In this article, we propose a space spreading-assisted framework that leverages either {time or frequency diversity or both to reduce interference and signal loss} owing to channel impairments and facilitate the efficient operation of large-scale dense Internet-of-Things (IoT). Our approach employs dispersion of data-streams transmitted from individual IoT devices over indexed space-time (ST), space-frequency (SF) or space-time-frequency (STF) blocks. As a result, no two devices transmit on the same block; only one is activated while the rest of the devices in the network is silent, thereby minimizing possibility of interference on the transmit side. On the receive side, multiple-antenna array ameliorates performance in presence of channel impairments while exploiting array-processing gain. As interference due to superposition of multiple data-streams is killed at its root, no extra energy is wasted in fighting interference and other impairments, thereby enabling energy-efficient transmission from multiple devices over multiple access channel (MAC). To validate the proposed concept, we simulate the performance of the framework against dense IoT networks deployed in generalized indoor and outdoor scenarios in terms of probability of signal outage. Results demonstrate that our conceptualized framework benefits from interference-free transmission as well as enhancement in overall system performance. 

Space-Time- and Frequency- Spreading for Interference Minimization in Dense IoT

Compared to the generations up to 4G, whose main focus was on broadband and coverage aspects, 5G has expanded the scope of wireless cellular systems towards embracing two new types of connectivity: massive machine-type communication (mMTC) and ultra-reliable low-latency communications (URLLC). This paper will discuss the possible evolution of these two types of connectivity within the umbrella of 6G wireless systems. The paper consists of three parts. The first part deals with the connectivity for a massive number of devices. While mMTC research in 5G was predominantly focused on the problem of uncoordinated access in the uplink for a large number of devices, the traffic patterns in 6G may become more symmetric, leading to closed-loop massive connectivity. One of the drivers for this is distributed learning/inference. The second part of the paper will discuss the evolution of wireless connectivity for critical services. While latency and reliability are tightly coupled in 5G, 6G will support a variety of safety critical control applications with different types of timing requirements, as evidenced by the emergence of metrics related to information freshness and information value. Additionally, ensuring ultra-high reliability for safety critical control applications requires modeling and estimation of the tail statistics of the wireless channel, queue length, and delay. The fulfillment of these stringent requirements calls for the development of novel AI-based techniques, incorporating optimization theory, explainable AI, generative AI and digital twins. The third part will analyze the coexistence of massive connectivity and critical services. We will consider scenarios in which a massive number of devices need to support traffic patterns of mixed criticality. This will be followed by a discussion about the management of wireless resources shared by services with different criticality.

Wireless 6G Connectivity for Massive Number of Devices and Critical Services

Performance Attribution for Portfolio Constraints

In this article, we propose a space spreading-assisted framework that leverages either {time or frequency diversity or both to reduce interference and signal loss} owing to channel impairments and facilitate the efficient operation of large-scale dense Internet-of-Things (IoT). Our approach employs dispersion of data-streams transmitted from individual IoT devices over indexed space-time (ST), space-frequency (SF) or space-time-frequency (STF) blocks. As a result, no two devices transmit on the same block; only one is activated while the rest of the devices in the network is silent, thereby minimizing possibility of interference on the transmit side. On the receive side, multiple-antenna array ameliorates performance in presence of channel impairments while exploiting array-processing gain. As interference due to superposition of multiple data-streams is killed at its root, no extra energy is wasted in fighting interference and other impairments, thereby enabling energy-efficient transmission from multiple devices over multiple access channel (MAC). To validate the proposed concept, we simulate the performance of the framework against dense IoT networks deployed in generalized indoor and outdoor scenarios in terms of probability of signal outage. Results demonstrate that our conceptualized framework benefits from interference-free transmission as well as enhancement in overall system performance.

Ultra-reliable low latency communication (URLLC) is an innovative service offered by fifth-generation (5G) wireless systems. URLLC enables various mission-critical applications by facilitating reliable and low-latency signal transmission to support extreme Quality of Service (QoS) requirements. Apart from reliability and latency, ensuring secure data transmission for URLLC has been a prominent issue for researchers in recent years. Using finite blocklength signals to achieve the stringent reliability and latency criteria in URLLC eliminates the possibility of using conventional complex cryptographic security enhancement techniques based on encoding and decoding of secret keys. Thus, the development of lightweight security mechanisms is of paramount importance for URLLC. Recently, Physical-Layer Security (PLS) techniques have emerged as a powerful alternative to the complex cryptography-based security approaches for facilitating secure URLLC by exploiting the randomness of the wireless channel. Therefore, in this survey, we present a comprehensive and in-depth review of the state-of-the-art PLS enhancements utilized to unleash secure URLLC while analyzing the impact of various system design parameters on its performance. Moreover, the survey incorporates a detailed overview of the recent advancements in ensuring secure URLLC using PLS in various mission-critical applications, and 5G URLLC enabling technologies like non-orthogonal multiple access (NOMA), multi-antenna systems, cooperative communication using unmanned aerial vehicles (UAV), and intelligent reflective surfaces (IRS). Apart from this, we briefly discuss the role of advanced Machine Learning (ML) techniques in designing robust and intelligent PLS schemes for URLLC service.

A Survey on Security of Ultra/Hyper Reliable Low Latency Communication: Recent Advancements, Challenges, and Future Directions

Optimal Impact Portfolios with General Dependence and Marginals

Most researches on the Internet of Vehicles (IoV) in the 5G era are focused on the low latency and the massive transmitted data. However, the 5G base station has relatively a small coverage and a high cost, which is the main reason to hinder the development of the 5G applications on the IoV. In this paper, we try to put forward some feasible solutions for this problem. We will use two physical layer communication techniques, the Coherent Beamforming (CB) technique and the Successive Interference Cancellation (SIC) technique, to jointly optimize the throughput of vehicles data transmission and the network infrastructure cost. We first establish the mathematical model based on CB and SIC technology and prove that it cannot be solved directly. Then, we design the Road and CB-nodes Assignment (RCA) algorithm and the Uninstalling Data Scheduling (UDS) algorithm to solve the problem. RCA is based on CB and SIC technology to reasonably arrange CB-nodes, so that CB-nodes set can send data from vehicles to the base station and the base station can receive multiple sets of data at the same time. UDS schedules vehicles based on various factors to optimize the total amount of data transferred by vehicles and improve throughput. In simulations, we conduct some comparative experiments, which are the not using Algorithm 3 scheme, the CB scheme, the SIC scheme, the common scheme and the Relay Task Offloading Scheme in Vehicular Edge Computing algorithm (RVEC Algorithm). Results show that our proposed method has the advantages of cost saving and throughput improvement.

Joint CB and SIC Technology to Optimize Throughput and Cost Under IoV

Finite blocklength is a favorable way to meet the extremely low-latency requirement, which leads to the classic Shannon's capacity formula no longer suitable. With the achievable rate region, optimization problems on three different scenarios are considered: (i) joint design of power and blocklength allocation for two transceiver pairs with single-antenna transmitter; (ii) beamforming and blocklength design for the two transceiver pairs with multi-antenna transmitter; (iii) beamforming and blocklength design for the multiple transceiver pairs with multi-antenna transmitter. Under the finite blocklength transmission mode, the achievable rate expression is a complicated function with respected to the blocklength and power allocation coefficients or beamforming vector, which makes the optimization problems difficult to solve. In this paper, by analyzing the monotonic and concaveconvex properties of achievable rate with interference channel (IFC), we formulate the achievable rate region as the convex hull of a series of power/beamforming and blocklength rate regions for two-user systems with IFC, as well as the Pareto boundary of achievable rate region on the blocklength and beamforming vector for the two-user systems, respectively. Sequentially, the optimal blocklength and power coefficients are derived in the closed-form and optimal beamforming vectors are also obtained by transforming the non-convex problem into convex one. Numerical results provide the perspective of looking at the rate region with finite blocklength.

Achievable Rate Region for uRLLC Interference Channel With Finite Blocklength Transmission

A powerful method for automated decision systems is Adversarial Imitation Learning (AIL). It is based on a generative adversarial framework that alternately optimizes a generator (learner) and a discriminator (reward function). In the popular mind, a high-accuracy discriminator results in informative rewards, thus AIL must balance the performance between a generator and a discriminator. However, we find that the AIL reward function is biased and also results in informative rewards. Thus, we theoretically analyze the bias in the AIL reward function and find that balancing the performance of a generator and a discriminator is not necessary when we recover an unbiased reward function. Further, we propose a mutual information based auxiliary reward function. Experiments on continuous control tasks indicate that MI-GAIL is able to address the bias problem of the AIL reward function and further improve sample efficiency and training stability compared with up-to-date algorithms. 

Learning Unbiased Rewards with Mutual Information in Adversarial Imitation Learning

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Optimal Impact Portfolios with General Dependence and Marginals

Joint CB and SIC Technology to Optimize Throughput and Cost Under IoV

Achievable Rate Region for uRLLC Interference Channel With Finite Blocklength Transmission

Learning Unbiased Rewards with Mutual Information in Adversarial Imitation Learning