Effective capacity in multiple access channels with arbitrary inputs

Abstract

In this paper, we consider a two-user multiple access fading channel under quality-of-service (QoS) constraints. We initially formulate the transmission rates for both transmitters, where the transmitters have arbitrarily distributed input signals. We assume that the receiver performs successive decoding with a certain order. Then, we establish the effective capacity region that provides the maximum allowable sustainable arrival rate region at the transmitters' buffers under QoS guarantees. Assuming limited transmission power budgets at the transmitters, we attain the power allocation policies that maximize the effective capacity region. As for the decoding order at the receiver, we characterize the optimal decoding order regions in the plane of channel fading parameters for given power allocation policies. In order to accomplish the aforementioned objectives, we make use of the relationship between the minimum mean square error and the first derivative of the mutual information with respect to the power allocation policies. Through numerical results, we display the impact of input signal distributions on the effective capacity region performance of this two-user multiple access fading channel.

Effective Capacity in Broadcast Channels with Arbitrary Inputs

“…As clearly noticed in (8)-(11), a closed-form solution for P 1 or P 2 cannot be obtained easily, which is mainly due to the tied relation between P 1 and P 2 . For instance, P 1 is a function of P 2 as observed in (8) for z ∈ Z, whereas P 2 is a function of P 1 as seen in (11) for z ∈ Z c . Therefore, we need to employ numerical techniques that consist of iterative solutions.…”

“…In this paper, different than the aforementioned studies and our recent study [11], we focus on a broadcast channel scenario in which one transmitter employs arbitrarily distributed input signaling to convey data to two receivers under average power constraints and QoS requirements. We define the effective capacity region and provide an algorithm to obtain the optimal power allocation policies that maximize this region by enforcing the relation between the mutual information and the MMSE.…”

“…Moreover, the authors in [18] studied the effective capacity of point-to-point channels and derived the optimal power allocation policies to maximize the system throughput by employing arbitrary input distributions under an average power constraint. More recently, we explored the effective capacity regions of multiple access channels with arbitrary input distributions and identified the optimal power allocation policies under average transmission power constraints [11].…”

“…Hence, in region Z, we first obtain P 2 by solving (9) for given P 1 , and then we find P 1 by solving (8) after inserting P 2 into (8). Similarly, in region Z c , we first obtain P 2 by solving (11) for given P 1 , and then we find P 1 by solving (10) after inserting P 2 into (10). We can employ bisection search methods to obtain P 1 and P 2 .…”

Detection performances and effective capacity of cognitive radio with primary user emulators

“…Unlike the metrics in the physical layer, the metrics in the link layer are mainly the probabilities of non-empty buffer and the connection qualities. Effective capacity was adopted as the metric in the link layer, and was defined to characterise the diverse traffic performances [10,11]. The effective capacity was considered as the maximum arrival rates of the data in a buffer under specific constraints [12][13][14].…”