-time approximation algorithms for three representative classes, Erdőos-Réenyi, planted partition and geometrically structured graph models, which correspond to globally well-connected, locally well-connected with large clusters and locally well-connected with small clusters, respectively, provide their performance guarantee in terms of approximation and complexity. First, for the dense Erdős-Rényi and planted partition graphs, we show that an arbitrary seeding and a simple seeding proportional to the size of clusters are almost optimal with high probability. Second, for geometrically structured sparse graphs, including planar and d-dimensional graphs, our algorithm that (a) constructs clusters, (b) seeds the border individuals among clusters, and (c) greedily seeds inside each cluster always outputs an almost optimal solution. We validate our theoretical findings with extensive simulations under a real social graph. We believe that our results provide new practical insights on how to seed over a social network depending on its connection structure, where individuals rationally adopt a new innovation. To our best knowledge, we are the first to study such diffusion speed maximization on the game-based diffusion, while the extensive research efforts have been made in epidemic-based models, often referred to as influence maximization.

Both users and mobile network providers increasingly suffer from explosive growth of mobile traffic. We study socalled delayed Wi-Fi offloading that has been recently proposed as a low-cost solution of alleviating mobile data explosion. Delayed Wi-Fi offloading is a technology that offloads traffic from cellular to Wi-Fi by persuading users into delaying their delay-tolerant traffic and thus actively exploiting users' chance to meet Wi-Fi. In this paper, we study the economic effects of such user-oriented delayed Wi-Fi offloading in the monopoly market as well as the market with two providers. In the monopoly market, we model a two-stage game, where the provider selects an offloading price for which users are the price-takers. In the market with two providers, we consider a situation that either of providers, say A, launches a delayed Wi-Fi offloading service as a separate service from the original cellular service, thus allowing the users in another provider, say B, to subscribe to the offloading service from A, but with some incurring switching cost. In both markets, we study how the equilibrium offloading price changes depending on other system parameters, e.g., cellular cost, Wi-Fi density, and the number of subscribers, by computing the Nash equilibriums and providing extensive numerical results for various parameter changes, which gives us useful insights into how economically viable user-oriented delayed Wi-Fi offloading is.

Cellular networks are facing severe traffic overloads due to the proliferation of smart handheld devices and traffic-hungry applications. A cost-effective and practical solution is to offload cellular data through WiFi. Recent theoretical and experimental studies show that a scheme, referred to as delayed WiFi offloading, can significantly save the cellular capacity by delaying users' data and exploiting mobility and thus increasing chance of meeting WiFi APs (Access Points). Despite a huge potential of WiFi offloading in alleviating mobile data explosion, its success largely depends on the economic incentives provided to users and operators to deploy and use delayed offloading. In this paper, we study how much economic benefits can be generated due to delayed WiFi offloading, by modeling the interaction between a single provider and users based on a two-stage sequential game. We first analytically prove that WiFi offloading is economically beneficial for both the provider and users. Also, we conduct trace-driven numerical analysis to quantify the practical gain, where the increase ranges from 21% to 152% in the providers revenue, and from 73% to 319% in the users surplus.

This paper studies how global information affects the diffusion of innovations on a network. The diffusion of innovation is modeled by the logit dynamics of a weighted N-person coordination game among (bounded) rational users where innovations spread through users' strategic choices. We find a critical asymptotic threshold for the weight on global information where the diffusion of innovations undergoes a transition in the rate of convergence regardless of any network structure. In particular, it is found that the convergence to the pervasive adoption is slowed down by global information.

This paper considers a hybrid peer-to-peer (p2p) system, a dynamic distributed caching system with an authoritative server dispensing contents only if the contents fail to be found by searching an unstructured peer-to-peer (p2p) system. We study the case when some peers may not be fully cooperative in the search process and examine the impact of various noncooperative behaviors on the querying load on the server as the peer population size increases. We categorize selfish peers into three classes: impatient peers that directly query the server without searching the p2p system, non-forwarders that refuse to forward query requests, and non-resolvers that refuse to share contents. It is shown that in the hybrid p2p system, impatient and/or nonforwarding behaviors prevent the system from scaling well because of the high server load, while the system scales well under the non-resolving selfish peers. Our study implies that the hybrid p2p system does not mandate an incentive mechanism for content sharing, which is in stark contrast to unstructured p2p systems, where incentivizing peers to share contents is known to be a key factor for the system's scalability.

Cellular networks are facing severe traffic overloads due to the proliferation of smart handheld devices and traffichungry applications. A cost-effective and practical solution is to offload cellular data through WiFi. Recent theoretical and experimental studies show that a scheme, referred to as delayed WiFi offloading, can significantly save the cellular capacity by delaying users' data and exploiting mobility and thus increasing chance of meeting WiFi APs (Access Points). Despite a huge potential of WiFi offloading in alleviating mobile data explosion, its success largely depends on the economic incentives provided to users and network providers to deploy and use delayed offloading. In this paper, we study how much economic benefits can be generated due to delayed WiFi offloading, by modeling the interaction between a single provider and users based on a two-stage sequential game. We first analytically prove that WiFi offloading is economically beneficial for both the provider and users. Also, we conduct trace-driven numerical analysis to quantify the practical gain, where the increase ranges from 21 to 152% in the provider's revenue, and from 73 to 319% in the users' surplus.

Recently, economic models have been proposed to study adoption dynamics of entrant and incumbent technologies motivated by the need for new network architectures to complement the current Internet. We propose new models of adoption dynamics of entrant and incumbent technologies among bounded rational users who choose a satisfying strategy rather than an optimal strategy based on aspiration-based learning. Two models of adoption dynamics are proposed according to the characteristics of aspiration level. The impacts of switching cost, the benefit from entrant and incumbent technologies, and the initial aspiration level on the adoption dynamics are investigated.

We consider a game theoretic model incorporating channel state information into slotted ALOHA in a fading environment. Each user sets a threshold for her channel gain and sends a packet only when the channel gain is higher than the threshold at a given slot. This threshold is decided to maximize the net benefit of a user, utility minus power consumption. The asymptotic behaviors of the total throughput at a symmetric Nash equilibrium point are studied for fading and non-fading environments in a homogeneous system. It is shown that the total throughput in a fading environment increases as the number of users increases, while the total throughput in the simple classical slotted ALOHA decreases when users are sensitive enough to power consumption. Convergence to the symmetric Nash equilibrium is also studied.

New network technologies constantly seek to displace incumbents. Their success depends on technological superiority, the size of the incumbent's installed base, users' adoption behaviors, and various other factors. The goal of this paper is to develop an understanding of competition between network technologies and identify the extent to which different factors, in particular converters (a.k.a. gateways), affect the outcome. Converters can help entrants overcome the influence of the incumbent's installed base by enabling cross-technology interoperability. However, they have development, deployment, and operations costs and can introduce performance degradations and functionality limitations, so that if, when, why, and how they help is often unclear. To this end, the paper proposes and solves a model for adoption of competing network technologies by individual users. The model incorporates a simple utility function that captures key aspects of users' adoption decisions. Its solution reveals a number of interesting and at times unexpected behaviors, including the possibility for converters to reduce overall market penetration of the technologies and to prevent convergence to a stable state, something that never arises in their absence. The findings were tested for robustness, e.g., different utility functions and adoption models, and found to remain valid across a broad range of scenarios.

In this paper, we study non-cooperative user behavior in random-access wireless networks in which users have freedom to choose their back-off contention window size according to network's congestion status. We formulate a non- cooperative game and show the existence and uniqueness of its equilibrium point. We also propose an iterative method leading to the equilibrium point of the game. A discussion of alternative game formulations in the same problem context is also given.

Bit Torrent is a popular peer-to-peer file-sharing network that employs transaction-level incentives, i.e., typically two files are swapped for each transaction between peers. Also, larger files are segmented into "chunks" that are the subject of individual transactions. In this paper, we give a simple deterministic stratified epidemiological models of the dissemination of a single popular file in peer-to-peer file-sharing networks that employ BitTorrent-like incentives. We then use this simple model to evaluate the effect of these incentives by comparison with a system does not segment files and always involves only a single file transfer per transaction, i.e., involves a client-peer and server- peer.

This paper considers the problem of finding a tamper-resistant and charge-sensitive end-to-end window flow-control mechanism for greedy users. Using a mathematical model of resource distribution, we propose a distributed window flow-control mechanism leading to a flow-rate vector which achieves maximum total utility. Desirable features of the proposed window control algorithm and properties of the equilibrium points are explored. We also prove the convergence of the proposed window control algorithm.

In this paper, we study end-user dynamics of communication networks employing usage-based pricing. We propose a generic network access mechanism in which users modify their access control parameter based on the quality of service they receive in order to maximize their net benefit. For the examples of users sharing access to a bandwidth resource via a single trunk with Erlang loss dynamics and for a differentiated services (diffserv) network, we study the equilibrium/fixed points and give analytical results on convergence assuming the network prices are fixed.

A generic mechanism for end-user transmission rate control into a differentiated services Internet is formulated and basic results of corresponding Nash equilibria are proved. We consider specific examples of the mechanism including additive increase and multiplicative decrease inspired by present day TCP congestion control. For the example of users sharing access to a bandwidth resource via resizable provisioned label-switched paths (MPLS), we study the equilibria and the performance of the generic mechanism and give analytical results on convergence to equilibria. The fairness of the resulting equilibria when user demands exceed available network resources is also studied.