In this chapter, a framework for power-efficient scheduling in LTE uplink systems is pre- sented. Both the QoS requirements and the channel fading parameters were considered. The scheduling problem was formulated and presented as a multi-stage problem. Then, it was simplified into a single point binary integer programming problem. Subsequently, a low-complexity iterative scheduler was proposed to solve the binary integer programming problem. The iterative scheduler proved to consume slightly more power compared to the binary integer programming scheduling approach, but it has considerably lower computa- tional complexity. Simulation results were used to compare the proposed schedulers with the Proportional Fair scheduler in terms of power efficiency, delay, transmission rate, and complexity. The results show that the proposed schedulers maintained the required QoS and reduced the total transmit power under different practical scenarios. These power sav- ings were achieved because of the schedulers’ attribute of transmitting data at low rates while maintaining the required QoS.
Chapter 3
Low-Complexity Power-Efficient Schedulers for
LTE Uplink with Delay-Sensitive Traffic
3.1
Introduction
The recent years have witnessed unremitting advances in the wireless technology domain, which served to grow the mobile data market. New wireless applications and services have emerged, and accessing data services via mobile devices has increased considerably. To keep up with the increase in mobile data traffic, long term evolution (LTE) technology has been developed to support high performance radio-access technology.
LTE supports high data rate links and enables users to run multiple concurrent appli- cations with heterogeneous quality of service (QoS) requirements, such as live streaming of audio, video, and social media applications. However, to maintain a fixed error perfor- mance, increasing the transmitted data rate is accompanied with a power increase to keep the energy per bit unchanged. Furthermore, as the total number of bits transmitted per unit time grows, the total transmission power per unit time becomes substantially higher as well. Unfortunately, the increasing demand for transmission power is quite higher than the improvement in batteries’ capacity. As most end-user devices are powered from small size batteries, high data rate transmission would reduce the average operation time-per-charge of battery-powered devices. Consequently, the development of power-efficient transmis- sion techniques has become an important design consideration to improve the battery life of mobile devices.
In the literature, there has been increasing interest to better understand and model the power consumption of smartphones. For example, Zhangt et al. [35] designed an online power model that estimates the power consumption of different components in Android smartphones including central processing unit (CPU), liquid-crystal display (LCD), global positioning system (GPS), audio, Wi-Fi and cellular interfaces. The work reported in [36] models the impact of wireless signal strength on smartphone energy by analysing traces collected from 3785 smartphones. A power model of a commercial LTE network is pre- sented in [37], where an application is designed and installed on Android smartphones to collect traces of the power consumption. The study suggests that the power consumption of LTE is 23 times higher than the power consumption of WiFi interfaces.
In LTE systems, the LTE uplink is based on single carrier frequency division multiple access (SC-FDMA). Compared to orthogonal frequency division multiple access (OFDMA), SC-FDMA has lower peak-to-average power ratio (PAPR). The Low PAPR advantage of SC-FDMA is achieved by localized-mapping of the resource blocks (RBs), where each user should be mapped to a subset of contiguous RBs.
Resource scheduling in OFDMA-based systems has been widely investigated in the literature [38]. Many schedulers have been developed to optimize different allocation met- rics such as the sum rate maximization [39], total transmit power minimization [40], and fairness [41]. Several solutions have been presented based on game theory [41], convex optimization and dual decomposition [42, 43, 44], dynamic backpressure policies [45], and interior point methods [46]. However, the contiguity constraint of the SC-FDMA changes the scheduling problem into a non-convex optimization problem [13, 47], and prevents the direct application of power-efficient transmission techniques that are derived for OFDMA systems [38]. Due to the contiguity constraint, the resource allocation in SC-FDMA systems is typically formulated as a binary integer programming (BIP) prob- lem [8, 13, 20, 25, 47].
In the literature, the resource allocation for SC-FDMA systems can be divided into two groups. The first group considers a static data traffic model. The main objective in such scenarios is either to maximize the aggregated cell throughput subject to a maximum transmit power threshold, or to minimize the total transmit power for all users subject to a constant data rate. For example, Wong et al. [47] considered weighted sum-rate max-
imization in the LTE uplink. The problem is formulated and solved as a BIP. A reduced complexity technique that solves the same problem is reported in [13], where the BIP is transformed into a continuous space canonical dual problem, which is solved using algo- rithms with polynomial complexity. Heuristic algorithms were also proposed to solve the BIP problem with lower complexity [48, 49, 50].
It is worth noting that the schemes described in [13, 47, 48, 49, 50] aim at maximizing the system capacity regardless of the power consumption of user equipments (UEs). The schemes assume that UEs transmit at their maximum transmit power, which degrades the power efficiency of the scheduler. Moreover, the schemes do not consider the dynamic nature of the traffic and assumes full-buffer occupancy, which is not necessarily true in practical systems.
The second group considers dynamic traffic models and QoS requirements as the optimal scheduler presented in Chapter 2. However, the proposed scheduler is complex and not globally optimal. In addition, it requires that the average arrival rates of all user traffic bearers to be known at the evolved node-B (eNB).
In this chapter, the global optimal scheduler for the LTE uplink is derived. The sched- uler minimizes the total transmit power of all users while satisfying the delay requirements. The scheduling problem is formulated as a dynamic programming (DP) problem, and the scheduler considers the dynamic nature of the traffic load, maximum transmit power thresh- old, contiguous allocation, and the time-varying fading channel. Moreover, to reduce the complexity, two power-efficient heuristic schedulers are proposed to solve the scheduling problem.
The rest of this chapter is organized as follows. Section 3.2 presents the system model. Section 3.3 discusses the scheduling constraints. The DP problem is formulated and discussed in Section 3.4. The heuristic algorithms are described in Section 3.5. The numerical results are presented in Section 3.6, and finally Section 3.7 concludes the chapter.