Power-delay Tradeoff Research Articles

MAESTRO-X: Distributed Orchestration of Rotary-Wing UAV-Relay Swarms

This work details a scalable framework to orchestrate a swarm of rotary-wing UAVs serving as cellular relays to facilitate beyond line-of-sight connectivity and traffic offloading for ground users. First, a Multiscale Adaptive Energyconscious Scheduling and TRajectory Optimization (MAESTRO) framework is developed for a single UAV. Aiming to minimize the time-averaged latency to serve user requests, subject to an average UAV power constraint, it is shown that the optimization problem can be cast as a semi-Markov decision process, and exhibits a multiscale structure: outer actions on radial wait velocities and terminal service positions minimize the longterm delay-power trade-off, optimized via value iteration; given these outer actions, inner actions on angular wait velocities and service trajectories minimize a short-term delay-energy cost; finally, rate adaptation is embedded along the trajectory to leverage air-to-ground channel propagation conditions. A novel hierarchical competitive swarm optimization scheme is developed in the inner optimization, to devise high-resolution trajectories via iterative pair-wise updates. Next, MAESTRO is eXtended to UAV swarms (MAESTRO-X) via scalable policy replication, enabled by a decentralized command-and-control network augmented with: (1) spread maximization to proactively position UAVs to serve future requests; (2) consensus-driven conflict resolution to orchestrate scheduling decisions based on delay-energy costs including queuing dynamics; (3) adaptive frequency reuse to improve spectrum utilization across the network; and (4) a piggybacking mechanism allowing UAVs to serve multiple ground users simultaneously. Numerical evaluations show that, for user requests of 10 Mbits, generated according to a Poisson arrival process with rate 0.2 req/min/UAV, single-agent MAESTRO offers 3.8× faster service than a high-altitude platform and 29% faster than a static UAV deployment; moreover, for a swarm of 3 UAV-relays, MAESTRO-X delivers data payloads 4.7× faster than a successive convex approximation scheme; and remarkably, a single UAV optimized via MAESTRO outclasses 3 UAVs optimized via a deep-Q network by 38%.

Cross-Layer Resource Allocation in HetNet NOMA Systems With Dynamic Traffic Arrivals

Non-orthogonal multiple access (NOMA) has become a promising candidate aiming to enhance the system performance in the fifth generation of wireless communication. However, most existing studies on the resource allocation of NOMA systems only consider the short-term performance improvement but ignore the problems caused by the imperfect time-varying channels and the dynamic traffic arrivals, which will result in unsuccessful transmission issues. Different from the existing approaches, we propose a long-term cross-layer resource allocation model with dynamic traffic arrivals and limited channel information. With the low-overhead one-bit feedback, the optimal decoding order, user scheduling, and power allocation are analyzed. Specifically, the problem is formulated as a stochastic optimization framework to minimize long-term power consumption. By applying the Lyapunov optimization framework, the problem can be transformed into a joint traffic rate control and mixed-integer programming resource allocation problem. This NP-hard problem is difficult to be solved directly. Therefore, we devise an efficient sub-optimal algorithm with the dynamic penalty factor. In our theoretical analysis, we prove that the delay-power tradeoff can be achieved by tuning a control parameter. The simulation results confirm that our proposed algorithms can efficiently reduce the power consumption compared with the baseline algorithms while satisfying the QoS requirements.

DESIGN SPACE EXPLORATION OF LOW POWER FULL ADDER ACROSS MULTIPLE LOGIC STYLES AND PROCESS

The signal processing in digital domain becoming ubiquitous, a full adder circuit module has gained prominence for its optimization in terms of its speed, power dissipation, noise margin and area. Further, with the advent of mobile applications on electronic devices, low power has emerged as an overriding design criterion. The proposed work aims to explore the design space of a low power full adder circuit and the implications of logic styles on power-delay trade-offs. Further, the issues impacting the selection of process for design for a given application are addressed. A 1-bit full adder circuit in static CMOS logic style and hybrid logic style is designed and optimized for low-power by carrying out exhaustive simulations using the LT spice simulator in 65 nm and 45 nm standard process to explore their power envelope, before low power process becomes necessary for design. Low power designs are demonstrated and the trade-offs with speed and area are explored.

A low power and PVT variation tolerant mux-latch for serializer interface and on-chip serial link

Multiplexer (mux) latch is one of the key components for serializer interface to admit communication at Gbps. This work describes the working of a 2:1 mux-latch coupled to a power delivery network (PDN) and a CPU core drawing abrupt current. In comparison to conventional designs, multiplexer and latch operate at the same time, which is quite different because enable inputs are imperative to start latch operation. An analytical model is also derived to understand power delay trade-off and to choose gate sizes to obtain a comparable delay without increasing power. The average power and delay in post-layout are 257.7 μW and 35 ps, respectively, in a 90-nm CMOS, power supply voltage (Vdd) of 1.1 V and a clock switching at 6.25 GHz. However, a 117OC change in temperature at distinct corner allows average power to vary between 32.8 and 64 μW. The corresponding variation is 135–183.7 μW as Vdd switches from 0.75 to 1.1 V. In addition to so, delay changes between 5-14 ps and 3–12 ps for the given change in temperature and Vdd, respectively. Nonetheless, the effective supply voltage VP oscillates with time as the CPU core draws abrupt current. The delay values due to AC noise are found to be different than VP having no noise and the corresponding jitter changes linearly as a function of noise.

Effective Workload Allocation in Fog Device based on Power Consumption and Delay Tradeoff

Fog computing that emerges as an important paradigm, describes decentralized computing architecture between cloud and devices. It includes potential challenges, such as increase in traffic overhead, since all requests are sent to the main server that causes delay, which cannot be tolerated in delay sensitive applications and the usage of inappropriate scheduling causes high power consumption in fog device. These challenges must be overcome by employing effective workload. In this paper, in order to find effective workload allocation based on power and delay tradeoff various scheduling algorithms like SJF (Shortest Job First), FCFS (First Come First Served) and RR (Round Robin) are implemented in fog device and its power and delay trade off are analyzed in fog computing subsystem.

Low-Latency and Energy-Efficient Wireless Communications With Energy Harvesting

Energy harvesting (EH) aided communications hold a great potential in the design of green communication systems for their high energy efficiency. However, the random power supply due to EH may cause an intolerable delay in data transmission. To overcome this, a Reliable Energy Source (RES) is desired to provide transmission power when the large delay is induced. In this paper, we study the delay-optimal scheduling policy for EH aided communications with the constraint of average power provided by RES. More specifically, the delay-minimal scheduling is obtained through the two-dimensional Markov chain modeling and linear programming (LP) formulation. To further reduce the computational complexity, we present a value iteration algorithm, based on which we not only reveal a threshold-based structure of the delay-optimal scheduling policy for EH-aided communications with large-capacity batteries, but also conceive a low complexity policy that is asymptotically optimal. For EH-aided communications with finite-capacity batteries, we present a unified framework based on large deviation theory. The non-asymptotic framework demonstrates that the delay-power tradeoff curve of the low complexity scheduling policy is capable of converging to that of the delay-optimal policy exponentially as the capacity of the battery increases.

Joint Queue-Aware and Channel-Aware Scheduling for Non-Orthogonal Multiple Access

Non-orthogonal multiple access (NOMA) has received considerable attention as a promising candidate for future mobile networks. How to reduce traffic delay through cross-layer scheduling in NOMA systems is a challenging issue. In this paper, a cross-layer approach for NOMA systems is designed to fulfill the delay requirements. In particular, scheduling decisions should adapt to the system dynamics. With the distribution information of the system dynamics, the constrained Markov decision process (CMDP) is employed to characterize the scheduling decision. The optimal scheduling policy can be obtained by converting the scheduling problem to linear programming. A computational approach based on the Kronecker product is conceived to formulate the linear programming when the dimension of the CMDP is high. Then the optimal delay-power tradeoff can be achieved. Moreover, the optimal decoding order is demonstrated that it can be obtained directly based on the channel states. The threshold-based structure of the optimal scheduling decisions is revealed. Without the distribution information, the Lyapunov approach is exploited to propose an online scheduling policy, where the virtual power queue is used to tackle the power constraint. In NOMA systems, our CMDP-based approach achieves a better performance over the Lyapunov approach by taking advantage of the distribution information.

Analysis of Power Allocation for Non-Orthogonal Multiple Access

Most multiple access schemes provide orthogonal access to the users in time, frequency, code and space, this is not true for NOMA, where each user operates in the same band and at the same time where they are distinguished by their power levels. It uses superposition coding at the transmitter such that the successive interference cancellation (SIC) receiver can separate the users both in the uplink and in the downlink channels. The users in (NOMA) are classified based on power, while in Orthogonal Multiple Access (OMA) it is classified based on time, frequency, and code. The NOMA system contains a power-delay tradeoff and hence power efficiency becomes critical for Ultra-Reliable Low Latency Communication (URLLC), especially where a huge number of devices are battery-powered. Combining these considerations, we simulate Dynamic Power Allocation (DPA) for power domain non-orthogonal multiple access (PD-NOMA) with user mobility. For small and clumsy battery-powered IoT devices, power efficiency becomes critical. Further, flexibility is also important to communicate with diverse machine-type devices as well as human users while meeting a variety of quality of service (QoS) requirements. The performance of the DPA is compared with Static Power Allocation under user mobility.

Decentralized computation offloading for multi-user mobile edge computing: a deep reinforcement learning approach

Mobile edge computing (MEC) emerges recently as a promising solution to relieve resource-limited mobile devices from computation-intensive tasks, which enables devices to offload workloads to nearby MEC servers and improve the quality of computation experience. In this paper, an MEC enabled multi-user multi-input multi-output (MIMO) system with stochastic wireless channels and task arrivals is considered. In order to minimize long-term average computation cost in terms of power consumption and buffering delay at each user, a deep reinforcement learning (DRL)-based dynamic computation offloading strategy is investigated to build a scalable system with limited feedback. Specifically, a continuous action space-based DRL approach named deep deterministic policy gradient (DDPG) is adopted to learn decentralized computation offloading policies at all users respectively, where local execution and task offloading powers will be adaptively allocated according to each user’s local observation. Numerical results demonstrate that the proposed DDPG-based strategy can help each user learn an efficient dynamic offloading policy and also verify the superiority of its continuous power allocation capability to policies learned by conventional discrete action space-based reinforcement learning approaches like deep Q-network (DQN) as well as some other greedy strategies with reduced computation cost. Besides, power-delay tradeoff for computation offloading is also analyzed for both the DDPG-based and DQN-based strategies.

Energy-Efficient UAV Communications Under Stochastic Trajectory: A Markov Decision Process Approach

A typical application of unmanned aerial vehicles (UAVs) is the surveillance of distant targets, where data collected by its cameras need to be transmitted back to a ground terminal (GT) for further processing. However, due to the high mobility of a UAV, the power consumption for transmitting data tasks might be very high, especially when the UAV is far away from the GT. To guarantee the energy-efficiency of the UAV, the collected data tasks can be preprocessed to reduce the amount of data transmitted, which can potentially save the power consumption of the UAV. In this article, a random walk model is implemented to capture the mobility of the UAV. Our design goal is to minimize the average power consumption of the UAV under the delay constraint. We adopt the probabilistic scheduling based on the Markov Decision Process (MDP) framework to schedule the transmission and computation of the data tasks in the UAV. The power-optimal scheduling policy can be obtained by converting the joint optimization problem to linear programming (LP), and thus the optimal power-delay tradeoff can be achieved. Finally, the optimization results are validated with extensive simulations.

Analysis for Joint Delay-Power Tradeoff with Buffer/Channel-Aware and Its FPGA Implementation in Wireless Sensor Networks

In this paper, we aim to investigate the delay-power tradeoff problem which is attracting widespread interest due to its importance in wireless technology. This research has two main objectives. First, to assess the effect of different system parameters on the performance metrics. Second, to provide a solution for this optimization problem. A two-state, slow-fading channel is categorized into good and bad channel states. An adaptive transmission and random data arrivals are considered in our model. Each channel category has its own Markov chain, which is used in modeling the system. A joint Buffer-Aware and Channel-Aware (BACA) problem was introduced. In addition, an enhanced iterative algorithm was introduced for obtaining a sub-optimal delay-power tradeoff. The results show that the tradeoff curve is piecewise linear, convex and decreasing. Furthermore, a channel-aware system was investigated to provide analysis of the effect of system parameters on the delay and power. The obtained results show that the dominant factors that control the system performance are based on the arrival rate and the channel goodness factor. Moreover, a simplified field programable gate array (FPGA) hardware implementation for the channel aware system scheduler is presented. The implementation results show that the consumed power for the proposed scheduler is 98.5 mW and the maximum processing clock speed is 190 MHz.

Delay-Optimal and Energy-Efficient Communications With Markovian Arrivals

In this paper, delay-optimal and energy-efficient communication is studied for a single link under Markov random arrivals. We present the optimal tradeoff between delay and power over Additive White Gaussian Noise (AWGN) channels and extend the optimal tradeoff for block fading channels. Under time-correlated traffic arrivals, we develop a cross-layer solution that jointly considers the arrival rate, the queue length, and the channel state in order to minimize the average delay subject to a power constraint. For this purpose, we formulate the average delay and power problem as a Constrained Markov Decision Process (CMDP). Based on steady-state analysis for the CMDP, a Linear Programming (LP) problem is formulated to obtain the optimal delay-power tradeoff. We further show the optimal transmission strategy using a Lagrangian relaxation technique. Specifically, the optimal adaptive transmission is shown to have a threshold type of structure, where the thresholds on the queue length are presented for different transmission rates under the given arrival rates and channel states. By exploiting the result, we develop a threshold-based algorithm to efficiently obtain the optimal delay-power tradeoff. We show how a trajectory-sampling version of the proposed algorithm can be developed without the prior need of arrival statistics.

Power Efficiency and Delay Tradeoff of 100G Energy Efficient Ethernet Protocol

This paper investigates the dual-mode power-saving strategy designed for the 100G Energy Efficient Ethernet. The process of this strategy is a sequence of cycles, where each cycle is an interval elapsed between two consecutive instants when the buffer of the Ethernet interface becomes empty. In each cycle, the interface first enters the fast-wake mode to perform the conditional sleep-mode selection of the strategy. If the number of arrivals during the fast-wake mode reaches a threshold, the interface directly wakes up; otherwise, it proceeds to the deep-sleep mode. The sequence of cycles switches between two types: deep-sleep cycles with deep-sleep mode and light-sleep cycles without deep-sleep mode. We analyze the dual-mode strategy based on the condition of the number of arrivals during the fast-wake mode. We first derive the weights of conditions according to the feature of the conditional sleep-mode operation, and then calculate the unconditioned performance measure of the dual-mode strategy based on the weighted average of that of these two kinds of cycles. Finally, we obtain the close-form expressions of the power efficiency and the mean delay, based on which we provide a set of parameter selection rules. We show that the dual-mode strategy with these rules can select suitable sleep modes according to the instantaneous traffic rate, and thus perform well under bursty input traffic.

AI-Enhanced Offloading in Edge Computing: When Machine Learning Meets Industrial IoT

The Industrial Internet of Things (IIoT) enables intelligent industrial operations by incorporating artificial intelligence (AI) and big data technologies. An AI-enabled framework typically requires prompt and private cloud-based service to process and aggregate manufacturing data. Thus, integrating intelligence into edge computing is without doubt a promising development trend. Nevertheless, edge intelligence brings heterogeneity to the edge servers, in terms of not only computing capability, but also service accuracy. Most works on offloading in edge computing focus on finding the power-delay trade-off, ignoring service accuracy provided by edge servers as well as the accuracy required by IIoT devices. In this vein, in this article we introduce an intelligent computing architecture with cooperative edge and cloud computing for IIoT. Based on the computing architecture, an AI enhanced offloading framework is proposed for service accuracy maximization, which considers service accuracy as a new metric besides delay, and intelligently disseminates the traffic to edge servers or through an appropriate path to remote cloud. A case study is performed on transfer learning to show the performance gain of the proposed framework.

Non-Orthogonal Multiple Access for Delay-Sensitive Communications: A Cross-Layer Approach

Non-orthogonal multiple access (NOMA) has attracted great attention in the fifth-generation (5G) system to meet the rapidly increasing demand on quality of service (QoS). Among various QoS requirements, the urgent latency requirement has been expected to be provided for the delay-sensitive applications. In this paper, the delay-optimal uplink transmission in NOMA is studied over a block fading channel based on a cross-layer design, by which average latency is minimized with reliability provided by power allocation. In particular, the superposition coding in the physical layer and the scheduling in the network layer are jointly determined by the joint probabilities on the decisions of coding orders and transmission rates with the aware channel and queue states. With a constrained Markov decision process (CMDP), the cross-layer optimization is formulated to minimize the average delay subject to the constraints on power and reliability. The optimal delay-power tradeoff is obtained by formulating an equivalent linear programming (LP), which can be presented for multiple users based on a unified algorithm. Moreover, the optimal joint scheduling and superposition coding (JSSC) policy is constructed by using the structural properties. Based on the optimal cross-layer design, the transmission latency is optimized in the practical NOMA system.

Low Cost Functional Obfuscation of Reusable IP Cores Used in CE Hardware Through Robust Locking

Intellectual property (IP) cores used in consumer electronics devices mandate protection against reverse engineering (RE) attacks. Functional obfuscation protects the design functionality by enhancing the complexity of RE attacks. This paper proposes a novel low cost (based on power-delay tradeoff) functional obfuscation methodology through employment of robust IP locking technique. In the proposed obfuscation methodology, several strong multipairwise secure IP locking block designs are presented that can only be actuated through application of valid serial key bits. As demonstration, this paper also shows a practical example of a functional obfuscated netlist structure of FIR filter. Proposed obfuscation on comparison with a recent approach for several DSP cores yielded a power reduction of ~10%, design cost reduction ~ 6.5% and security enhancement (strength of obfuscation) of $>\,\,4.29\,\,{e+9}$ times.

The Transmit-Energy vs Computation-Delay Trade-Off in Gateway-Selection for Heterogenous Cloud Aided Multi-UAV Systems

Unmanned aerial vehicles (UAVs) have been widely used in a range of compelling applications. In this paper, we integrate both the networking techniques and the cloud computing tasks of multi-UAV systems. We commence by proposing an energy efficient scheme for selecting the gateway of UAVs invoked for relaying data to the heterogenous cloud. Then, relying on queuing theory and Lyapunov optimization, we strike a power-delay trade-off by jointly optimizing the computational task scheduling and resource allocation in the heterogeneous cloud architecture, which is comprised of an edge cloud and a powerful remote cloud. We analyze the optimal resource-allocation strategy for each time slot and an iterative algorithm is conceived for reducing the computational complexity. Finally, our numerical results demonstrate the superiority of the proposed scheme.

Effective Capacity and Power Allocation for Machine-Type Communication

Effective capacity (EC) determines the maximum communication rate subject to a particular delay constraint. In this paper, we analyze the EC of ultra reliable machine type communication networks operating in the finite blocklength regime. First, we present a closed form approximation for EC in quasi-static Rayleigh fading channels. Our analysis determines the upper bounds for EC and delay constraint when varying transmission power. Finally, we characterize the power-delay tradeoff for fixed EC and propose an optimum power allocation scheme which exploits the asymptotic behavior of EC in the high SNR regime. The results illustrate that the proposed scheme provides significant power saving with a negligible loss in EC.

Joint Queue-Aware and Channel-Aware Delay Optimal Scheduling of Arbitrarily Bursty Traffic Over Multi-State Time-Varying Channels

This paper is motivated by the observation that the average queueing delay can be decreased by sacrificing power efficiency in wireless communications. In this sense, we naturally wonder what is the minimum queueing delay when the available power is limited and how to achieve the minimum queueing delay. To answer these two questions in the scenario where randomly arriving packets are transmitted over multi-state wireless fading channel, a probabilistic cross-layer scheduling policy is proposed in this paper, and characterized by a constrained Markov Decision Process (MDP). Using the steady-state probability of the underlying Markov chain, we are able to derive the mathematical expressions of the concerned metrics, namely, the average queueing delay and the average power consumption. To describe the delay-power tradeoff, we formulate a non-linear programming problem, which, however, is very challenging to solve. By analyzing its structure, this optimization problem can be converted into an equivalent Linear Programming (LP) problem via variable substitution, which allows us to derive the optimal delay-power tradeoff as well as the optimal scheduling policy. The optimal scheduling policy turns out to be dual-threshold-based, which means transmission decisions should be made based on the optimal thresholds imposed on the queue length and the channel state.

Power Efficiency and Delay Tradeoff of 10GBase-T Energy Efficient Ethernet Protocol

In this paper, we study the power efficiency and delay performance of the burst mode transmission (BTR) strategy for the IEEE 802.3az energy efficient Ethernet (EEE) protocol. In the BTR strategy, the Ethernet interface goes to sleep once its transmission buffer becomes empty and wakes up as soon as the first arrival has waited for time $\tau $ or the $N$ -th frame arrives at the interface. Based on the number of arrivals during the vacation time, a new approach is proposed to analyze the M/G/1 queue with vacation times that are governed by the arrival process and the $\tau $ and $N$ parameters of BTR strategy. Our key idea is to establish the connection between the vacation time and the arrival process to account for their dependency. We first derive the distribution of the number of arrivals during a vacation time based on an event tree of the BTR strategy, from which, we obtain the mean vacation time and the power efficiency. Next, from the condition on the number of arrivals at the end of a vacation period, we derive a generalized P-K formula of the mean delay for EEE systems, and prove that the classical P-K formula of the vacation model is only a special case when the vacation time is independent of the arrival process. Our analysis demonstrates that the $\tau $ policy and $N$ policy of the BTR strategy are compensating each other. The $\tau $ policy ensures the frame delay is bounded when the traffic load is light, while the $N$ policy ensures the queue length at the end of vacation times is bounded when the traffic load is heavy. These results, in turn, provide the rules to select appropriate $\tau $ and $N$ . Our analytical results are confirmed by simulations.