Upcoming fifth-generation (5G) networks need to support novel ultra-reliable and low-latency (URLLC) traffic that utilizes short packets. This requires a paradigm shift as traditional communication systems are designed to transmit only long data packets based on Shannon’s capacity formula, which poses a challenge for system designers. To address this challenge, this paper relies on an unmanned aerial vehicle (UAV) and a reconfigurable intelligent surface (RIS) to deliver short URLLC instruction packets between ground Internet of Things (IoT) devices. In this context, we perform passive beamforming of RIS antenna elements as well as non-linear and non-convex optimization to minimize the total decoding error rate and find the UAV’s optimal position and blocklength. In this paper, a novel, polytope-based method from the class of direct search methods (DSM) named Nelder-Mead Simplex (NMS) is used to solve the optimization problem based on its computational efficiency; in terms of lesser number of required iterations to evaluate objective function. The proposed approach yields better convergence performance than the traditional gradient descent optimization algorithm and a lower computation time and equivalent performance for the blocklength variable as the exhaustive search. Moreover, the proposed approach allows ultra-high reliability, which can be attained by increasing the number of antenna elements in RIS as well as increasing the allocated blocklengths. Simulations demonstrate the RIS’s performance gain and conclusively show that the UAV’s position is crucial for achieving ultra-high reliability in short packet transmission.

In the upcoming sixth-generation (6G) networks, ultra-reliable and low-latency communication (URLLC) is considered as an essential service that will empower real-time wireless systems, smart grids, and industrial applications. In this context, URLLC traffic relies on short blocklength packets to reduce the latency, which poses a daunting challenge for network operators and system designers since classical communication systems are designed based on the classical Shannon’s capacity formula. Therefore, to tackle this challenge, this article considers an unmanned aerial vehicle (UAV) acting as a decode-and-forward relay to communicate short URLLC control packets between a controller and multiple-mobile robots in a cell to enable a use-case of Agriculture 4.0. Moreover, this article employs perturbation theory and studies the quasi-optimization of the UAV’s location, height, beamwidth, and resource allocation, including time-varying power and blocklength for the two phases of transmission from the controller to UAV and from UAV to robots. In this regard, we propose an iterative optimization method to find the optimal UAV’s height and location, the antenna beamwidth, and the variable power and blocklength allocated to each robot inside the circular cell to minimize the average overall decoding error. It is demonstrated that the proposed algorithm outperforms other benchmark algorithms based on fixed parameters and performs nearly as well as the smart exhaustive search. Lastly, our results emphasize the need to jointly optimize all of the abovementioned UAV’s system parameters and resource allocation for the two phases of transmission to achieve URLLC for multiple-mobile robots.

Ultra-reliable and low-latency (URLLC) traffic in the upcoming sixth-generation (6G) systems utilize short packets, signalling a distancing from traditional communication systems devised only to support long data packets based on Shannon's capacity formula. This poses a formidable challenge for system designers and network operators. Many URLLC scenarios involve infrastructure-less unmanned aerial vehicle (UAV)-assisted communications. One of the biggest challenges with UAVs is their limited battery capacity, which can cause abrupt disruption of UAV-assisted communications. To overcome these limitations, we consider URLLC-enabled over-the-air charging of UAV relay system using a laser transmitter. Furthermore, we formulate a non-convex optimization problem to minimize the total decoding error rate subject to optimal resource allocation, including blocklength allocation, power control, trajectory planning, and energy harvesting to facilitate URLLC in such systems. In this regard, given its lower complexity, a novel perturbation-based iterative method is proposed to solve the optimization problem. The proposed method yields optimal blocklength allocation and power control for the two transmission phases, i.e., from the source node to the UAV and from the UAV to the robot acting as a ground station. It also maps the UAV trajectory from the initial position to the final position, and the UAV completes the flight using the laser's harvested energy. It is shown that the proposed algorithm and fixed baseline scheme, named fixed blocklength (FB), yield a similar performance as the exhaustive search in terms of UAV energy consumption. In contrast, fixed trajectory (FT) delivers the worst performance. Simultaneously, the proposed method yields the best performance in terms of the lowest average overall decoding error compared to fixed baseline schemes, including FB and FT, showing the efficacy of the proposed technique.

Achieving ultra-high reliability for short packets is a core challenge for future wireless communication systems, as current systems are designed only to transmit long packets based on classical information-theoretic principles. To tackle this challenge, this letter relies on multi-hop unmanned aerial vehicle (UAV) relay links to deliver short ultra-reliable and low-latency (URLLC) instruction packets between ground Internet of Things (IoT) devices. To accomplish this task, we perform non-linear optimization to minimize the overall decoding error probability in order to find the optimal values of the distance and the blocklength. In this vein, a novel, semi-empirical based non-iterative algorithm is proposed to solve the quasi-optimization problem. The algorithm executes in quasilinear time and converges to a globally optimal/sub-optimal solution based on the chosen parameters. Simulation results demonstrate that our algorithm allows operation under the ultra-reliable regime (URR), and yields the same performance as exhaustive search algorithms.

In this manuscript, we propose an alternating optimization framework to maximize the energy efficiency of a backscatter-enabled cooperative Non-orthogonal multiple access (NOMA) system by optimizing the transmit power of the source, power allocation coefficients (PAC), and power of the relay node under imperfect successive interference cancellation (SIC) decoding. A three-stage low-complexity energy-efficient alternating optimization algorithm is introduced which optimizes the transmit power, PAC, and relay power by considering the quality of service (QoS), power budget, and cooperation constraints. Subsequently, a joint channel coding framework is introduced to enhance the performance of far user which has no direct communication link with the base station (BS) and has bad channel conditions. In the destination node, the far user data is jointly decoded using a Sum-product algorithm (SPA) based joint iterative decoder realized by jointly-designed Quasi-cyclic Low-density parity-check (QC-LDPC) codes. Simulation results evince that the proposed backscatter-enabled cooperative NOMA system outperforms its counterpart by providing an efficient performance in terms of energy efficiency. Also, proposed jointly-designed QC-LDPC codes provide an excellent bit-error-rate (BER) performance by jointly decoding the far user data for considered BSC cooperative NOMA system with only a few decoding iterations.

In this manuscript, we present an energy-efficient alternating optimization framework based on the multiantenna ambient backscatter communication (AmBSC)-assisted cooperative nonorthogonal multiple access (NOMA) for next-generation (NG) Internet of Things (IoT)-enabled communication networks. Specifically, the energy-efficiency maximization is achieved for the considered AmBSC-enabled multicluster cooperative IoT NOMA system by optimizing the active-beamforming vector and power-allocation coefficients (PACs) of IoT NOMA users at the transmitter, as well as passive-beamforming vector at the multiantenna-assisted backscatter node. Usually, increasing the number of IoT NOMA users in each cluster results in intercluster interference (ICI) (among different clusters) and intracluster interference (among IoT NOMA users). To combat the impact of ICI, we exploit a zero-forcing (ZF)-based active-beamforming, as well as an efficient clustering technique at the source node. Further, the effect of intracluster interference is mitigated by exploiting an efficient power-allocation policy that determines the PAC of IoT NOMA users under the Quality-of-Service (QoS), cooperation, SIC decoding, and power-budget constraints. Moreover, the considered nonconvex passive-beamforming problem is transformed into a standard semidefinite programming (SDP) problem by exploiting the successive-convex approximation (SCA), as well as the difference of convex (DC) programming, where Rank-1 solution of passive-beamforming is obtained based on the penalty-based method. Furthermore, the numerical analysis of simulation results demonstrates that the proposed energy-efficiency maximization algorithm exhibits an efficient performance by achieving convergence within only a few iterations.