This paper examines the problem of distributed path planning for a mobile sensor network comprised of communication-aware robots performing general information gathering missions. Mutual information is derived for distributed sensing over packet erasure channels that model multi-hop communication. We model distributed path planning as a non-cooperative game and derive utility functions that are optimized locally by each robot. Each robot computes the control input in a distributed manner that results in a combined action that can be bounded by the optimal centralized result by utilizing sub-modularity in certain cases. It is shown that when the communication model includes multi-hop communication to expand the coverage of the sensor network, the property of sub-modularity is lost. We further show that the additional global knowledge required for the local computation of utility functions can be learned by simple consensus approaches. Finally, we discuss a sampling approach to approximate the proposed utility functions in order to reduce the associated computational requirements.

We model an Internet marketplace using a set of servers that choose prices for performing jobs. Each server has a queue of unfinished jobs, and is penalized for delay by the market maker via a holding cost. A server completes jobs with a low or high "quality", and jobs truthfully report the quality with which they were completed. The best estimate of quality based on these reports is the "reputation" of the server. A server bases its pricing decision on the distribution of its competitors offered prices and reputations. An entering job is given a random sample of servers, and chooses the best one based on a linear combination of price and reputation. We seek to understand how prices would be determined in such a marketplace using the theory of Mean Field Games. We show the existence of a Mean Field Equilibrium and show how reputation plays a role in allowing servers to declare larger prices than their competitors. We illustrate our results by a numerical study of the system via simulation with parameters chosen from data gathered from existing Internet marketplaces.

There has been much work done on designing cellular scheduling algorithms. These algorithms are set up in the manner of a direct mechanism in which the queues reveal their states (backlog), and the scheduler chooses an allocation. In policies such as longest queue first (LQF), scheduling can be shown to yield short queue lengths. However, these algorithms are reliant on truthful declarations of state. Our goal in this article is to determine if a Vickrey–Clarke–Groves (VCG)-type mechanism (a second price auction) that elicits truthful value will also possess the desired LQF-like behavior in a system of dynamically evolving queues. Our approach is to use the concept of a mean field game under which at each time instant, a queue chooses its bid as a best response to its belief that the bids of the others will be drawn independently from a common bid distribution. If this best response is itself a sample from the belief bid distribution, a mean field equilibrium (MFE) is said to exist. We show the existence of the MFE and find it using its structure that the results of the allocation policy are the same as LQF. Thus, the desired LQF-like behavior arises naturally under the second-price auction mechanism. We also present results on the accuracy of the model as the number of agents (queues) becomes large. Finally, using simulations with a large number of queues, we show that computation of the MFE is straightforward.

A large number of congestion control protocols have been proposed in the last few years, with all having the same purpose—to divide available bandwidth resources among different flows in a fair manner. Each protocol operates on the paradigm of some conception of link price (such as packet losses or packet delays) that determines source transmission rates. Recent work on network utility maximization has brought forth the idea that the fundamental price or Lagrange multiplier for a link is proportional to the queue length at that link, and that different congestion metrics (such as delays or drops) are essentially ways of interpreting such a Lagrange multiplier. We thus ask the following question: Suppose that each flow has a number of congestion control protocols to choose from, which one (or combination) should it choose? We introduce a framework wherein each flow has a utility that depends on throughput and also has a disutility that is some function of the queue lengths encountered along the route taken. Flows must choose a combination of protocols that would maximize their payoffs. We study both the socially optimal, as well as the selfish cases to determine the loss of system-wide value incurred through selfish decision making, so characterizing the “price of heterogeneity.” We also propose tolling schemes that incentivize flows to choose one of several different virtual networks catering to particular needs and show that the total system value is greater, hence making a case for the adoption of such virtual networks.

We consider wireless networks in which multiple paths are available between each source and destination. We allow each source to split traffic among all of its available paths, and we ask the question: How do we attain the lowest possible number of transmissions per unit time to support a given traffic matrix? Traffic bound in opposite directions over two wireless hops can utilize the “reverse carpooling” advantage of network coding in order to decrease the number of transmissions used. We call such coded hops “hyper-links.” With the reverse carpooling technique, longer paths might be cheaper than shorter ones. However, there is a peculiar situation among sources—the network coding advantage is realized only if there is traffic in both directions of a shared path. We consider the problem of routing with network coding by selfish agents (the sources) as a potential game and develop a method of state-space augmentation in which additional agents (the hyper-links) decouple sources' choices from each other by declaring a hyper-link capacity, allowing sources to split their traffic selfishly in a distributed fashion, and then changing the hyper-link capacity based on user actions. Furthermore, each hyper-link has a scheduling constraint in terms of the maximum number of transmissions allowed per unit time. We show that our two-level control scheme is stable and verify our analytical insights by simulation.

A large number of congestion control protocols have been proposed in the last few years, with all having the same purpose-to divide available bandwidth resources among different flows in a fair manner. Each protocol operates on the paradigm of some conception of link price (such as packet losses or packet delays) that determines source transmission rates. Recent work on network utility maximization has brought forth idea that the fundamental price or Lagrange multiplier for a link is proportional the queue length at that link, and that different congestion metrics (such as delays or drops) are essentially ways of interpreting such a Lagrange multiplier. We thus ask the following question: Suppose that each flow has a number of congestion control protocols to choose from, which one (or combination) should it choose? We introduce a framework wherein each flow has a utility that depends on throughput, and also has a disutility that is some function of the queue lengths encountered along the route taken. Flows must choose a combination of protocols that would maximize their payoffs. We study both the socially optimal, as well as the selfish cases to determine the loss of system-wide value incurred through selfish decision making, so characterizing the “price of heterogeneity”. We also propose tolling schemes that incentivize flows to choose one of several different virtual networks catering to particular needs, and show that the total system value is greater, hence making a case for the adoption of such virtual networks.

In the emerging broadband wireless networks such as IEEE 802.16m and LTE-A networks which employ universal frequency reuse-1, the cell coverage is predominantly limited by the co-channel interference. Bit level data repetition, and conventional multi-antenna maximal-ratio-combining (MRC) techniques are typically used to improve the signal-to-interference-plus- noise ratio (SINR) at the receiver. Simple data repetition does not guarantee efficient interference suppression and it reduces spectrum efficiency. In this paper, we propose a symbol level data repetition technique called conjugate data repetition (CDR), which transmits the modulation alphabet of the desired signal and its complex-conjugate in distinct sub carriers. The CDR operation is performed across all base stations in a synchronous manner. We show that minimum mean-square error (MMSE) filtering of the complex-valued signal and its conjugated copy, provides a high interference cancellation (IC) gain. For repetition factor greater than 2, we propose a combination of conjugate repetition and random phase rotation of the repeated symbols. Simulation results show that CDR with a repetition factor 2 or 3 can provide a significant advantage in coverage/reliability for cell edge users.