Long term evolution (LTE) carrier aggregation with 5 GHz unlicensed national informational infrastructure band has been pointed out by the industry as a good solution to handle the rapidly increasing amounts of data traffic. To provide fair coexistence of LTE-licensed assisted access (LTE-LAA) and Wi-Fi on 5 GHz, several coexistence mechanisms have already been proposed. This paper provides a comprehensive survey of the coexistence of LTE-LAA and Wi-Fi on 5 GHz with corresponding deployment scenarios. We first analyze coexistence-related features of those two technologies, including motivation, LTE carrier aggregation with unlicensed band, LTE and Wi-Fi medium access control protocols comparison, coexistence challenges and enablers, performance difference between LTE-LAA and Wi-Fi, as well as co-channel interference. Second, we further extensively discuss current considerations about the coexistence of LTE-LAA and Wi-Fi. Third, influential factors for the classification of small cell scenarios, as well as four representative scenarios are investigated in detail. Then we explore a relatively smooth technical route for solving coexistence-related problems, which practically takes features of a specific scenario as the base for designing deployment mode of LTE-LAA and/or Wi-Fi. A scenario-oriented decision making procedure for the coexistence issue and the analysis on an example deployment scenario, including design and performance evaluation metrics focusing on the concept of the scenario-oriented coexistence are presented. We finally forecast further research trends on the basis of our conclusion.

Coexistence of LTE-LAA and Wi-Fi on 5 GHz With Corresponding Deployment Scenarios: A Survey

Critical infrastructure systems are vital to underpin the functioning of a society and economy. Due to the ever-increasing number of Internet-connected Internet-of-Things (IoT)/Industrial IoT (IIoT), and the high volume of data generated and collected, security and scalability are becoming burning concerns for critical infrastructures in industry 4.0. The blockchain technology is essentially a distributed and secure ledger that records all the transactions into a hierarchically expanding chain of blocks. Edge computing brings the cloud capabilities closer to the computation tasks. The convergence of blockchain and edge computing paradigms can overcome the existing security and scalability issues. In this article, we first introduce the IoT/IIoT critical infrastructure in industry 4.0, and then we briefly present the blockchain and edge computing paradigms. After that, we show how the convergence of these two paradigms can enable secure and scalable critical infrastructures. Then, we provide a survey on the state of the art for security and privacy and scalability of IoT/IIoT critical infrastructures. A list of potential research challenges and open issues in this area is also provided, which can be used as useful resources to guide future research.

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Convergence of Blockchain and Edge Computing for Secure and Scalable IIoT Critical Infrastructures in Industry 4.0

Vehicular edge computing (VEC) is a new computing paradigm that has great potential to enhance the capability of vehicle terminals (VTs) to support resource-hungry in-vehicle applications with low latency and high energy efficiency. In this article, we investigate an important computation offloading scheduling problem in a typical VEC scenario, where a VT traveling along an expressway intends to schedule its tasks waiting in the queue to minimize the long-term cost in terms of a tradeoff between task latency and energy consumption. Due to diverse task characteristics, dynamic wireless environment, and frequent handover events caused by vehicle movements, an optimal solution should take into account both where to schedule (i.e., local computation or offloading) and when to schedule (i.e., the order and time for execution) each task. To solve such a complicated stochastic optimization problem, we model it by a carefully designed Markov decision process (MDP) and resort to deep reinforcement learning (DRL) to deal with the enormous state space. Our DRL implementation is designed based on the state-of-the-art proximal policy optimization (PPO) algorithm. A parameter-shared network architecture combined with a convolutional neural network (CNN) is utilized to approximate both policy and value function, which can effectively extract representative features. A series of adjustments to the state and reward representations are taken to further improve the training efficiency. Extensive simulation experiments and comprehensive comparisons with six known baseline algorithms and their heuristic combinations clearly demonstrate the advantages of the proposed DRL-based offloading scheduling method.

Deep-Reinforcement-Learning-Based Offloading Scheduling for Vehicular Edge Computing

A survey on computation offloading modeling for edge computing

6G is envisioned to empower wireless communication and computation through the digitalization and connectivity of everything, by establishing a digital representation of the real network environment. Mobile edge computing (MEC), as one of the key enabling factors, meets unprecedented challenges during mobile offloading due to the extremely complicated and unpredictable network environment in 6G. The existing works on offloading in MEC mainly ignore the effects of user mobility and the unpredictable MEC environment. In this paper, we present a new vision of Digital Twin Edge Networks (DITEN) where digital twins (DTs) of edge servers estimate edge servers’ states and DT of the entire MEC system provides training data for offloading decision. A mobile offloading scheme is proposed in DITEN to minimize the offloading latency under the constraints of accumulated consumed service migration cost during user mobility. The Lyapunov optimization method is leveraged to simplify the long-term migration cost constraint to a multi-objective dynamic optimization problem, which is then solved by $Actor$ - $Critic$ deep reinforcement learning. Simulations results show that our proposed scheme effectively diminishes the average offloading latency, the offloading failure rate, and the service migration rate, as compared with benchmark schemes, while saving the system cost with DT assistance.

Reducing Offloading Latency for Digital Twin Edge Networks in 6G

Recently, there has been an increasing interest in operating long-term evolution (LTE) in unlicensed bands (i.e., LTE-U). However, since LTE and wireless local area networks (WLANs) are designed to operate in different bands, they have no coexistence mechanism, which leads to significant performance degradation. In particular, since LTE does not sense channel vacancy prior to transmissions, the LTE interference severely affects the WLAN operation. To address this problem, we propose a fair listen-before-talk (F-LBT) algorithm for coexistence of LTE-U and WLAN in unlicensed bands. F-LBT jointly considers the total system throughput and the fairness between LTE-U and WLAN and then allocates an appropriate idle period for WLAN. Evaluation results demonstrate that F-LBT can improve the total system throughput while providing the fairness between LTE-U and WLAN.

A Fair Listen-Before-Talk Algorithm for Coexistence of LTE-U and WLAN

A novel concept of the edge cloud has recently been introduced to reduce transmission costs in mobile cloud computing services. Heterogeneous networks with diverse radio access networks will be pervasive in the future. In this paper, we propose a spatial and temporal computation offloading decision algorithm (ST-CODA) in edge cloud-enabled heterogeneous networks. In ST-CODA, a mobile device decides where and when to process tasks by means of a Markov decision process with the consideration of the processing time and energy consumption of different computation nodes and the transmission cost in heterogeneous networks. Extensive evaluation results are given to demonstrate the effectiveness of the ST-CODA in terms of the transmission cost, the energy efficiency of the mobile device, and the number of tasks that can be processed before their deadline.

Spatial and Temporal Computation Offloading Decision Algorithm in Edge Cloud-Enabled Heterogeneous Networks

Recently, the interest in operating long term evolution (LTE) in unlicensed bands (i.e., LTE-U) has increased. However, since LTE does not sense for channel vacancy prior to transmissions, whereas a wireless local area network (WLAN) is based on carrier sensing multiple access, LTE interference severely degrades the performance of WLAN. To maximize the LTE throughput while maintaining fairness between LTE and WLAN, we formulate a joint optimization problem of channel selection and frame scheduling in LTE. As a practical solution of this problem, we propose a low-complexity heuristic algorithm called the sequential operation algorithm (SOA). Evaluation results demonstrate that SOA can achieve an LTE throughput comparable to that of the optimal solution, while maintaining fairness between LTE and WLAN by selecting appropriate channels and conducting effective frame scheduling.

Joint Optimization of Channel Selection and Frame Scheduling for Coexistence of LTE and WLAN

CG-E2S2: Consistency-guaranteed and energy-efficient sleep scheduling algorithm with data aggregation for IoT

In the delayed WiFi offloading, since data are downloaded only through WiFi networks before a predefined delay timer expires, the performance of the delayed WiFi offloading is affected by the delay timer value. To assess the performance of the delayed WiFi offloading, we develop analytical models for the expected saving monetary cost and download completion time. Based on these analytical models, the optimal delay timer is derived to maximally save the monetary cost while maintaining the outage probability on the download time at low level. Evaluation results demonstrate the effects of the average cellular-only residence time and WiFi data rate, and show that better performance can be achieved when the delay timer is set to the optimal value.

Jaewook Lee

Papers

A Fair Listen-Before-Talk Algorithm for Coexistence of LTE-U and WLAN

Spatial and Temporal Computation Offloading Decision Algorithm in Edge Cloud-Enabled Heterogeneous Networks

Joint Optimization of Channel Selection and Frame Scheduling for Coexistence of LTE and WLAN

CG-E2S2: Consistency-guaranteed and energy-efficient sleep scheduling algorithm with data aggregation for IoT

Performance Optimization of Delayed WiFi Offloading in Heterogeneous Networks