The sixth generation (6G) wireless communication network presents itself as a promising technique that can be utilized to provide a fully data-driven network evaluating and optimizing the end-to-end behavior and big volumes of a real-time network within a data rate of Tb/s. In addition, 6G adopts an average of 1000+ massive number of connections per person in one decade (2030 virtually instantaneously). The data-driven network is a novel service paradigm that offers a new application for the future of 6G wireless communication and network architecture. It enables ultra-reliable and low latency communication (URLLC) enhancing information transmission up to around 1 Tb/s data rate while achieving a 0.1 millisecond transmission latency. The main limitation of this technique is the computational power available for distributing with big data and greatly designed artificial neural networks. The work carried out in this paper aims to highlight improvements to the multi-level architecture by enabling artificial intelligence (AI) in URLLC providing a new technique in designing wireless networks. This is done through the application of learning, predicting, and decision-making to manage the stream of individuals trained by big data. The secondary aim of this research paper is to improve a multi-level architecture. This enables user level for device intelligence, cell level for edge intelligence, and cloud intelligence for URLLC. The improvement mainly depends on using the training process in unsupervised learning by developing data-driven resource management. In addition, improving a multi-level architecture for URLLC through deep learning (DL) would facilitate the creation of a data-driven AI system, 6G networks for intelligent devices, and technologies based on an effective learning capability. These investigational problems are essential in addressing the requirements in the creation of future smart networks. Moreover, this work provides further ideas on several research gaps between DL and 6G that are up-to-date unknown.

We consider a simultaneous wireless transfer of information and power in a two-way relaying network, where a decode-and-forward protocol is employed for data exchange between two source devices at different rates. The data exchange is accomplished in two time phases (TPs) with possibly asymmetric TP ratios, where the relay is powered by the source devices in the first phase through simultaneous wireless information and power transfer (SWIPT) either by power splitting (PS) or by time switching (TS). For the network, resource allocation of the PS and TP ratios for the PS-based SWIPT and that of the TS and TP ratios for the TS-based SWIPT are studied to minimize the system outage probability. We first solve the joint optimization problem of each case with the instantaneous channel state information and then propose suboptimal schemes utilizing one or two values for the TP ratio to lower the implementation complexity. The results from analysis and simulation show that the proposed schemes outperform the benchmark scheme with equal resource allocation and that the gain gets more prominent as the rates of the data exchanged or the relay locations become more asymmetric.

Since the 1960s, joint sensing and communication (JSAC) has been proposed as an attractive technique with advantages of enhanced spectral and hardware efficiency along with low latency. However, in those old days, complex transceiver designs hindered the massive adoption of JSAC in many applications. Nevertheless, thanks to advancing wireless technologies in recent years, JSAC has recently attracted substantial attention for a wide range of civil and military applications. In particular, JSAC enables simultaneous sensing and communication functionalities with the full cooperation of both operations in shared resources such as hardware as well as radio resources (i.e., frequency, time, space and so on). Note that sensing functionality is associated with other sub-functionalities of radio-detection-and-ranging (Radar), computation, and localization which are sporadically used terms in the literature. Thus, to generalize the concept and avoid any confusion, we use the term “sensing” in the acronym JSAC as a consistent and general term associated with the other aforementioned sub-functionalities. This paper elaborates on defining sensing and communication operations and then, provides an overview with preliminaries, key latest findings, and state-of-the-art of JSAC. It also explores both existing and emerging Internet of Things (IoT) applications of JSAC. Next, it provides a new classification of JSAC technologies by taking into account not only the existing JSAC technologies but also a diverse range of technologies that allow JSAC to be used for various types of IoT applications. Eventually, this study projects future research directions and challenges of enabling JSAC in IoT.

We consider a full-duplex wireless powered communication network, supporting wireless power transfer of a hybrid access point in the downlink and time-division multiple-access (TDMA) devices in the uplink simultaneously. By considering the residual self-interference (SI), we formulate the problems of power allocation (PA) and time allocation (TA) to maximize the sum rate of the uplink subject to the energy causality as well as the peak and average power constraints. The non-convex optimization problems of the joint PA-TA and the PA are approximated as second-order cone programs to be solved with successive convex approximation. The results show that the proposed PA-TA (PA) algorithm outperforms the conventional PA-TA when the SI exists at any (severe) level.

We consider a two-phase decode-and-forward (DF) relay network assisted by an unmanned aerial vehicle (UAV), where the UAV performs energy harvesting and information decoding simultaneously with a power splitting (PS) receiver structure. For the network, we optimize the PS and time allocation (TA) factors to minimize the outage probability in transferring the data from a source to a destination suffering from blockages in the direct link. The optimal solution is found in two steps, optimization of the PS factor for given TA and then optimization of the TA factor using the conditionally optimized PS factor given in a closed-form. To reduce the complexity of the optimal method, we next propose a suboptimal method using a fixed TA determined by the transmission rate only and a fixed PS ffactor given by a function of the average power of the UAV-to-destination channel. The outage probability of the proposed method is analyzed in a generalized UAV channel model including path-loss, shadowing, and Nakagami-m fading, which is shown to agree with the simulation results and is close to the optimal performance. In addition, the optimal UAV position is investigated in various channel environments, which reveals that the optimal altitude depends on the deployment scenarios and the optimal ground position is the center-point between the source and destination in general.

As an innovative framework for sustainable communication within next-generation networks, reconfigurable intelligent surfaces (RISs) have developed potential transformation in improving simultaneous wireless information and power transfer (SWIPT). Alongside SWIPT, integrated sensing and communication (ISAC) has gained significant attention for its ability to combine communication and sensing functionalities within the same infrastructure, optimizing resource utilization and improving system performance. In this paper, we propose a framework that integrates an active (A-RIS) and simultaneous transmitting and reflecting RIS (STAR-RIS) assisted multiple-input multiple-output (MIMO) technology for SWIPT in an ISAC system. The system consists of an A-RIS, a STAR-RIS, a dual functional base station (DFBS), a group of reflection and transmission communication users (CUs) and reflection and transmission energy receiving devices (EDs), sensing targets simultaneously with the aid of RIS. We formulate an optimization problem to maximize the target rate while balancing the communication rate under energy harvesting (EH) constraints, and phase-shits constraints. It implies the trade-offs between target sensing accuracy and communication performance, demonstrating the potential of RIS to enhance system capabilities in diverse ISAC scenarios. The optimization problem and its constraints are inherently non-convex due to the high coupling between variables. Consequently, we employ the minimum mean square error method to address the non-convex nature of the problem. Thereby, it simplifies the problem by transforming it into a more manageable form and then applying an alternating optimization framework. This framework addresses the design of beamforming challenges at both the DFBS and the RIS (A-RIS/STAR-RIS) separately, by solving them iteratively using general approximation techniques. The analysis highlights the advantages of the proposed ARIS and STAR-RIS assisted SWIPT for ISAC framework over conventional RIS by achieving performance gain of 12-15%.