Sugumar Murugesan

Miao He, Sugumar Murugesan, Junshan Zhang

Integrating volatile renewable energy resources into the bulk power grid is challenging, due to the reliability requirement that at each instant the load and generation in the system remain balanced. In this study, we tackle this challenge for smart grid with integrated wind generation, by leveraging multi-timescale dispatch and scheduling. Specifically, we consider smart grids with two classes of energy users - traditional energy users and opportunistic energy users (e.g., smart meters or smart appliances), and investigate pricing and dispatch at two timescales, via day-ahead scheduling and realtime scheduling. In day-ahead scheduling, with the statistical information on wind generation and energy demands, we characterize the optimal procurement of the energy supply and the day-ahead retail price for the traditional energy users; in realtime scheduling, with the realization of wind generation and the load of traditional energy users, we optimize real-time prices to manage the opportunistic energy users so as to achieve systemwide reliability. More specifically, when the opportunistic users are non-persistent, i.e., a subset of them leave the power market when the real-time price is not acceptable, we obtain closedform solutions to the two-level scheduling problem. For the persistent case, we treat the scheduling problem as a multitimescale Markov decision process. We show that it can be recast, explicitly, as a classic Markov decision process with continuous state and action spaces, the solution to which can be found via standard techniques. We conclude that the proposed multi-scale dispatch and scheduling with real-time pricing can effectively address the volatility and uncertainty of wind generation and energy demand, and has the potential to improve the penetration of renewable energy into smart grids.

Sugumar Murugesan, Philip Schniter, Ness B. Shroff

We focus on the downlink of a cellular system, which corresponds to the bulk of the data transfer in such wireless systems. We address the problem of opportunistic multiuser scheduling under imperfect channel state information, by exploiting the memory inherent in the channel. In our setting, the channel between the base station and each user is modeled by a two-state Markov chain and the scheduled user sends back an ARQ feedback signal that arrives at the scheduler with a random delay that is i.i.d across users and time. The scheduler indirectly estimates the channel via accumulated delayed-ARQ feedback and uses this information to make scheduling decisions. We formulate a throughput maximization problem as a partially observable Markov decision process (POMDP). For the case of two users in the system, we show that a greedy policy is sum throughput optimal for any distribution on the ARQ feedback delay. For the case of more than two users, we prove that the greedy policy is suboptimal and demonstrate, via numerical studies, that it has near optimal performance. We show that the greedy policy can be implemented by a simple algorithm that does not require the statistics of the underlying Markov channel or the ARQ feedback delay, thus making it robust against errors in system parameter estimation. Establishing an equivalence between the two-user system and a genie-aided system, we obtain a simple closed form expression for the sum capacity of the Markov-modeled downlink. We further derive inner and outer bounds on the capacity region of the Markov-modeled downlink and tighten these bounds for special cases of the system parameters.

Shanshan Wang, Sugumar Murugesan, Junshan Zhang

We consider a cognitive radio network with multiple primary users (PUs) and one secondary user (SU), where a spectrum server is utilized for spectrum sensing and scheduling the SU to transmit over one of the PU channels opportunistically. One practical yet challenging scenario is when \textit{both} the PU occupancy and the channel fading vary over time and exhibit temporal correlations. Little work has been done for exploiting such temporal memory in the channel fading and the PU occupancy simultaneously for opportunistic spectrum scheduling. A main goal of this work is to understand the intricate tradeoffs resulting from the interactions of the two sets of system states - the channel fading and the PU occupancy, by casting the problem as a partially observable Markov decision process. We first show that a simple greedy policy is optimal in some special cases. To build a clear understanding of the tradeoffs, we then introduce a full-observation genie-aided system, where the spectrum server collects channel fading states from all PU channels. The genie-aided system is used to decompose the tradeoffs in the original system into multiple tiers, which are examined progressively. Numerical examples indicate that the optimal scheduler in the original system, with observation on the scheduled channel only, achieves a performance very close to the genie-aided system. Further, as expected, the optimal policy in the original system significantly outperforms randomized scheduling, pointing to the merit of exploiting the temporal correlation structure in both channel fading and PU occupancy.

Ashok Sundaresan, Sugumar Murugesan, Sean Davis et al.

The group affect or emotion in an image of people can be inferred by extracting features about both the people in the picture and the overall makeup of the scene. The state-of-the-art on this problem investigates a combination of facial features, scene extraction and even audio tonality. This paper combines three additional modalities, namely, human pose, text-based tagging and CNN extracted features / predictions. To the best of our knowledge, this is the first time all of the modalities were extracted using deep neural networks. We evaluate the performance of our approach against baselines and identify insights throughout this paper.