Millimeter-wave (mmWave) integrated access and backhaul (IAB) has recently received considerable interest for its advantage in reducing the expenses related to the deployment of fiber optics, such as the Terragraph proposed by Meta’s Connectivity Lab. Terragraph networks aim to provide high-speed internet access to dense urban environments. However, due to the vulnerability to blockages and high path loss associated with mmWave frequencies, the proper deployment planning of mmWave networks is required to achieve the desired service quality. By obtaining a stable power supply through its tether connected to the ground, tethered unmanned aerial vehicle (UAV)-mounted base station (BS) can provide reliable communication service with the sacrifice of limited mobility. In this paper, we investigate the potential of incorporating tethered UAVs into Terragraph-like networks. To this end, we propose a novel deep reinforcement learning (DRL) framework that aims to minimize the overall deployment cost by optimizing the number of required UAVs and terrestrial BSs (TBSs), the hovering positions of deployed UAVs, and the multi-hop backhauling topology. Unlike the conventional DRL frameworks that focus on maximizing the expected cumulative or average reward, we formulate the proposed framework based on the max-Bellman optimality equation in order to maximize the maximum reward. Numerical results reveal that the proposed algorithm is able to yield significant reduction in terms of deployment cost. We also use case studies from cities in Asia, Europe, and North America to verify the practical applicability of the proposed framework.

Age of information (AoI) has introduced a new dimension into the design of real-time monitoring networks. However, how to achieve energy-efficient freshness-aware information transmission with energy-constrained Internet of Things (IoT) devices remains a challenge. To solve this challenge, in this paper, we consider a new metric called freshness-aware energy efficiency (FAEE), which is defined as the ratio of AoI improvement to transmission energy consumption. We study a freshness-aware integrated access and backhaul (IAB) network in which multiple IoT devices (IDs) that are accountable for generating and transmitting status updates frequently over time so as to maintain the freshness of information observed at the IAB donor. For this system setup, we first formulate three mixed discrete-continuous optimization problems in order to maximize the long-term average FAEE under different transmission scheduling schemes, including round-robin (RR), sub-channel allocation (SA), and time-resource allocation (TA). By constructing average-reward Markov decision processes (MDPs) with mixed discrete-continuous action space to model these optimization problems, we propose a novel proximal policy optimization (PPO) based deep reinforcement learning (DRL) framework, which is referred to as APO-CD, to learn sub-optimal policies for status update strategy and resource allocation. Extensive simulation results are provided to show the effectiveness of our proposed algorithm and to reveal several design insights of freshness-aware IAB networks.

Mobile edge computing (MEC) can dramatically promote the computation capability and prolong the lifetime of mobile users (MUs) by offloading computation-intensive tasks to edge cloud. In this paper, a spatial-random two-tier heterogeneous network (HetNet) is modelled to feature random node distribution, where the small-cell base stations (SBSs) and the macro base stations (MBSs) are cascaded with servers with different levels of computing and storage capacity. Only a certain type of application services and finite number of offloaded tasks can be cached and processed in the resource-limited edge server. For that setup, we investigate the performance of two offloading strategies corresponding to integrated access and backhaul (IAB)-enabled MEC networks and traditional cellular MEC networks. Using tools from stochastic geometry and queuing theory, we derive the average delay for the two different strategies, in order to better understand the influence of IAB on MEC networks. Simulations results are provided to verify the derived expressions and to reveal various system-level insights.

Aerial relays have been regarded as an alternative and promising solution to extend and improve satellite-terrestrial communications, as the probability of line-of-sight transmissions increases compared with adopting terrestrial relays. In this paper, a cooperative satellite-aerial-terrestrial system including a satellite transmitter (S), a group of terrestrial receivers (D), and an aerial relay (R) is considered. Specifically, considering the randomness of S and D and employing stochastic geometry, the coverage probability of R-D links in non-interference and interference scenarios is studied, and the outage performance of S-R link is investigated by deriving an approximated expression for the outage probability. Moreover, an optimization problem in terms of the transmit power and the transmission time over S-R and R-D links is formulated and solved to obtain the optimal end-to-end energy efficiency for the considered system. Finally, some numerical results are provided to validate our proposed analysis models, as well as to study the optimal energy efficiency performance of the considered system.

Mobile-edge cloud computing (MEC) as an emerging and prospective computing paradigm, can significantly enhance computation capability and prolong the lifetime of mobile devices (MDs) by offloading computation-intensive tasks to the cloud. This paper considers applying simultaneous wireless information and power transfer (SWIPT) technique to a multi-user computation offloading problem for mobile-edge cloud computing, where energy-limited mobile devices (MDs) harvest energy form the ambient radio-frequency (RF) signal. We investigate partial computation offloading by jointly optimizing MDs' clock frequency, transmit power and offloading ratio with the system design objective of minimizing energy cost of mobile devices. To this end, we first formulate an energy cost minimization problem constrained by task completion time and finite mobile- edge cloud computation capacity. Then, by exploiting alternative optimization (AO) based on difference of convex function (DC) programming and linear programming, we design an iterative algorithm for clock frequency control, transmission power allocation, offloading ratio and power splitting ratio to solve the non-convex optimization problem. Our simulation results reveal that the proposed algorithm can converge within a few iterations and yield minimum system energy cost.

In the 5G wireless networks, non‐orthogonal multiple access (NOMA) is a promising paradigm to improve its high spectrum efficiency. This study considers applying simultaneous wireless information and power transfer (SWIPT) technique to cooperative NOMA wireless networks, where energy‐constrained relay nodes harvest the ambient radio‐frequency signal and use the harvested energy to forward the packets from sources to destinations. To this end, the authors first formulate the energy‐efficient cooperative transmission problem for SWIPT in NOMA with imperfect channel state information. Then, by exploiting alternative optimisation and dual decomposition, they design an iterative algorithm for power allocation and power splitting (PS) to solve the non‐convex optimisation problem. The authors' simulation results reveal that the proposed algorithm can converge within a few iterations and yield optimal system energy efficiency.