This paper considers the problem of lossy compression for the computation of a function of two correlated sources, both of which are observed at the encoder. Due to presence of observation costs, the encoder is allowed to observe only subsets of the samples from both sources, with a fraction of such sample pairs possibly overlapping. The rate-distortion function is characterized for memoryless sources, and then specialized to Gaussian and binary sources for selected functions and with quadratic and Hamming distortion metrics, respectively. The optimal measurement overlap fraction is shown to depend on the function to be computed by the decoder, on the source statistics, including the correlation, and on the link rate. Special cases are discussed in which the optimal overlap fraction is the maximum or minimum possible value given the sampling budget, illustrating non-trivial performance trade-offs in the design of the sampling strategy. Finally, the analysis is extended to the multi-hop set-up with jointly Gaussian sources, where each encoder can observe only one of the sources.

This work considers communication over Gaussian interference channels with processing energy cost, which explicitly takes into account the energy expended for processing when transmitters are on. In the presence of processing energy cost, transmitting all the time as in the conventional no-cost case is no longer optimal. For a two-user Gaussian interference channel with processing energy cost, assuming that the on-off states of transmitters are not utilized for signaling, several transmission schemes with varying complexities are proposed and their sum-rates are compared with an interference-free upper bound. Moreover, the very strong interference regime, under which interference does not incur any rate penalty, is identified and shown to be larger than the case of no processing energy cost for certain scenarios of interest. Also, extensions to a three-user cascade Gaussian Z interference channel with processing energy cost are provided, where scheduling of user transmissions based on the channel set-up is investigated.

Energy efficiency is a key requirement in the design of wireless sensor networks. While most theoretical studies only account for the energy requirements of communication, the sensing process, which includes measurements and compression, can also consume comparable energy. In this paper, the problem of sensing and communicating parallel sources is studied by accounting for the cost of both communication and sensing. In the first formulation of the problem, the sensor has a separate energy budget for sensing and a rate budget for communication, while, in the second, it has a single energy budget for both tasks. Furthermore, in the second problem, each source has its own associated channel. Assuming that sources with larger variances have lower sensing costs, the optimal allocation of sensing energy and rate that minimizes the overall distortion is derived for the first problem. Moreover, structural results on the solution of the second problem are derived under the assumption that the sources with larger variances are transmitted on channels with lower noise.

This paper considers the problem of lossy compression for the computation of a function of two correlated sources, both of which are observed at the encoder. Due to presence of observation costs, the encoder is allowed to observe only subsets of the samples from both sources, with a fraction of such sample pairs possibly overlapping. For both Gaussian and binary sources, the distortion-rate function, or rate-distortion function, is characterized for selected functions and with quadratic and Hamming distortion metrics, respectively. Based on these results, for both examples, the optimal measurement overlap fraction is shown to depend on the function to be computed by the decoder, on the source correlation and on the link rate. Special cases are discussed in which the optimal overlap fraction is the maximum or minimum possible value given the sampling budget, illustrating non-trivial performance trade-offs in the design of the sampling strategy.

Energy efficiency is a key requirement in the design of wireless sensor networks. While most theoretical studies only account for the energy requirements of communication, the sensing process, which includes measurements and compression, can also consume comparable energy. In this paper, the problem of sensing and communicating parallel sources is studied by accounting for the cost of both communication and sensing. In the first formulation of the problem, the sensor has a separate energy budget for sensing and a rate budget for communication, while, in the second, it has a single energy budget for both tasks. Furthermore, in the second problem, each source has its own associated channel. Assuming that sources with larger variances have lower sensing costs, the optimal allocation of sensing energy and rate that minimizes the overall distortion is derived for the first problem. Moreover, structural results on the solution of the second problem are derived under the assumption that the sources with larger variances are transmitted on channels with lower noise. Closed-form solutions are also obtained for the case where the energy budget is sufficiently large. For an arbitrary order on the variances and costs, the optimal solution to the first problem is also obtained numerically and compared with several suboptimal strategies.

In this work, a four-terminal complex Gaussian network composed of a source, a destination, an eavesdropper, and a jammer relay is studied under two different set of assumptions: 1) The jammer relay does not hear the source transmission, and 2) The jammer relay is causally given the source message. In both cases, the jammer relay assists the eavesdropper and aims to decrease the achievable secrecy rates. The source, on the other hand, aims to increase it. To help the eavesdropper, the jammer relay can use pure relaying and/or send interference. Each of the problems is formulated as a two-player, noncooperative, zero-sum continuous game. Assuming Gaussian strategies at the source and the jammer relay in the first problem, the Nash equilibrium is found and shown to be achieved with mixed strategies in general. The optimal cumulative distribution functions (cdfs) for the source and the jammer relay that achieve the value of the game, which is the Nash equilibrium secrecy rate, are found. For the second problem, the Nash equilibrium solution is found and the results are compared to the case when the jammer relay is not informed about the source message.

This work considers coordination and bargaining between two selfish users over a Gaussian interference channel. The usual information theoretic approach assumes full cooperation among users for codebook and rate selection. In the scenario investigated here, each user is willing to coordinate its actions only when an incentive exists and benefits of cooperation are fairly allocated. The users are first allowed to negotiate for the use of a simple Han-Kobayashi type scheme with fixed power split. Conditions for which users have incentives to cooperate are identified. Then, two different approaches are used to solve the associated bargaining problem. First, the Nash Bargaining Solution (NBS) is used as a tool to get fair information rates and the operating point is obtained as a result of an optimization problem. Next, a dynamic alternating-offer bargaining game (AOBG) from bargaining theory is introduced to model the bargaining process and the rates resulting from negotiation are characterized. The relationship between the NBS and the equilibrium outcome of the AOBG is studied and factors that may affect the bargaining outcome are discussed. Finally, under certain high signal-to-noise ratio regimes, the bargaining problem for the generalized degrees of freedom is studied.

This work considers coordination and bargaining between two selfish users over a Gaussian interference channel using game theory. The usual information theoretic approach assumes full cooperation among users for codebook and rate selection. In the scenario investigated here, each selfish user is willing to coordinate its actions only when an incentive exists and benefits of cooperation are fairly allocated. To improve communication rates, the two users are allowed to negotiate for the use of a simple Han-Kobayashi type scheme with fixed power split and conditions for which users have incentives to cooperate are identified. The Nash bargaining solution (NBS) is used as a tool to get fair information rates. The operating point is obtained as a result of an optimization problem and compared with a TDM-based one in the literature.

A four terminal Gaussian network composed of a source, a destination, an eavesdropper and a jammer relay is investigated when the jammer relay is causally given the source message. The source aims to increase the achievable secrecy rates, whereas the jammer relay aims to decrease it. To help the eavesdropper and to decrease achievable perfect secrecy rates, the jammer relay can use pure relaying and/or send interference to assist eavesdropping. The problem is formulated as a zero-sum game and the saddle point solutions are found. The results are compared to the case when the jammer relay is not informed about the source message.