Basak Guler

Selective Interference Alignment for MIMO Cognitive Femtocell Networks

This paper presents a novel cross-tier interference management solution for coexisting two-tier networks by exploiting cognition and coordination between tiers via the use of agile radios. The cognitive users sense their environment to determine the receivers they are interfering with, and adapt to it by designing their precoders using interference alignment (IA) in order to avoid causing performance degradation to nearby receivers. The proposed approach judiciously chooses the set of users to be aligned at each receiver as a subset of the cross-tier interferers, hence is termed selective IA. The proposed solution includes identification of the subspace in which cross-tier interference signals would be aligned followed by a distributed algorithm to identify the precoders needed at the selected interferers. The intra-tier interference is then dealt with using minimum mean squared error (MMSE) interference suppression. Numerical results demonstrate the effectiveness of selective IA for both uplink and downlink interference management.

Interference Alignment for Cooperative MIMO Femtocell Networks

This paper proposes a method for applying the idea of Interference Alignment (IA) in femtocell networks. In order to manage the uplink interference caused by macrocell users at the femtocell base stations (FBS), cooperation between macrocell users with the closest femtocell base stations could be used to align the received signals of macrocell users in the same subspace at multiple FBS simultaneously. We develop a method to apply IA while providing the QoS requirements of macrocell users, in terms of minimum received SINR at the macrocell base station (MBS). With this approach, the BER performance of femtocell users is shown to improve, while maintaining the quality of the communication channel of macrocell users.

Uplink Interference Management for Coexisting MIMO Femtocell and Macrocell Networks: An Interference Alignment Approach

This paper considers uplink interference management for two-tier cellular systems by way of Interference Alignment (IA). In order to manage the uplink interference caused by macrocell users at the femtocell base stations (FBS), cooperation between macrocell users with the closest femtocell base stations is proposed with the goal of aligning the received signals of macrocell users in the same subspace at multiple FBSs. The precoder design for macrocell users is accomplished using successive semidefinite programming relaxations. The proposed solution aims to minimize the cross-tier interference leaked to the femtocells while providing the macrocell users with a minimum received signal to interference plus noise ratio (SINR) at the macrocell base station (MBS). Intra-tier femtocell interference is dealt with minimum mean squared error (MMSE) interference suppression. Numerical results demonstrate that the proposed two-tier interference management approach improves the performance of femtocell users, while maintaining the desired quality of the communication channel of macrocell users.

Optimal strategies for targeted influence in signed networks

Online social communities often exhibit complex relationship structures, ranging from close friends to political rivals. As a result, persons are influenced by their friends and foes differently. Network applications can benefit from accompanying these structural differences in propagation schemes. In this paper, we study the optimal influence propagation policies for networks with positive and negative relationship types. We tackle the problem of minimizing the end-to-end propagation cost of influencing a target person in favor of an idea by utilizing the relationship types in the underlying social graph. The propagation cost is incurred by social and physical network dynamics such as frequency of interaction, the strength of friendship and foe ties, propagation delay or the impact factor of the propagating idea. We extend this problem by incorporating the impact of message deterioration and ignorance. We demonstrate our results in both a controlled environment and the Epinions dataset. Our results show that judicious propagation schemes lead to a significant reduction in the average cost and complexity of influence propagation compared to naïve myopic algorithms.

Remote source coding with two-sided information

This paper studies the impact of side information on the lossy compression of a remote source, one which is indirectly accessed by the encoder. In particular, we identify the conditions under which sharing side information between the encoder and the decoder may be superior or inferior to having two-sided, i.e., correlated but not identical, side information. As a special case, we characterize the optimal rate-distortion function for a direct binary source with two-sided information by proposing an achievable scheme and proving a converse. This example suggests a hierarchy on the impact of side information, in that the performance is mainly determined by how well the decoder learns about the source and then by how well the encoder learns about the decoders observation.

Using Social Sensors for Influence Propagation in Networks With Positive and Negative Relationships

Online social communities often exhibit complex relationship structures, ranging from close friends to political rivals. As a result, persons are influenced by their friends and foes differently. Future network applications can benefit from integrating these structural differences in propagation schemes through socially aware sensors. In this paper, we introduce a propagation model for such social sensor networks with positive and negative relationship types. We tackle two main scenarios based on this model. The first one is to minimize the end-to-end propagation cost of influencing a target person in favor of an idea by utilizing sensor observations about the relationship types in the underlying social graph. The propagation cost is incurred by social and physical network dynamics such as propagation delay, frequency of interaction, the strength of friendship/foe ties or the impact factor of the propagating idea. We next extend this problem by incorporating the impact of message deterioration and ignorance, and by limiting the number of persons influenced against the idea before reaching the target. Second, we study the propagation problem while minimizing the number of negatively influenced persons on the path, and provide extensions to elaborate on the impact of network parameters. We demonstrate our results in both an artificially created network and the Epinions signed network topology. Our results show that judicious propagation schemes lead to a significant reduction in the average cost and complexity of network propagation compared to naïve myopic algorithms.

A study on compressing graphical structures

Many real-world systems such as the WWW, Semantic Web, logical expressions, and social networks can be represented in graphical forms. This paper studies compressing graphical structures from a graph entropy point of view. We show that structural entropy is equal to the chromatic entropy of the characteristic graph, termed the structural characteristic graph. We establish the relation between structural entropy and graph entropy, and investigate the cases when (conditional) graph entropy is equal to (conditional) chromatic entropy.

Communicating in a socially-aware network: Impact of relationship types

Communication networks are linked to and influenced by human interactions. Socially-aware systems should integrate these complex relationship patterns in the network design. This paper studies the impact of friendship and antagonistic relationships between individuals on optimal network propagation policies. We develop a network propagation model for signed networks, and determine the optimal policies to influence a target node with an opinion while minimizing the total number of persons against it. We also provide extensions to this problem to elaborate on the impact of network parameters, such as minimum-delay propagation, while limiting the number of persons influenced against the idea before reaching the target. We provide numerical evaluations in a synthetic setup as well as the Epinions online social dataset. We demonstrate that propagation schemes with social and influence-centric constraints should take into account the relationship types in network design.

On lossy transmission of correlated sources over a multiple access channel

We study lossy communication of correlated sources over a multiple access channel. In particular, we provide a joint source-channel coding scheme for transmitting correlated sources with decoder side information, and study the conditions under which separate source and channel coding is optimal. For the latter, the encoders and/or the decoder have access to a common observation conditioned on which the two sources are independent. By establishing necessary and sufficient conditions, we show the optimality of separation when the encoders and the decoder both have access to the common observation. We also demonstrate that separation is optimal when only the encoders have access to the common observation whose lossless recovery is required at the decoder. As a special case, we study separation for sources with a common part. Our results indicate that side information can have significant impact on the optimality of source-channel separation in lossy transmission.

Semantic index assignment

Conventional performance criteria for communication networks do not take into account the semantics of the data to be communicated. For example, (word) error rates treat errors between semantically similar words (car and automobile) and semantically distant words (car and computer) equally. In reality, the meaning of the message is distorted much less when automobile is recovered instead of computer when the intended message is car. In order to correctly address the performance of a semantic system, a new performance criterion is necessary that takes into account the semantic similarities between recovered words. We study in this paper the index assignment problem with a source that produces semantic messages to develop a better understanding of how their meanings affect the semantic error performance in a noisy communication network, and in particular for networks with queries. To this end, we utilize the semantic distances based on lexical taxonomies as a distortion measure in a communication system. Our findings indicate the need for development of semantics-aware physical systems that allow for better integration of human factors and intelligence within complex systems design.