Optimal Power Control and Scheduling for Real-Time and Non-Real-Time Data

Abstract

We consider a joint scheduling-and-powerallocation problem of a downlink cellular system. The system consists of two groups of users: real-time (RT) and non-realtime (NRT) users. Given an average power constraint on the base station, the problem is to find an algorithm that satisfies the RT hard deadline constraint and NRT queue stability constraint. We propose two sum-rate-maximizing algorithms that satisfy these constraints as well as achieving the system's capacity region. In both algorithms, the power allocation policy has a closed-form expression for the two groups of users. However, interestingly, the power policy of the RT users differ in structure from that of the NRT users. The first algorithm is optimal for the on-off channel model with a polynomial-time scheduling complexity in the number of RT users. The second, on the other hand, works for any channel fading model which is shown, through simulations, to have an average complexity that is close-to-linear. We also show the superiority of the proposed algorithms over existing approaches using extensive simulations.

Dynamic power control for time-critical networking with heterogeneous traffic

“…Lyapunov optimization theory has been widely applied for developing dynamic algorithms that schedule users with packets with deadlines. In [17][18][19][20], the authors consider the rate maximization under power and delay constraints. In [17], the authors consider the power allocation for users with hard-deadline constraints.…”

“…In [17], the authors consider the power allocation for users with hard-deadline constraints. In [18], the authors consider the rate maximization of non-real-time users while satisfying the packet drop rate for users with packets with deadlines. In [19,20], consider packets with deadlines for scheduling real-time traf ic in wireless environments.…”

Offloading Deadline-Constrained Cellular Traffic

“…E [x|U (k)] is the conditional expectation of the random variable x given U (k). Squaring (12) and (13), taking the conditional expectation then summing over i, the drift becomes bounded by…”

Optimal power control and scheduling under hard deadline constraints for continuous fading channels

“…We show through simulations that the complexity, in the number of users, of the proposed algorithm is close-to-linear. More details on the results of this work is presented in [12]. The rest of this paper is organized as follows.…”

“…15: Set P * i (k) = P X (S * R (k) , i) for all i ∈ N R , set µ i * NR (k) = T − i∈S * R (k) µ i (k) and set r i (k) = a i (k) if Q i (k) < B max and 0 otherwise ∀i ∈ N NR . 16: Update (1), (11) and (12) at the end of the kth slot.…”