Container-based operation system (OS) level virtualization has been adopted by many edge-computing platforms. However, for an edge server, inter-container communications, and container management consume significant CPU resources. Given an application composed of interdependent tasks, the number of such operations is closely related to the dependency between the scheduled tasks. Thus, to improve the execution efficiency of an application in an edge server, task scheduling and task containerizing need to be considered together. To this end, a joint task scheduling and containerizing (JTSC) scheme is developed in this article. Experiments are first carried out to quantify the resource utilization of container operations. System models are then built to capture the features of task execution in containers in an edge server with multiple processors. With these models, joint task scheduling and containerizing is conducted as follows. First, tasks are scheduled without considering containerization, which results in initial schedules. Second, based on system models and guidelines gained from the initial schedules, several containerization algorithms are designed to map tasks to containers. Third, task execution durations are updated by adding the time for inter-container communications, and then the task schedules are updated accordingly. The JTSC scheme is evaluated through extensive simulations. The results show that it reduces inefficient container operations and enhances the execution efficiency of applications by 60 percent.

This paper considers a multiple-input single-output (MISO) wireless powered communication network (WPCN) and aims to maximize the energy efficiencies (EEs) of the individual users. Specifically, we study user-wise EE maximization problems for TDMA-based WPCN (T-WPCN) and SDMA-based WPCN (S-WPCN), which are both non-convex due to their sum-of-ratios objective functions. We first solve the T-WPCN problem by transforming into a convex parametric problem and obtain its optimal solution by iteratively solving convex problems. Next, we solve the S-WPCN problem by convexifying it using feasible set reduction scheme. We show that T-WPCN outperforms S-WPCN when there is no minimum throughput requirement, while we can observe that S-WPCN outperforms T-WPCN when minimum throughput requirement is high. Simulation results verify our theoretical findings and show the effectiveness of our proposed schemes.

This paper studies an energy-efficient (EE) design for wireless powered mobile edge computing (MEC) system. We introduce a new EE performance metric called computation energy efficiency (CEE), which is defined as the total computation bits (achieved by both local computing and offloading) per energy consumption. Specifically, we study a CEE maximization problem for the wireless powered MEC system, which is non-convex and thus cannot be solved by standard convex optimization techniques. To optimally solve the problem, we divide it into its inner problem and outer problem. Although the inner problem is non-convex, we derive its optimal solution in a closed-form. Then, based on the solution of the inner problem, we optimally solve the outer problem by using the Dinkelbach method. Simulation results show the effectiveness of our proposed scheme in terms of CEE performance and convergence speed.

We consider a multiple-input single-output wireless powered communication network (WPCN), where the single-antenna users harvest energy from a multi-antenna access point (AP) and then transmit information back to the AP. With multiple antennas, the AP can perform energy beamforming in the downlink and exploit multiplexing- or (receive) beamforming-gain in the uplink. We consider maximizing the sum of the users' energy efficiencies (EEs) by jointly optimizing the energy beamforming of the AP, transmit powers of the users, and time allocation. We formulate EE maximization problems for both TDMA-based WPCN (T-WPCN) and SDMA-based WPCN (S-WPCN), which are non-convex because they have the sum-of-ratios objective functions. We optimally solve the former by reformulating it into an equivalent parametric problem, whose solution can be obtained by iteratively solving convex problems, while the latter is optimally solved by convexifying it using the so-called feasible set reduction scheme. We show that the T-WPCN outperforms S-WPCN when there is no minimum throughput requirement, while we can observe that the S-WPCN outperforms T-WPCN when minimum throughput requirement is high. The simulation results verify our theoretical findings and demonstrate the effectiveness of our proposed schemes.

We discuss the globally optimal energy-efficient design of a 2D relay network. Different from the existing routing protocols on energy saving, which finds the minimal energy route for a given data rate, the proposed algorithm jointly optimizes routing and data rate to maximize energy efficiency (EE) defined as the achievable data rate per power consumption. We propose a low-complexity algorithm to circumvent the huge complexity of the exhaustive search for the network EE maximization and prove its global optimality. Moreover, the proposed algorithm is implemented in a distributed fashion because each relay needs to send its routing information only to the relays in its adjacent tiers, which significantly reduces the signaling overhead of the centralized implementation. Our analysis on the worst-case complexity in a fading channel shows that the complexity of the proposed algorithm increases linearly while that of the exhaustive search increases exponentially as the tier index increases. Simulation results confirm that the proposed algorithm outperforms the existing routing protocols on energy saving and achieves the globally optimal network EE at a significantly lower complexity than the exhaustive search.

We study energy-efficient secure link adaptation for large-scale device-to-device (D2D) underlaid cellular networks, where base stations (BSs), D2D transmitters, and eavesdroppers (Eve nodes) are distributed as independent homogeneous Poisson point processes. First, we analyze the impact of the underlaid D2D network on the secrecy performance, i.e., secrecy spectral efficiency (SE) and secrecy energy efficiency (EE) of the existing cellular network. We show that when the ratio of the Eve node density to the BS density is above a certain threshold, the secrecy performance of the cellular network first increases and then decreases with the D2D power. Otherwise, it decreases with the D2D power. Next, based on the results, we develop a linkadaptation scheme that controls the D2D power, the confidential message rate, and the redundancy rate to strike a balance between the D2D network's secrecy EE and secrecy SE while guaranteeing a required secrecy performance of the existing cellular network. Simulation results show that the proposed link adaptation scheme can achieve all the points on the Pareto boundary.

We study energy-efficient power allocation for the sensing-based spectrum sharing (SBSS) of the cognitive users operating in an uplink multiuser MIMO mode. Different from the existing energy-efficient designs that consider system-wise energy efficiency, we consider user-wise energy efficiencies and optimize them in a Pareto sense. However, due to the non-convexity of the formulated problem, finding the optimal solution is a challenging problem. To resolve the non-convexity of the problem, we propose to reduce the feasible set by including additional power constraints. Also, we show that the feasible set reduction technique preserves the optimality of the original problem. Simulation results show that the proposed scheme significantly enhances the energy efficiency of the cognitive users compared with the existing spectral-efficient designs.

We study the energy efficiency issue in 5G communications scenarios, where cognitive femtocells coexist with picocells operating at the same frequency bands. Optimal energy-efficient power allocation based on the sensing-based spectrum sharing (SBSS) is proposed for the uplink cognitive femto users operating in a multiuser MIMO mode. Both hard-decision and soft-decision schemes are considered for the SBSS. Different from the existing energy-efficient designs in multiuser scenarios, which consider system-wise energy efficiency, we consider user-wise energy efficiency and optimize them in a Pareto sense. To resolve the nonconvexity of the formulated optimization problem, we include an additional power constraint to convexify the problem without losing global optimality. Simulation results show that the proposed schemes significantly enhance the energy efficiency of the cognitive femto users compared with the existing spectral-efficient designs.

In this paper, we investigate energy-efficient clustering and medium access control for cellular-based machine-to-machine (M2M) networks to minimize device energy consumption and prolong network battery lifetime. First, we present an accurate energy consumption model that considers both static and dynamic energy consumptions, and utilize this model to derive the network lifetime. Second, we find the cluster size to maximize the network lifetime and develop an energy-efficient cluster-head selection scheme. Furthermore, we find feasible regions where clustering is beneficial in enhancing network lifetime. We further investigate communications protocols for both intra- and inter-cluster communications. While inter-cluster communications use conventional cellular access schemes, we develop an energy-efficient and load-adaptive multiple access scheme, called n-phase carrier sense multiple access with collision avoidance (CSMA/CA), which provides a tunable tradeoff between energy efficiency, delay, and spectral efficiency of the network. The simulation results show that the proposed clustering, cluster-head selection, and communications protocol design outperform the others in energy saving and significantly prolong the lifetimes of both individual nodes and the whole M2M network.

Compared to half duplex communications, full duplex communications can significantly improve link capacity. However, in a large scale wireless network such as a wireless mesh network, the capacity gain from full duplex communications has not been fully investigated. To this end, a metric of network capacity called transmission capacity is studied in this paper for a full duplex wireless network. It captures the maximum transmission throughput in a unit area, subject to a certain outage probability. The key challenge of deriving transmission capacity is to characterize the aggregate interference of the typical link in a full duplex wireless network, which is completely different from that in a half duplex wireless network. In this paper, stochastic geometry is employed to model the network topology as a Thomas cluster point process and then the aggregate interference is characterized as a shot-noise process. Based on these models, the transmission capacity is derived. Analytical results show that under the same network density the distribution of aggregate interference in a full duplex wireless network is more dispersed than that in a half duplex wireless network. Comparisons of transmission capacity between a full duplex network and a half duplex network reveal that the capacity gain from full duplex communications is limited due to severe aggregate interference. This result implies that self-interference cancellation alone cannot ensure scalable full duplex wireless networking.

Licensed band communication systems have recently begun trying to utilize unlicensed bands. Due to lack of coordination, however, this spectrum utilization results in strong interference to other unlicensed band equipment (UBE). This problem highlights the need for coexistence scenario that will prevent UBE performance degradation. In this paper, we propose a new access control mechanism for downlink cellular networks using the unlicensed band. First, we analyze the impact of the cellular networks on the UBE outage performance. Based on the analysis, we then obtain a decision threshold for the unlicensed band access control. In the proposed control mechanism, the decision threshold is adaptively controlled by considering the density of the UBE. The numerical results demonstrate that the proposed access control mechanism guarantees the UBE's target performance under various network circumstances.

In this paper, we study energy-efficient transmit power control for multi-tier multi-antenna (MIMO) heterogeneous cellular networks (HetNets), where each tier operates in closed-access policy and base stations (BSs) in each tier are distributed as a stationary Poisson point process (PPP). Each BS serves multiple users at a fixed distance away from it. We first study noncooperative energy-efficient power control, where each tier selfishly chooses its transmit power to maximize its network energy efficiency (EE). This is modeled by a noncooperative power control game. We prove the existence and the uniqueness of the Nash equilibrium of the game. Moreover, we analyze the effects of circuit power and BS densities of the tiers on their transmit power at the Nash equilibrium. Then, we investigate cooperative energy-efficient power control, where all the tiers cooperatively choose their transmit power to optimize their network EE. This cooperative power control is formulated as a multiobjective problem. To obtain Pareto-optimal solutions of the problem, we develop an algorithm that alternately updates the transmit power of the tiers. In addition to the transmit power, the circuit power consumption of the operating BSs and their active antennas affects the network EE. Due to the circuit power, activating all the BSs and turning on all the antennas may not be optimal in maximizing the network EE. Motivated by this observation, we also develop energy-efficient BS activation control and antenna activation control schemes. Finally, we extend the analysis to the highest signal-to-interference-plus-noise ratio (SINR) association, where each user connects to the BS that offers the highest SINR among the BSs in its tier. Simulation results show that the proposed energy-efficient designs significantly improve the network EE at the cost of small spectral efficiency loss compared with the spectral-efficient designs.

In this paper, an optimal beamforming and power allocation for the sensing-based spectrum sharing (SBSS) that employs a soft decision on the state of the primary user is proposed. Different from the existing soft-decision-based SBSS that employs a single antenna and adjusts only transmit power, in the proposed scheme, the secondary transmitter employs multiple transmit antennas and controls the beamforming direction and the transmit power. We design the optimal beamforming and power allocation under the constraints of total transmit power and interference power, where both average and instantaneous power constraints are considered. Simulation results demonstrate that the proposed technique can significantly improve the average throughput of the secondary user compared with the existing methods. The proposed beamforming for the soft-decision-based SBSS is a promising scheme, particularly for cognitive femto base stations (BSs) to boost up the spectral efficiency while managing the interference to the macro BSs.

In this paper, we study energy-efficient power control for spectrum-sharing networks, where multiple systems of transmitting nodes share the same spectrum. In the spectrum sharing networks, reducing the transmit power of one system is always beneficial to the energy efficiencies (EEs) of the other systems. However, reducing the transmit power may not be always optimal in maximizing the EE of a system. These conflicting interests among the systems are molded by a non-cooperative energy-efficient power control game, where each system chooses its transmit power to maximize its own EE. We prove the existence and uniqueness of the Nash equilibrium of this game. However, due to the selfish behaviors of the systems in the network, the Nash equilibrium is in general inefficient. To improve the EE of the Nash equilibrium, we incorporate pricing to the non-cooperative energy-efficient power control game. For the spectrum sharing network consisting of two systems, we find a sufficient condition on the transmit powers of the systems assuring the pricing-based game to be a supermodular game, which always admits at least one Nash equilibrium.

In this paper, we develop an uplink pilot and downlink link adaptation approach to improve the energy efficiency (EE) of mobile users in time division duplexing (TDD) multi-user multiple input and multiple output (MU-MIMO) systems. Assuming reciprocity between uplink and downlink channels, the downlink transmission is based on uplink channel estimation. While more uplink pilot power ensures more accurate channel estimation and better downlink performance, it incurs higher energy consumption of mobile users. This paper reveals the relationship and tradeoff among pilot power, channel estimation, and downlink link adaptation that achieves the highest energy efficiency for mobile users. We show that the energy efficiency of different users can be decoupled because the downlink average throughput of each user is independent of the pilot powers of other users and energy-efficient design can be done on a per-user basis. Based on the analysis, we propose an uplink pilot and downlink link adaptation algorithm to improve the EE of mobile users. Simulation results are finally provided to demonstrate the significant gain in energy efficiency for mobile users.

Diversity and transmission rate are the main performance metrics for MIMO techniques. We propose a low-complexity MIMO scheme that achieves both full diversity and full rate (FDFR) over flat fading channels when there is a sufficiently many number of transmit and receive antennas. The proposed scheme uses energy spreading transforms (EST's) to spread a symbol energy to multiple transmit antennas and makes iterative decision-feedback detection possible at the receiver. Simulation results show that the proposed scheme performs close to the (interference-free) genie-aided system when there are sufficiently large number of transmit and receive antennas.

In this letter, the principle of iterative detection based on energy spreading transform (EST) introduced by Hwang and Li is applied to the multiuser-detection (MUD) problem. Different from turbo principle, EST enables iterative signal detection at the receiver without any bandwidth increase resulting from channel coding. It is shown by analysis as well as computer simulation that a significant performance gain in user separation can be obtained by employing ESTs consisting of different random permutations for different users.

In this paper, we apply recently developed energy spreading transform (EST) technique to down-link MC-CDMA systems for frequency and spatial diversity. The performance of the proposed EST based MC-CDMA systems is independent of the number of active users, which is different from all current MC-CDMA systems. It is demonstrated by computer simulation that the BER for the EST based system is 1.1 times 110-4while it is 1.6 times 10-3for a MC-CDMA system without EST when the system is with full load and Eb/No= 12 dB. The EST based MC-CDMA system also facilitates transmit diversity and MIMO techniques when multiple transmit and receive antennas are available. In particular, we develop symbol shuffle and combined schemes for spatial diversity for a EST based MC-CDMA system with multiple transmit antennas at the base station. Compared with a MC-CDMA system without EST, the required Eb/Nofor 1% BER for the developed scheme is improved by about 4 dB.

In this paper, we investigate transmit diversity for MC-CDMA down-link mobile communications. We apply recently developed energy spreading transform (EST) in MC-CDMA systems to pick up diversity in frequency-selective wireless channels and to improve signal detection performance. It is shown by computer simulation that the required Eb/No's for a 10-2bit-error-rate (BER) are improved by about 5 dB for both an indoor channel and an urban channel compared with a MC-CDMA system without an EST. For a system with two transmit antennas and Alamouti's code, the required Eb/Nofor an EST based MC-CDMA system is improved by 2.7 dB for an indoor channel and 3.3 dB for an outdoor channel compared with a MC-CDMA system without an EST. For a system with multiple transmit antennas, symbol shuffling scheme, together with an EST, has been proposed for transmit diversity. Even though symbol shuffling scheme alone is not as good as Alamouti's code, it can be combined with Alamouti's code to reach matched filter bound (MFB) for systems with over four transmit antennas. Comparison of our transmit-diversity approach with the widely used MMSE detection for MC-CDMA systems is also provided.

In this paper, an improved scheme for energy spreading transform (EST) based equalization is proposed. In the improved scheme, optimal frequency-and time-domain filters that maximize signal-to-interference-noise ratio (SINR) are employed to enhance the performance while the filters used in the original scheme [1] can be regarded as an approximation of the optimal filters.

Multiple transmit and receive antenna arrays can be used to form multiple input and multiple output (MIMO) systems for diversity and multiplexing in wireless communications. In this paper, we develop iterative signal-detection schemes based on energy spreading transform (EST) (T. Hwang and Y. Li) for MIMO channels. The EST in a MIMO system improves signal-detection performance by spreading the symbol energy over the space and time domain. It also enables iterative signal detection without employing channel coding. Analytical and simulation results demonstrate that the performance of the proposed schemes is very close to that of the genie-aided receiver when there are a sufficiently large number of receive antennas and signal-to-noise ratio (SNR) is above a threshold

We use a space-time energy spreading transform (ST-EST) for iterative signal detection in multiple input and multiple output (MIMO) wireless systems. Theoretical and simulation results demonstrate that when the signal-to-noise ratio (SNR) is above a threshold, the performance of the system is very close to that of the genie-aided (interference-free) receiver.

In this paper, a novel iterative equalization is proposed. Different from the existing turbo-like equalization, the proposed one uses an energy spreading transform (EST) instead of a (soft) channel decoder, which separates equalization and decoding. The complexity of the proposed equalization approach is comparable to that of decision-feedback equalization (DFE). However, analytical and simulation results demonstrate that its performance is very close to the matched filter bound (MFB) when the signal-to-noise ratio (SNR) is above a threshold.