Can Karakus

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.

Enhancing Multiuser MIMO Through Opportunistic D2D Cooperation

We propose a cellular architecture that combines multiuser MIMO downlink with opportunistic use of unlicensed Industrial, Scientific, and Medical Radio (ISM) bands to establish device-to-device (D2D) cooperation. The architecture consists of a physical-layer cooperation scheme based on forming downlink virtual MIMO channels through D2D relaying, and a novel resource allocation strategy for such D2D-enabled networks. We prove the approximate optimality of the physical-layer scheme, and demonstrate that such cooperation boosts the effective

Rate splitting is approximately optimal for fading Gaussian interference channels

\mathsf {SNR}

Gaussian Interference Channel With Intermittent Feedback

of the weakest user in the system, especially in the many-user regime, due to multiuser diversity. To harness this physical-layer scheme, we formulate the cooperative user scheduling and the relay selection problem using the network utility maximization framework. For such a cooperative network, we propose a novel utility metric that jointly captures fairness in throughput and the cost of relaying in the system. We propose a joint user scheduling and relay selection algorithm, which we prove to be asymptotically optimal. We study the architecture through system-level simulations over a wide range of scenarios. The highlight of these simulations is an approximately

Encoded distributed optimization

6x

Interference channel with intermittent feedback

improvement in data rate for cell-edge (bottom fifth-percentile) users (over the state-of-the-art) while still improving the overall throughput, and considering various system constraints.

Approximately achieving the feedback interference channel capacity with point-to-point codes

In this paper, we study the 2-user Gaussian interference-channel with feedback and fading links. We show that for a class of fading models, when no channel state information at transmitter (CSIT) is available, the rate-splitting schemes for static interference channel, when extended to the fading case, yield an approximate capacity region characterized to within a constant gap. We also show a constant-gap capacity result for the case without feedback. Our scheme uses rate-splitting based on average interference-to-noise ratio (inr). This scheme is shown to be optimal to within a constant gap if the fading distributions have the quantity log (E [inr]) - E [log (inr)] uniformly bounded over the entire operating regime. We show that this condition holds in particular for Rayleigh fading and Nakagami fading models. The capacity region for the Rayleigh fading case is obtained within a gap of 2.83 bits for the feedback case, and within 1.83 bits for the non-feedback case.

An achievable rate region for Gaussian interference channel with intermittent feedback

We investigate how to exploit intermittent feedback for interference management by studying the two-user Gaussian interference channel (IC). We approximately characterize (within a universal constant) the capacity region for the Gaussian IC with intermittent feedback. We exactly characterize the capacity region of the linear deterministic version of the problem, which gives us insight into the Gaussian problem. We find that the characterization only depends on the forward channel parameters and the marginal probability distribution of each feedback link. The result shows that passive and unreliable feedback can be harnessed to provide multiplicative capacity gain in Gaussian ICs. We find that when the feedback links are active with sufficiently large probabilities, the perfect feedback sum-capacity is achieved to within a constant gap. In contrast to other schemes developed for IC with feedback, our achievable scheme makes use of quantize-map-and-forward to relay the information obtained through feedback, performs forward decoding, and does not use structured codes. We also develop new outer bounds enabling us to obtain the (approximate) characterization of the capacity region.

Redundancy Techniques for Straggler Mitigation in Distributed Optimization and Learning.

Today, many real-world machine learning and data analytics problems are of a scale that requires distributed optimization; unlike in centralized computing, these systems are vulnerable to network and node failures. Recently, coding-theoretic ideas have been applied to mitigate node failures in such distributed computing networks. Relaxing the exact recovery requirement of such techniques, we propose a novel approach for adding redundancy in large-scale convex optimization problems, making solvers more robust against sudden and persistent node failures and loss of data. This is done by linearly encoding the data variables; all other aspects the computation operate as usual. We show that under moderate amounts of redundancy, it is possible to recover a close approximation to the solution under node failures. In particular, we show that encoding with (equiangular) tight frames result in bounded objective error, and obtain an explicit error bound for a specific construction that uses Paley graphs. We also demonstrate the performance of the proposed technique for three specific machine learning problems, (two using real world datasets) namely ridge regression, binary support vector machine, and low-rank approximation.

Straggler Mitigation in Distributed Optimization Through Data Encoding

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.