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We study medium access in FMCW radar networks. In particular, we propose a slotted ALOHA protocol and analyze probability of interference between radars as a function of system parameters such as total number of radars, chirp duration, number of chirps in a repetition interval, as well as medium access probability. We see that the characteristics of interference probability in FMCW radar networks are very different from those in wireless communication networks. We observe that interference probability also depends on the number of chirps in a radar packet. We further propose a notion of throughput and study its variation with various parameters. We perform extensive simulations to verify our analytical results.

We propose job scheduling algorithms to minimize job migration and server running costs in cloud computing platforms offering Infrastructure as a Service. We first consider algorithms that assume knowledge of job-size on arrival of jobs. We characterize the optimal cost subject to system stability. We develop a drift-plus-penalty framework based algorithm that can achieve optimal cost arbitrarily closely. Specifically this algorithm yields a trade-off between delay and costs. We then relax the job-size knowledge assumption and give an algorithm that uses readily offered service to the jobs. We show that this algorithm gives order-wise identical cost as the job size based algorithm. We illustrate the performance of the proposed algorithms and compare these to the existing algorithms via simulation.

We study BS scheduling for cellular downlink. We propose a drift-plus-penalty based BS scheduling algorithm that minimizes BS running and switching costs while ensuring network stability. We illustrate the performance of the proposed algorithm via simulation.

We propose a class of binary queue length information based max-weight scheduling algorithms for wireless networks. In these algorithms, the scheduler, in addition to channel states, only needs to know when a link's queue length crosses a prescribed threshold. We show that these algorithms are throughput optimal. Further, we incorporate time-since-last-service (TSLS) information to improve delay and service regularity of the scheduling algorithms while ensuring throughput optimality. We also perform simulations to illustrate throughput, delay and service regularity performance of the proposed algorithms.