Navid Abedini

Opportunities for network coding: To wait or not to wait

It has been well established that reverse-carpooling based network coding can significantly improve the efficiency of multi-hop wireless networks. However, in a stochastic environment when there are no opportunities to code because of packets without coding pairs, should these packets wait for a future opportunity or should they be transmitted without coding? To help answer that question we formulate a stochastic dynamic program with the objective of minimizing the long-run average cost per unit time incurred due to transmissions and delays. In particular, we develop optimal control actions that would balance between costs of transmission against those of delays. In that process we seek to address a crucial question: what should be observed as the state of the system? We analytically show that just the queue lengths is enough if it can be modeled as a Markov process. Subsequently we show that a stationary policy based on queue lengths is optimal and describe a procedure to find such a policy. We further substantiate our results with simulation experiments for more generalized settings.

Content caching and scheduling in wireless networks with elastic and inelastic traffic

The rapid growth of wireless content access implies the need for content placement and scheduling at wireless base stations. We study a system under which users are divided into clusters based on their channel conditions, and their requests are represented by different queues at logical front ends. Requests might be elastic (implying no hard delay constraint) or inelastic (requiring that a delay target be met). Correspondingly, we have request queues that indicate the number of elastic requests, and deficit queues that indicate the deficit in inelastic service. Caches are of finite size and can be refreshed periodically from a media vault. We consider two cost models that correspond to inelastic requests for streaming stored content and real-time streaming of events, respectively. We design provably optimal policies that stabilize the request queues (hence ensuring finite delays) and reduce average deficit to zero [hence ensuring that the quality-of-service (QoS) target is met] at small cost. We illustrate our approach through simulations.

Realtime streaming with guaranteed QoS over wireless D2D networks

We consider a group of co-located wireless peer devices that desire to synchronously receive a live content stream. The devices are each equipped with an expensive unicast base-station-to-device (B2D) interface, as well as a broadcast device-to-device (D2D) interface over a shared medium. The stream is divided into blocks, which must be played out soon after their initial creation. If a block is not received within a specific time after its creation, it is rendered useless and dropped. The blocks in turn are divided into random linear coded chunks to facilitate sharing across the devices. We transform the problem into the two questions of (i) deciding which peer should broadcast a chunk on the D2D channel at each time, and (ii) how long B2D transmissions should take place for each block. We analytically develop a provably-minimum-cost algorithm that can ensure that QoS targets can be met for each device. We study its performance via simulations, and present an overview of our implementation on Android phones using the algorithm as a basis.

Opportunities for network coding: to wait or not to wait

It has been well established that wireless network coding can significantly improve the efficiency of multihop wireless networks. However, in a stochastic environment, some of the packets might not have coding pairs, which limits the number of available coding opportunities. In this context, an important decision is whether to delay packet transmission in hope that a coding pair will be available in the future or transmit a packet without coding. This paper addresses this problem by establishing a stochastic dynamic framework whose objective is to minimize a long-run average cost. We identify an optimal control policy that minimizes the costs due to a combination of transmissions and packet delays. We show that the optimal policy would be stationary, deterministic, and threshold-type based on queue lengths. Our analytical approach is applicable for many cases of interest such as time-varying on/off channels. We further substantiate our results with simulation experiments for more generalized settings.

Content caching and scheduling in wireless broadcast networks with elastic and inelastic traffic

The rapid growth of wireless content access implies the need for content placement and scheduling at wireless base stations. We study a system under which clients are divided into clusters based on their channel conditions, and their requests are represented by different queues at logical frontends. Requests might be elastic (implying no hard delay constraint) or inelastic (requiring that a delay target be met). Correspondingly, we have request queues that indicate the number of elastic requests, and deficit queues that indicate the deficit in inelastic service. Caches are of finite size, and can be refreshed periodically from a media vault. We design provably optimal policies that stabilize the request queues (hence ensuring finite delays) and reduce average deficit to zero (hence ensuring that the QoS target is met). We illustrate our approach through simulations.

Harnessing multiple wireless interfaces for guaranteed QoS in proximate P2P networks

We consider the problem of content distribution to a group of cooperative wireless peer devices that desire the same block of information. The QoS metric is that peers are all required to receive the block by a fixed deadline, with a certain target probability. The block is divided into chunks, which are received via two methods that can be used simultaneously - (i) the B2P (base-station-to-peer) network: each peer has an unreliable, expensive, unicast channel to a cellular base station, and (ii) the P2P (peer-to-peer) network: peers can share the content over a free, lossless internal wireless broadcast network. Chunks are coded using random linear codes to alleviate the duplicate chunk reception issue. We seek an algorithm that can attain the QoS metric at the lowest cost of using B2P network. We transform the problem into two questions of (i) deciding which peer should broadcast on the P2P channel at each time, and (ii) how long B2P transmissions should take place. We use dynamic programming and queueing ideas to show that for large field sizes, a combination of Max-Rank-First and Non-min-Rank-First policies for P2P transmissions is optimal, and determine the stopping time for B2P transmissions using a Markov chain model. We provide performance bounds for finite field sizes, and illustrate our insights using simulations.

Distributed Synchronization for Device-to-Device Communications in an LTE Network

We study the problem of distributed time and frequency synchronization for device-to-device communication in LTE [1]. LTE is an OFDMA system that crucially uses tight time and frequency synchronization between UEs and the base station for intracell and intercell interference coordination. For consistency with the cyclic prefix length and tone spacing used in LTE, we target an accuracy of a few microseconds for time synchronization and 150 Hz for frequency synchronization. We examine the problem both from a link and a system-level perspective. Link-level challenges involve designing a synchronization signal for computationally and energy efficient receiver algorithms, and system-level challenges involve achieving consistent timing using a distributed algorithm while leveraging the multiuser aspect of the problem to improve performance.