arXiv subjects

Gourav Saha

Publications and source records attributed to Gourav Saha.

5 recordsLinked to original sources

arXiv2025

Delay-Optimal Transmission Scheduling Policies for Time-Correlated Fading Channels

Millimeter-wave (mmWave) networks have the potential to support high throughput and low-latency requirements of 5G-and-beyond communication standards. But transmissions in this band are highly vulnerable to attenuation and blockages from humans, buildings, and foliage, which increase end-to-end packet delays. This work designs dynamic scheduling policies that minimize end-to-end packet delays while keeping packet transmission costs low. Specifically, we consider a mmWave network that consists of a transmitter that transmits data packets over an unreliable communication channel modeled as a Gilbert-Elliott channel.The transmitter operates under an ACK/NACK feedback model and does not observe the channel state unless it attempts a transmission. The objective is to minimize a weighted average cost consisting of end-to-end packet delays and packet transmission costs. We pose this dynamic optimization problem as a partially observable Markov decision process (POMDP). To the best of our knowledge, this is the first POMDP formulation for mmWave network with partial channel state information that considers delay minimization. We show that the POMDP admits a solution that has a threshold structure, i.e., for each queue length, the belief (the conditional probability that the channel is in a good state) is partitioned into intervals, and the transmitter sends j packets when the belief lies in the j-th interval. We then consider the case when the system parameters such as the packet arrival rate, and the transition probabilities of the channel are not known, and leverage these structural results in order to use the actor-critic algorithm to efficiently search for a policy that is locally optimal.

math.OC↗

arXiv2021

On the Optimal Duration of Spectrum Leases in Exclusive License Markets with Stochastic Demand

This paper addresses the following question which is of interest in designing efficient exclusive-use spectrum licenses sold through spectrum auctions. Given a system model in which customer demand, revenue, and bids of wireless operators are characterized by stochastic processes and an operator is interested in joining the market only if its expected revenue is above a threshold and the lease duration is below a threshold, what is the optimal lease duration which maximizes the net customer demand served by the wireless operators? Increasing or decreasing lease duration has many competing effects; while shorter lease duration may increase the efficiency of spectrum allocation, longer lease duration may increase market competition by incentivizing more operators to enter the market. We formulate this problem as a two-stage Stackelberg game consisting of the regulator and the wireless operators and design efficient algorithms to find the Stackelberg equilibrium of the entire game. These algorithms can also be used to find the Stackelberg equilibrium under some generalizations of our model. Using these algorithms, we obtain important numerical results and insights that characterize how the optimal lease duration varies with respect to market parameters in order to maximize the spectrum utilization. A few of our numerical results are non-intuitive as they suggest that increasing market competition may not necessarily improve spectrum utilization. To the best of our knowledge, this paper presents the first mathematical approach to optimize the lease duration of spectrum licenses.

cs.GT↗

arXiv2021

Optimal Spectrum Partitioning and Licensing in Tiered Access under Stochastic Market Models

We consider the problem of partitioning a spectrum band into M channels of equal bandwidth, and then further assigning these M channels into P licensed channels and M-P unlicensed channels. Licensed channels can be accessed both for licensed and opportunistic use following a tiered structure which has a higher priority for licensed use. Unlicensed channels can be accessed only for opportunistic use. We address the following question in this paper. Given a market setup, what values of M and P maximize the net spectrum utilization of the spectrum band? While this problem is of fundamental nature, it is highly relevant practically, e.g., in the context of partitioning the recently proposed Citizens Broadband Radio Service band. If M is too high or too low, it may decrease spectrum utilization due to limited channel capacity or due to wastage of channel capacity, respectively. If P is too high (low), it will not incentivize the wireless operators who are primarily interested in unlicensed channels (licensed channels) to join the market. These tradeoffs are captured in our optimization problem which manifests itself as a two-stage Stackelberg game. We design an algorithm to solve the Stackelberg game and hence find the optimal M and P. The algorithm design also involves an efficient Monte Carlo integrator to evaluate the expected value of the involved random variables like spectrum utilization and operators' revenue. We also benchmark our algorithms using numerical simulations.

cs.GT↗

arXiv2015

Maximizing Profit of Cloud Brokers under Quantized Billing Cycles: a Dynamic Pricing Strategy based on Ski-Rental Problem

In cloud computing, users scale their resources (computational) based on their need. There is massive literature dealing with such resource scaling algorithms. These works ignore a fundamental constrain imposed by all Cloud Service Providers (CSP), i.e. one has to pay for a fixed minimum duration irrespective of their usage. Such quantization in billing cycles poses problem for users with sporadic workload. In recent literature, Cloud Broker (CB) has been introduced for the benefit of such users. A CB rents resources from CSP and in turn provides service to users to generate profit. Contract between CB and user is that of pay-what-you-use/pay-per-use. However CB faces the challenge of Quantized Billing Cycles as it negotiates with CSP. We design two algorithms, one fully online and the other partially online, which maximizes the profit of the CB. The key idea is to regulate users demand using dynamic pricing. Our algorithm is inspired by the Ski-Rental problem. We derive competitive ratio of these algorithms and also conduct simulations using real world traces to prove the efficiency of our algorithm.

cs.DC↗

arXiv2014

Towards Analog Memristive Controllers

Memristors, initially introduced in the 1970s, have received increased attention upon successful synthesis in 2008. Considerable work has been done on modeling and applications in specific areas, however, very little is known on the potential of memristors for control applications. Being nanoscopic variable resistors, it is intuitive to think of using them as a variable gain. The main contribution of this paper is the development of a memristive analog gain control framework and theoretic foundation of a control strategy which can be implemented using this framework. Analog memristive controllers may find applications in control of large array of miniaturized devices where robust and adaptive control is needed due to parameter uncertainty and ageing issues.

math.OC↗