Opportunistic Scheduling for Full-Duplex Uplink-Downlink Networks

We study opportunistic scheduling and the sum capacity of cellular networks with a full-duplex multi-antenna base station and a large number of single-antenna half-duplex users. Simultaneous uplink and downlink over the same band results in uplink-to-downlink interference, degrading performance. We present a simple opportunistic joint uplink-downlink scheduling algorithm that exploits multiuser diversity and treats interference as noise. We show that in homogeneous networks, our algorithm achieves the same sum capacity as what would have been achieved if there was no uplink-to-downlink interference, asymptotically in the number of users. The algorithm does not require interference CSI at the base station or uplink users. It is also shown that for a simple class of heterogeneous networks without sufficient channel diversity, it is not possible to achieve the corresponding interference-free system capacity. We discuss the potential for using device-to-device side-channels to overcome this limitation in heterogeneous networks.Comment: 10 pages, 2 figures, to appear at IEEE International Symposium on Information Theory (ISIT) '1

Interference Channel with Intermittent Feedback

We investigate how to exploit intermittent feedback for interference management. Focusing on the two-user linear deterministic interference channel, we completely characterize the capacity region. We find that the characterization only depends on the forward channel parameters and the marginal probability distribution of each feedback link. The scheme we propose makes use of block Markov encoding and quantize-map-and-forward at the transmitters, and backward decoding at the receivers. Matching outer bounds are derived based on novel genie-aided techniques. As a consequence, the perfect-feedback capacity can be achieved once the two feedback links are active with large enough probabilities.Comment: Extended version of the same-titled paper that appears in IEEE International Symposium on Information Theory (ISIT) 201

Privacy-Utility Trade-off of Linear Regression under Random Projections and Additive Noise

Data privacy is an important concern in machine learning, and is fundamentally at odds with the task of training useful learning models, which typically require the acquisition of large amounts of private user data. One possible way of fulfilling the machine learning task while preserving user privacy is to train the model on a transformed, noisy version of the data, which does not reveal the data itself directly to the training procedure. In this work, we analyze the privacy-utility trade-off of two such schemes for the problem of linear regression: additive noise, and random projections. In contrast to previous work, we consider a recently proposed notion of differential privacy that is based on conditional mutual information (MI-DP), which is stronger than the conventional $(\epsilon, \delta)$-differential privacy, and use relative objective error as the utility metric. We find that projecting the data to a lower-dimensional subspace before adding noise attains a better trade-off in general. We also make a connection between privacy problem and (non-coherent) SIMO, which has been extensively studied in wireless communication, and use tools from there for the analysis. We present numerical results demonstrating the performance of the schemes.Comment: A short version is published in ISIT 201

Comparing traditional sports and electronic sports

Electronic sports (eSports) viewership numbers have been growing over the years. Video games such as League of Legends, Counter Strike: Global Offensive, or Hearthstone: Heroes of Warcraft have a large following. During the League of Legends regular season matches, 250,000 people regularly tune in to watch the games. Professional players such as Enrique “Xpeke” Cedeno-Martinez have 470,000 Twitter followers. In this study, we built two visualizations to explore similarities and differences between eSports and traditional sports. We focused on the salaries earned by eSports players and the emergence of a new way of interacting with professional players via live streaming. We then compared how fan loyalties differ between the two fields