Time-sharing Strategy Research Articles

Data Sensing and Offloading in Edge Computing Networks: TDMA or NOMA?

With the development of Internet-of-Things (IoT), we witness the explosive growth in the number of devices with sensing, computing, and communication capabilities, along with a large amount of raw data generated at the network edge. Mobile (multi-access) edge computing (MEC), acquiring and processing data at network edge (like base station (BS)) via wireless links, has emerged as a promising technique for real-time applications. In this paper, we consider the scenario that multiple devices sense then offload data to an edge server/BS, and the offloading throughput maximization problems are studied by joint radio-and-computation resource allocation, based on time-division multiple access (TDMA) and non-orthogonal multiple access (NOMA) multiuser computation offloading. Particularly, we take the sequence of TDMA-based multiuser transmission/offloading into account. The studied problems are NP-hard and non-convex. A set of low-complexity algorithms are designed based on decomposition approach and exploration of valuable insights of problems. They are either optimal or can achieve close-to-optimal performance as shown by simulation. The comprehensive simulation results show that the sequence-optimized TDMA scheme achieves better throughput performance than the NOMA scheme, while the NOMA scheme is better under the assumptions of time-sharing strategy and the identical sensing capability of the devices.

Hierarchical Optimization Scheduling of Active Demand Response for Distribution Networks in 5G Base Stations

The study aims to solve the problem that the traditional scheduling optimization model does not apply to the multimicrogrid systems in the 5th generation mobile networks (5G). First, the response characteristics of the 5G base station energy storage demand are analyzed. Second, a microgrid hybrid power supply system is proposed. Third, a multimicrogrid demand response hierarchical optimal scheduling model based on the game is constructed, and the game interaction mechanism between various stakeholders in the demand response is studied. Finally, the strategy of each microgrid in the IEEE-9 bus system in the game bidding is simulated by a genetic algorithm (GA), and the simulation experiment is carried out by Matlab. The results show that (1) after the optimization of the multimicrogrid system, the overall load of the microgrid is reduced, the independent energy optimization is carried out according to the energy access of each microgrid, and renewable energy is fully used. (2) Based on the game-based time-sharing bidding strategy, the system gives priority to the low-cost microgrid for power supply and saves the cost. (3) The load transfer capacity of the three microgrids obtained by the model are 132.7 kW, 47.1 kW, and 46.4 kW, which is within the actual load range of each microgrid; their electricity sales are 665.8 kWh, 2053.3 kWh, and 1668.2 kWh; and their income is 132.45 CNY, 411.72 CNY, and 354.63 CNY, respectively. This shows that the game equilibrium index makes the power sales of each microgrid proportional to its income, which ensures the fair and effective scheduling in the bidding process. The study provides technical support for the construction of a 5G base station and the active demand response scheduling optimization of distribution networks.

Driver distraction and its effects on partially automated driving performance: A driving simulator study among young-experienced drivers

Drivers of partially automated vehicles (PAVs) are relieved from parts of the driving tasks allocated to the automated driver. Ironically, these drivers are obligated to continuously monitor the driving task at all times and keep their attention on the roadway. This reduction in the driving task's demands and cognitive workload may encourage drivers to engage with non-driving related tasks (NDRT), which may impair drivers' awareness of the road environment and, as a result, compromise safety. This study examined how engagement with a visual-manual NDRT affects the driving performance of PAV drivers. Thirty-seven participants were randomly assigned to one of two experimental conditions in a driving simulator. Each consisted of two simulated drives. The first experimental condition included one drive under manual driving conditions and another under partially automated driving conditions (i.e., L2). Both drives had no NDRT involved. The second experimental condition included one drive under L2 without an NDRT and one drive under L2, including engagement with an NDRT. Participants' eye movements and heart rate were recorded throughout the experiment. Across various measures, the findings showed that under L2 driving conditions, engagement with an NDRT impairs driving performance in two primary aspects: (1) drivers were less aware of road hazards, and (2) their mental workload was higher when they engaged with an NDRT. In addition, the findings reveal that for drivers engaged with an NDRT, the attentional time-sharing strategy between the NDRT and the roadway monitoring task affected the probability of identifying a hazard. This study shows the adverse effects of engagement with an NDRT under L2 driving conditions on driving performance. Future studies should examine different interventions to mitigate these effects, assuring that drivers are constantly aware of the roadway environment.

Study on the Moving Target Tracking Based on Vision DSP.

The embedded visual tracking system has higher requirements for real-time performance and system resources, and this is a challenge for visual tracking systems with available hardware resources. The major focus of this study is evaluating the results of hardware optimization methods. These optimization techniques provide efficient utilization based on limited hardware resources. This paper also uses a pragmatic approach to investigate the real-time performance effect by implementing and optimizing a kernel correlation filter (KCF) tracking algorithm based on a vision digital signal processor (vision DSP). We examine and analyze the impact factors of the tracking system, which include DP (data parallelism), IP (instruction parallelism), and the characteristics of parallel processing of the DSP core and iDMA (integrated direct memory access). Moreover, we utilize a time-sharing strategy to increase the system runtime speed. These research results are also applicable to other machine vision algorithms. In addition, we introduced a scale filter to overcome the disadvantages of KCF for scale transformation. The experimental results demonstrate that the use of system resources and real-time tracking speed also satisfies the expected requirements, and the tracking algorithm with a scale filter can realize almost the same accuracy as the DSST (discriminative scale space tracking) algorithm under a vision DSP environment.

Research and implementation of multi-object tracking based on vision DSP

This paper applies a pragmatic approach to study the real-time performance effect of software design methods for multiple object tracking (MOT) based on vision digital signal processing (vision DSP). The MOT system in the paper combines target detection, the Hungarian algorithm and the Kernel correlation filter (KCF) tracker. In addition, the MOT system needs to support multiway video streams, so higher speed and storage requirements are necessary for target tracking. Therefore, we carried out some studies on how to improve tracker speed performance and reduce system resource consumption under limited system resources. In the paper, we achieved the goal in two respects. Regarding the data processing, we studied how to efficiently process tracking data by utilizing the parallel characteristics of iDMA (integrated direct memory access) and a DSP core; and regarding the data storage, we proposed a time-sharing strategy to solve the DSP local memory (data RAM) usage issue for multiple tracking objects. In addition, regarding the software design, we propose a new strategy, which includes two levels of parallel computations: the frame-level parallel computations and the tracking object-level parallel computations. The experimental results show that the KCF tracking algorithm based on vision DSP achieves not only the desired real-time tracking speed but also the expected goal of system resource utilization. Our research methods also provide a reference for algorithm embedded applications in the field of computer vision.

Wireless energy consumption optimisation using node coverage controlling in linear-type network

With the mushrooming development of automatic, intelligent and unmanned technologies, the application of wireless sensor network has become a hot topic. There is a kind of narrow-band structure, such as corridor or tunnel, which needs to use wireless sensor network for parameter sensing. To address unbalanced energy consumption of wireless nodes along length direction, this paper proposes a coverage controlling strategy, and linear-type network is built using several wireless nodes. A base station node is equipped in narrow band structures. Firstly, the wireless perception models are analysed aiming at special monitoring environments, which contain routing path, coverage model, link load and data credible rate; secondly, the survival lifetime of linear-type network is solved based on energy consumption of every wireless node; thirdly, considering the accidental death of deployed nodes, time-sharing scheduling strategy is designed to guarantee the stability of monitoring linear-type network; finally, the experimental results show that the proposed coverage control strategy can optimise the survival lifetime and the energy efficiency of linear-type network, which can provide the reference for node coverage in linear structures.

Smart Grid Network Resource Scheduling Algorithm Based on Network Calculus

Smart grid technology is further developed on the basis of distributed power grids and micro-grids. The communications network is an indispensable part of the smart grid. How to achieve real-time accurate transmission of various types of smart information is a technical guarantee for smart grids. This paper analyze data of smart grid bases on the demand of net resource, and adopts the idea of priority scheduling to draw on the advantages of integrated services and differentiated services to design a time-sharing polling scheduling strategy. In this strategy, the algorithm classify the network data, Using network calculus theory to analyze the optimal length of the queue satisfying the upper bound of delay, According to the degree of network congestion perception, the credibility of the data is calculated, and non-adaptive flows are quickly identified. The strategy ensure that the data is able to transfer to the user quickly and lively depend on its needs, So ensure the real-time transmission of energy supply & consumption information of active distribution network, realize multi-source collaborative optimization scheduling and distributed hierarchical intelligent control management. The algorithm can effectively ensure the demand of high-priority services, making the overall performance of the smart grid communication system data such as real-time and accuracy optimized.

Joint Resource Optimization in Simultaneous Wireless Information and Power Transfer (SWIPT) Enabled Multi-Relay Internet of Things (IoT) System.

The internet of things (IoT) is becoming more indispensable in modern society as the further development and maturity of information technology progresses. However the exponential growth of IoT devices leads to severe energy consumption. As a technology with broad application prospects, simultaneous wireless information and power transfer (SWIPT) enables IoT devices to harvest energy from receiving radio frequency (RF) signals while ensuring information transmission. In this paper, we investigate the transmission rate optimization problem for a dual-hop multi-relay IoT system, where a decode-and-forward (DF) relay supports the SWIPT technique. We jointly optimize the resource including power and subcarrier allocation, to maximize the system transmission rate. The time-sharing strategy and Lagrange dual method are used to solve this optimization problem. Simulation results reveal that the proposed algorithm has a larger transmission rate than other benchmark algorithms when ensuring each relay has no additional energy supply. Specifically, the proposed algorithm improves the information transmission rate by 2.8%, 3.4% and 43% compared with other algorithms in the case of five relays when the source’s power is equal to 0.5 W, respectively.

Unconstrained Capacities of Quantum Key Distribution and Entanglement Distillation for Pure-Loss Bosonic Broadcast Channels.

We consider quantum key distribution (QKD) and entanglement distribution using a single-sender multiple-receiver pure-loss bosonic broadcast channel. We determine the unconstrained capacity region for the distillation of bipartite entanglement and secret key between the sender and each receiver, whenever they are allowed arbitrary public classical communication. A practical implication of our result is that the capacity region demonstrated drastically improves upon rates achievable using a naive time-sharing strategy, which has been employed in previously demonstrated network QKD systems. We show a simple example of a broadcast QKD protocol overcoming the limit of the point-to-point strategy. Our result is thus an important step toward opening a new framework of network channel-based quantum communication technology.

직병렬 충전 구조를 사용한 전기자동차 배터리 균형 충전

In this study, we describe a battery balancing charge technology using the combined method of serial charging and selective supplementary charging to resolve the unbalanced charge problem common among battery cells for electric vehicles. In this method, the major charging is performed using serial charging while the balancing is carried out with supplementary charging. The serial component is large and heavy while the supplementary component is smaller and lighter. As an upgrade to the combined balancing, we propose a new method in which the supplementary component is replaced with a small charger which charges poorly-charged cells selectively via a time-sharing strategy. The core parts of the proposed system include a sensor array to detect the individual battery cell voltage, a duty rate control incorporating IGBTs , and a battery management system. This thesis describes experimental results which show the expected functions and performance of the proposed cell balancing architecture.

Lossless Coding of Correlated Sources With Actions

This paper studies the problem of the distributed compression of correlated sources with an action-dependent joint distribution. This class of problems is, in fact, an extension of the Slepian–Wolf model, but where cost-constrained actions taken by the encoder or the decoder affect the generation of one of the sources. The purpose of this paper is to study the impact of actions on the achievable rates. In particular, two cases where transmission occurs over a rate-limited link are studied; case A for actions taken at the decoder and case B where actions are taken at the encoder. A complete single-letter characterization of the set of achievable rates is given in both cases. Furthermore, a network coding setup for the case where actions are taken at the encoder is investigated. The sources are generated at different nodes of the network and are required at a set of terminal nodes, yet transmission occurs over a general, acyclic, directed network. For this setup, generalized cut-set bounds are derived, and a full characterization of the set of achievable rates using single-letter expressions is provided. For this scenario, random linear network coding is proved to be optimal, even though this is not a classical multicast problem. In addition, two binary examples are investigated and demonstrate how actions taken at different nodes of the system have a significant effect on the achievable rate region, when compared with a naive time-sharing strategy.

Capacities of quantum amplifier channels

Quantum amplifier channels are at the core of several physical processes. Not only do they model the optical process of spontaneous parametric down-conversion, but the transformation corresponding to an amplifier channel also describes the physics of the dynamical Casimir effect in superconducting circuits, the Unruh effect, and Hawking radiation. Here we study the communication capabilities of quantum amplifier channels. Invoking a recently established minimum output-entropy theorem for single-mode phase-insensitive Gaussian channels, we determine capacities of quantum-limited amplifier channels in three different scenarios. First, we establish the capacities of quantum-limited amplifier channels for one of the most general communication tasks, characterized by the trade-off between classical communication, quantum communication, and entanglement generation or consumption. Second, we establish capacities of quantum-limited amplifier channels for the trade-off between public classical communication, private classical communication, and secret key generation. Third, we determine the capacity region for a broadcast channel induced by the quantum-limited amplifier channel, and we also show that a fully quantum strategy outperforms those achieved by classical coherent detection strategies. In all three scenarios, we find that the capacities significantly outperform communication rates achieved with a naive time-sharing strategy.

Wireless-Powered Communications With Non-Orthogonal Multiple Access

We study a wireless-powered uplink communication system with non-orthogonal multiple access (NOMA), consisting of one base station and multiple energy harvesting users. More specifically, we focus on the individual data rate optimization and fairness improvement and we show that the formulated problems can be optimally and efficiently solved by either linear programming or convex optimization. In the provided analysis, two types of decoding order strategies are considered, namely fixed decoding order and time- sharing. Furthermore, we propose an efficient greedy algorithm, which is suitable for the practical implementation of the time-sharing strategy. Simulation results illustrate that the proposed scheme outperforms the baseline orthogonal multiple access scheme. More specifically, it is shown that NOMA offers a considerable improvement in throughput, fairness, and energy efficiency. Also, the dependence among system throughput, minimum individual data rate, and harvested energy is revealed, as well as an interesting trade-off between rates and energy efficiency. Finally, the convergence speed of the proposed greedy algorithm is evaluated, and it is shown that the required number of iterations is linear with respect to the number of users.

Cooperative Quantization for Two-UserInterference Channels

We introduce cooperative quantizers for two-user interference channels where interference signals are treated as noise. Compared with the conventional quantizers where each receiver quantizes its own channel independently, the proposed cooperative quantizers allow multiple rounds of feedback communication in the form of conferencing between receivers. For both time-sharing and concurrent transmission strategies, we propose different cooperative quantizers to achieve the full-channel-state-information (full-CSI) network outage probability of sum rate and the full-CSI network outage probability of minimum rate, respectively. Our proposed quantizers only require finite average feedback rates, whereas the conventional quantizers require infinite rate to achieve the full-CSI performance. For the minimum rate, we also design cooperative quantizers for a joint time-sharing and concurrent transmission strategy that can approach the previously established optimal network outage probability with a negligible gap. Numerical simulations confirm that our cooperative quantizers based on conferencing outperform the conventional quantizers.

Approximate Sum-Capacity of the Y-Channel

A network where three users want to establish multiple unicasts between each other via a relay is considered. This network is called the Y-channel and resembles an elemental ingredient of future wireless networks. The sum-capacity of this network is studied. A characterization of the sum-capacity within an additive gap of 2 bits, and a multiplicative gap of 4, for all values of channel gains and transmit powers is obtained. Contrary to similar setups where the cut-set bounds can be achieved within a constant gap, they cannot be achieved in our case, where they are dominated by our new genie-aided bounds. Furthermore, it is shown that a time-sharing strategy, in which at each time two users exchange information using coding strategies of the bidirectional relay channel, achieves the upper bounds to within a constant gap. This result is further extended to the <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex Notation="TeX">${\rm K}$</tex></formula> -user case, where it is shown that the same scheme achieves the sum-capacity within <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$2\log (K-1)$</tex></formula> bits.

Multiple-Access Channel With Partial and Controlled Cribbing Encoders

In this paper, we consider a multiple-access channel (MAC) with partial cribbing encoders. This means that each of the two encoders obtains a deterministic function of the output of the other encoder with or without delay. The partial cribbing scheme is especially motivated by the additive noise Gaussian MAC, where perfect cribbing results in the degenerated case of full cooperation between the encoders and requires an infinite entropy link. We derive a single-letter characterization of the capacity of the MAC with partial cribbing for the cases of causal and strictly causal cribbing. Several numerical examples, such as those of quantized cribbing, are presented. We further consider and derive the capacity region where the cribbing depends on actions that are functions of the previous cribbed observations. In particular, we consider a scenario where the action is taken to decide “to crib or not to crib” and show that a naive time-sharing strategy is not optimal.

A New Approach to Generate Flexible Multiperiod Heat Exchanger Network Designs with Timesharing Mechanisms

Seasonal changes in demands, supplies, and also in the operating conditions of a chemical process may call for different system structures to optimize the performance of its heat exchanger network (HEN). To produce a multiperiod HEN design, the traditional approach is to solve a single mathematical program that minimizes the total annual cost (TAC). This objective function, that is, TAC, is usually the sum of the annualized capital costs and annual utility costs determined according to given durations of all periods in a year. As a result, the conventional designs are often suboptimal, since the period lengths may have to be adjusted in response to the unexpected disturbances during actual operations. A new design approach is taken in the present study to circumvent the aforementioned drawback. In particular, a single-period model is first constructed and solved to produce the optimal design for each period individually. A timesharing strategy is then applied to integrate all such single-period designs so as to reduce the overall capital investment as much as possible while still keeping the utility consumption rates in every period at the minimum levels. In addition to their economic benefits, the new designs should be considered to be more flexible, since they are optimal despite unforeseen changes in the operation schedule. Finally, the numerical results of extensive case studies are also reported in this paper to demonstrate the effectiveness of the proposed approach.

Quantum trade-off coding for bosonic communication

The trade-off capacity region of a quantum channel characterizes the optimal net rates at which a sender can communicate classical, quantum, and entangled bits to a receiver by exploiting many independent uses of the channel, along with the help of the same resources. Similarly, one can consider a trade-off capacity region when the noiseless resources are public, private, and secret key bits. In [Phys. Rev. Lett. 108, 140501 (2012)], we identified these trade-off rate regions for the pure-loss bosonic channel and proved that they are optimal provided that a longstanding minimum output entropy conjecture is true. Additionally, we showed that the performance gains of a trade-off coding strategy when compared to a time-sharing strategy can be quite significant. In the present paper, we provide detailed derivations of the results announced there, and we extend the application of these ideas to thermalizing and amplifying bosonic channels. We also derive a "rule of thumb" for trade-off coding, which determines how to allocate photons in a coding strategy if a large mean photon number is available at the channel input. Our results on the amplifying bosonic channel also apply to the "Unruh channel" considered in the context of relativistic quantum information theory.

Trade-off coding for universal qudit cloners motivated by the Unruh effect

A ‘triple trade-off’ capacity region of a noisy quantum channel provides a more complete description of its capabilities than does a single capacity formula. However, few full descriptions of a channel’s ability have been given due to the difficult nature of the calculation of such regions—it may demand an optimization of information-theoretic quantities over an infinite number of channel uses. This work analyses the d-dimensional Unruh channel, a noisy quantum channel which emerges in relativistic quantum information theory. We show that this channel belongs to the class of quantum channels whose capacity region requires an optimization over a single channel use, and as such is tractable. We determine two triple-trade off regions, the quantum dynamic capacity region and the private dynamic capacity region, of the d-dimensional Unruh channel. Our results show that the set of achievable rate triples using this coding strategy is larger than the set achieved using a time-sharing strategy. Furthermore, we prove that the Unruh channel has a distinct structure made up of universal qudit cloning channels, thus providing a clear relationship between this relativistic channel and the process of stimulated emission present in quantum optical amplifiers.

MIMO Downlink Scheduling with Non-Perfect Channel State Knowledge

Downlink scheduling schemes are well-known and widely investigated under the assumption that the channel state is perfectly known to the scheduler. In the multiuser MIMO (broadcast) case, downlink scheduling in the presence of non-perfect channel state information (CSI) is only scantly treated. In this paper we provide a general framework that addresses the problem systematically. Also, we illuminate the key role played by the channel state prediction error: our scheme treats in a fundamentally different way users with small channel prediction error (predictable users) and users with large channel prediction error (non-predictable users), and can be interpreted as a near-optimal opportunistic time-sharing strategy between MIMO downlink beamforming to predictable users and space-time coding to non-predictable users. Our results, based on a realistic MIMO channel model used in 3GPP standardization, show that the proposed algorithms can significantly outperform a conventional mismatched scheduling scheme that treats the available CSI as if it was perfect.