Improving PCMendurance is a fundamental issue when it is considered as an alternative to replace DRAM as main memory. Memory-based wear leveling (WL) is an effective way to improve PCM endurance, but its major challenge is how to efficiently determine the appropriate memory pages for allocation or swapping. In this article, we present a constant-cost WL design that is compatible with existing memory management. Two implementations, namely bucket-based and array-based WL, with constant-time (or nearly zero) search cost are proposed to be integrated into the OS layer and the hardware layer, respectively, as well as to trade between time and space complexity. The results of experiments conducted based on an implementation in Android, as well as simulations with popular benchmarks, to evaluate the effectiveness of the proposed design are very encouraging.
In simultaneous wireless information and power transfer (SWIPT), practical receiver architectures consisting of an information receiver and an energy harvester have been proposed in place of an ideal receiver capable of performing two tasks simultaneously using the same circuits. In this paper, we present a novel receiver architecture design incorporating an interplay between the information receiver and the energy harvester to enhance the performance of the practical SWIPT receiver. In particular, the energy level of the received signal monitored at the energy harvester is fed back to the information receiver to assist information decoding at the information receiver. The symbol-error-rate (SER) and diversity analyses show that the proposed receiver architecture could yield a higher diversity order for unconventional constellations where any two distinct symbols have distinct energy levels, and the same diversity order for conventional modulations. Simulation of PAM and QAM modulations verifies the analysis, shows the improved SER performance of the proposed receiver architecture, and illustrates the energy-dimension-augmented decision regions. Some insights into designing enhanced practical SWIPT receivers are provided as a result.
Resident applications, which autonomously awaken mobile devices, can gradually and imperceptibly drain device batteries. This paper introduces the concept of alarm similarity into wakeup management for mobile systems in connected standby. First, we define hardware similarity to reflect the degree of energy savings and time similarity to reflect the impact on user experience. We then propose a policy that aligns alarms based on their similarity to save standby energy while maintaining the quality of the user experience. Finally, we integrate our design into Android and conduct extensive experiments on a commercial smartphone running popular mobile apps. The results demonstrate that our design can further extend the standby time achieved with Android's native policy by up to one-third.
Application paradigms will increasingly exceed a mobile device's physical boundaries. This paper presents a system solution for a mobile device to mount remote sensors on other devices. Our design is generic to mobile senor stacks, thus supporting unmodified apps and commodity sensors. Furthermore, it uses an asynchronous access model to facilitate semantics passing and data reporting in between. Such semantic information allows the development of an energy-efficient reporting policy for remote sensing applications. The results of experiments conducted on commercial Android smartphones with popular apps demonstrate that our design is very efficient in terms of energy consumption and completion time.
This paper considers a two-cell uplink cochannel multiple-input multiple-output (MIMO) network with users sequentially arriving to the network. We study the problem of sequential base station (BS) selection for the users, with the selection criterion based on the degrees of freedom (DoF) available for the new arriving user. We find that different sequential BS selections affect individual and network performance in terms of the individual and network sum DoF as well as the number of admissible users in the network. We propose a method to build the tree structure for sequential BS selection, which carries trellis information for individual and system-wide selections. The properties of the tree are analytically studied. It turns out that by adopting an interference coordination strategy based on the concept of interference alignment, a better individual and network performance can be achieved. Simulation compares the proposed DoF-based BS selection and traditional BS selection schemes and highlights the advantages of the proposed scheme.
Mobile applications will become progressively more complicated and diverse. Heterogeneous computing architectures like big.LITTLE are a hardware solution that allows mobile devices to combine computing performance and energy efficiency. However, software solutions that conform to the paradigm of conventional fair scheduling and governing are not applicable to mobile systems, thereby degrading user experience or reducing energy efficiency. In this article, we exploit the concept of application sensitivity, which reflects the user’s attention on each application, and devise a user-centric scheduler and governor that allocate computing resources to applications according to their sensitivity. Furthermore, we integrate our design into the Android operating system. The results of experiments conducted on a commercial big.LITTLE smartphone with real-world mobile apps demonstrate that the proposed design can achieve significant gains in energy efficiency while improving the quality of user experience.
An increasing number of mobile devices are being equipped with 802.11n interfaces to support bandwidth-intensive applications; however, the improved bandwidth increases power consumption. To address the issue, researchers are focusing on antenna management. In this paper, we present a dynamic antenna management (DAM) scheme to improve the uplink energy efficiency on mobile devices whose packet workloads may vary significantly and frequently. First, we model antenna management as an optimization problem, with the objective of minimizing the energy required to transmit a sequence of variable-length packets with random arrival times. Then, we propose an optimal offline algorithm to solve the problem, as well as a competitive online algorithm that has a provable performance guarantee and allows compatible implementations on 802.11n mobile devices. To evaluate our scheme, we conducted extensive simulations based on real mobile user traces and application transmission patterns. Nearly all commercial 802.11n mobile devices support the power save mode (PSM). Our results demonstrate that DAM can improve the energy efficiency of PSM significantly at a cost of slight throughput degradation.
Mobile devices have increasingly been used to run multimedia applications which are extremely downlink-intensive. The conventional rate adaptive and/or margin adaptive approach for radio resource allocation may result in unnecessary energy consumption on mobile devices, which will not be energy efficient for mobile multimedia applications. In this paper, we develop an energy adaptive approach and design an energy-efficient downlink resource allocation scheme to support multimedia applications. The objective is to minimize the total energy consumption of mobile devices for data reception while meeting the data rate requirements at mobile devices and the transmit power constraint at the base station. We show that the optimization problem is NP -hard and then propose an efficient algorithm that has a provable performance guarantee under a certain condition. We have conducted extensive simulations to evaluate the efficacy of the proposed algorithm and our results provide useful insights into the design of energy-efficient resource allocation for wireless systems.
This paper studies joint subchannel allocation, power allocation, and beamforming for simultaneous wireless information and power transfer (SWIPT) in multiuser downlink orthogonal frequency-division multiple access (OFDMA) systems. We formulate a multi-objective optimization (MOO) problem where the objectives are to maximize both the information rate and the harvested power for all users in the system. We approach the MOO problem with two proposed methods, i.e., semidefinite relaxation based weighted aggregation (SDR-WA) and multi-objective genetic algorithm (MOGA). Simulation compares the achievable Pareto optimal solution set yielded by these methods, and illustrates the tradeoffs of the sum information rate vs. the sum harvested power in the system.
Graphics-intensive mobile games are becoming increasingly popular, but such applications place high demand on device CPUs and GPUs. The design of current mobile systems results in unnecessary energy waste due to lack of consideration of application phases and user attention (a “demand-level” gap) and because each processor administers power management autonomously (a “processor-level” gap). This paper proposes a user-centric CPU-GPU governing framework which aims to reduce energy consumption without significantly impacting the user experience. To bridge the gap at the demand level, we identify the user demand at runtime and accordingly determine appropriate governing policies for the respective processors. On the other hand, to bridge the gap at the processor level, the proposed framework interprets the frequency scaling intents of processors based on the observation of the CPU-GPU interaction and the processor status. We implemented our framework on a Samsung Galaxy S4, and conducted extensive experiments with real-world 3D gaming apps. Experimental results showed that, for an application being highly interactive and frequent phase changing, our proposed framework can reduce energy consumption by 45.1% compared with state-of-the-art policy without significantly impacting the user experience.