Towards Facilitating URLLC in UAV-enabled MEC Systems for 6G Networks

UAV-mounted IRS assisted wireless powered mobile edge computing systems: Joint beamforming design, resource allocation and position optimization

In 6G networks, integrated sensing and communication (ISAC) is envisioned as a key technology that enables wireless systems to perform joint sensing and communication using shared hardware, antenna(s) and spectrum. ISAC designs facilitate emerging applications such as digital twins, smart cities and autonomous driving. Such applications also demand ultra-reliable and low-latency communication (URLLC), a feature that was first introduced in 5G and is expected to be further enhanced in 6G. Thus, an ISAC-enabled URLLC system can prioritize critical and time-sensitive targets and ensure information delivery under strict latency and reliability constraints. We propose a bi-static multiple-input multiple-output (MIMO) ISAC system to detect the arrival of URLLC messages and prioritize their delivery. In this system, a dual-function base station (BS) communicates with a user equipment (UE) and a sensing receiver (SR) is deployed to collect echo signals reflected from a target of interest. The BS regularly transmits messages of enhanced mobile broadband (eMBB) services to the UE. During each eMBB transmission, if the SR senses the presence of a target of interest, it immediately triggers the transmission of an additional URLLC message. To reinforce URLLC transmissions, we propose a dirty-paper coding (DPC)-based technique that mitigates the interference of both eMBB and sensing signals. To decode the eMBB message, we consider two approaches for handling the URLLC interference: treating interference as noise (TIN) and successive interference cancellation (SIC). For this system, we formulate the rate-reliability-detection trade-off in the finite blocklength (FBL) regime by evaluating the communication rate of the eMBB transmissions, the reliability of the URLLC transmissions and the probability of the target detection. Our numerical analysis show that our proposed DPC-based ISAC scheme significantly outperforms power-sharing based ISAC and traditional time-sharing schemes. In particular, it achieves higher eMBB transmission rate while satisfying both URLLC and sensing constraints.

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has been considered as a promising approach to offering extensive coverage and massive computing capacities for Internet of Things (IoT). In this letter, we propose a novel multi-UAV-assisted multi-access MEC model by allowing each IoT user to offload task bits to multiple MEC servers deployed at UAVs simultaneously for parallel computing, which can effectively reduce the energy consumption of users and UAVs. The weighted sum energy consumption of UAVs and users is minimized by jointly optimizing the bit allocation, transmit power, CPU frequency, bandwidth allocation and UAVs' trajectories. Due to the non-convexity of the formulated problem, it is decomposed into two subproblems and a joint resource allocation and trajectory design algorithm is proposed by alternative optimization. Simulation results show that our proposed algorithm with multiple radio access outperforms the fixed trajectory, fixed bandwidth allocation and the single access schemes.

The coexistence of human type communications (HTC) with an ever-increasing number of machine type communications (MTC) is a challenge that the forthcoming Fifth Generation (5G) network is claimed to tackle. Ultra-Reliable and Low Latency Communications (URLLC) are among the most demanding services that 5G network will support. Also referred to as mission-critical services, URLLC often requires group communications. The evolved Multimedia Broadcast and Multicast Services (eMBMS) architecture, currently specified by 3GPP to support group-based services in cellular networks, needs a revision to deliver future mission-critical services. In this paper, we analyze the performance of different machine-type multicast service (MtMS) scenarios and we compare three subgroup based approaches for multicast data delivery to MTC devices with limited channel bandwidth. Achieved simulation results provide useful guidelines for the design of multicasting schemes able to support delay-sensitive applications in 5G networks.

5G is envisaged to support diverse use cases and applications. Enhanced Mobile Broadband (eMBB) and Ultra-Reliable and Low Latency Communications (URLLC) have been identified as two main communication scenarios in 5G. Unlike eMBB service which demands high data rate, URLLC has stringent latency and reliability requirement. Due to the sporadic and stochastic characteristics of URLLC traffic, an URLLC transmission may suddenly occurs during the transmission of eMBB data. It is sometimes necessary to transmit URLLC data on the resources occupied by eMBB. Thus in this paper, we investigate the dynamic downlink multiplexing for eMBB and URLLC data on the same time/frequency resource. Specifically, we present a dynamical puncturing scheme, which allows URLLC data occupy part of the radio resource that has been scheduled to ongoing eMBB transmission. The puncturing will degrade the performance of the eMBB traffic, therefore we introduce the signal space diversity (SSD) scheme into the proposed dynamic puncturing scheme. The decoding performance of eMBB data over the fading channel is thus improved through the component interleaving and rotation modulation. Furthermore, we derive the optimal rotation angel that can achieve the optimal puncturing performance by maximizing the average mutual information (AMI). The link-level simulation results show that the proposed scheme outperforms existing strategy, and the performance gain is around 1dB when the code rate is set as $2/3$. This observation also validates the theoretical analysis based on the AMI.

This paper studies the coexistence of enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communication (URLLC) services in a cellular network that is assisted by a reconfigurable intelligent surface (RIS). The system model consists of one base station (BS) and one RIS that is deployed to enhance the performance of both eMBB and URLLC in terms of the achievable data rate and reliability, respectively. We formulate two optimization problems, a time slot basis eMBB allocation problem and a mini-time slot basis URLLC allocation problem. The eMBB allocation problem aims at maximizing the eMBB sum rate by jointly optimizing the power allocation at the BS and the RIS phase-shift matrix while satisfying the eMBB rate constraint. On the other hand, the URLLC allocation problem is formulated as a multi-objective problem with the goal of maximizing the URLLC admitted packets and minimizing the eMBB rate loss. This is achieved by jointly optimizing the power and frequency allocations along with the RIS phase-shift matrix. In order to avoid the violation in the URLLC latency requirements, we propose a novel framework in which the RIS phase-shift matrix that enhances the URLLC reliability is proactively designed at the beginning of the time slot. For the sake of solving the URLLC allocation problem, two algorithms are proposed, namely, an optimization-based URLLC allocation algorithm and a heuristic algorithm. The simulation results show that the heuristic algorithm has a low time complexity, which makes it practical for real-time and efficient multiplexing between eMBB and URLLC traffic. In addition, using only 60 RIS elements, we observe that the proposed scheme achieves around 99.99% URLLC packets admission rate compared to 95.6% when there is no RIS, while also achieving up to 70% enhancement on the eMBB sum rate.

Consumer electronics can support sixth-generation (6G) systems and their services, including ultra-reliable and low-latency communications (URLLC). In this context, unmanned aerial vehicles (UAVs) have become increasingly popular because they can: be dynamically positioned, take advantage of channel gains, and communicate directly via line-of-sight. UAVs, on the other hand, are unable to maintain a stable flight for prolonged periods of time and suffer from jittering impairments caused by strong winds. As a result of atmospheric conditions and environmental interference, the perfect channel state information (CSI) becomes obsolete. The aim of this study is to propose a power-efficient resource allocation scheme for URLLC-enabled UAV communication systems under finite block lengths, imperfect CSIs, and adverse jittering effects caused by wind. This involves optimizing UAV positioning and blocklength distribution together. Additionally, we propose a perturbation-based semidefinite programming (SDP) approach to reduce the sum power and demonstrate that it outperforms fixed benchmark algorithms. As such, it can reach power savings up to 77.18% compared to fixed benchmark algorithms. Our extensive simulation results demonstrate that our approach performs similarly to the exhaustive search and has low complexity. Thus, the proposed method thus provides a practical power-efficient URLLC implementation for memory-constrained UAV networks.

The ultra-reliable and low-latency communication (URLLC) is the key driver of the current 5G new radio standardization. URLLC encompasses sporadic and small-payload transmissions that should be delivered within extremely tight radio latency and reliability bounds, i.e., a radio latency of 1 ms with 99.999% success probability. However, such URLLC targets are further challenging in the 5G dynamic time division duplexing (TDD) systems, due to the switching between the uplink and downlink transmission opportunities and the additional inter-cell cross-link interference (CLI). This paper presents a system level analysis of the URLLC outage performance within the 5G new radio flexible TDD systems. Specifically, we study the feasibility of the URLLC outage targets compared to the case with the 5G frequency division duplexing (FDD), and with numerous 5G design variants. The presented results therefore offer valuable observations on the URLLC outage performance in such deployments, and hence, introducing the state-of-the-art flexible-FDD technology.

Preemptive scheduling provides an efficient mechanism for the coexistence of enhanced Mobile Broadband (eMBB) and Ultra-Reliable and Low-Latency Communication (URLLC) services in 5G networks. Due to differences in Quality-of-Service (QoS) requirements of the two services, it is necessary to investigate the trade-offs among energy-efficiency, throughput, latency, and reliability, in task offloading scenarios. Existing studies do not fully extend the preemptive scheduling mechanism for uplink task offloading while considering the interplay between all the QoS requirements of eMBB and URLLC User Equipment (UE). We address this gap by considering an edge network with eMBB and URLLC UE and formulate an objective function to improve energy-efficiency, throughput, and task queueing delay of eMBB UE while satisfying the transmission reliability of URLLC users. We also introduce the Twin Delayed Deep Deterministic Policy Gradient (TD3) to optimize task offloading. Simulation results show that the TD3 outperforms a widely used benchmark model in terms of convergence rate and variance. Results also highlight a very crucial aspect in reward shaping that embodies the trade-offs among the optimization variables and constraints.

Carrier frequency offset (CFO) and channel equalization are two critical problems for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) wireless communication systems in ultra-reliable and low latency communication (URLLC). In this paper, we propose a semi-blind precoding aided structure that includes two CFO estimation approaches and an independent component analysis (ICA) based equalization scheme for MIMO OFDM systems in URLLC, requiring no pilots. We design a non-redundant balanced precoding strategy, killing two birds with one stone, where reference signals are superimposed into source signals to simultaneously allow CFO estimation and ambiguity elimination in the ICA-equalized signals. The proposed precoding-aided CFO estimation approach performs by maximizing a cost function formulated via the cross-correlations between the reference signal and the received signal. We further propose a low-complexity closed-form CFO estimation approach, by transforming the formulated cost function into a new expression. To maximize bit error rate (BER) performance, particle swarm optimization (PSO) is employed to perform the joint optimization of precoding constant and the number of OFDM blocks for CFO estimation, while avoiding exhaustive search. The proposed semi-blind precoding-aided structure provides a trade-off between performance, complexity and spectral efficiency for MIMO OFDM systems in URLLC.

An ultra-reliable and low latency communications assisted modulation based non-orthogonal multiple access scheme

Unmanned Aerial Vehicles (UAVs) have gained great attention in Internet-of-Things (IoT) applications benefiting from the flexibility of deployment and line-of-sight (LoS) channel conditions. In this paper, we study a UAV-assisted Mobile Edge Computing (MEC) system for providing services to large-scale IoT nodes (INs). In the considered system, the UAV acts as an Aerial Base Station (ABS) that can selectively access large-scale INs to enable efficient data collection and computational offloading while ensuring data integrity. Specifically, we first derive the reconstruction error upper bound based on Graph Laplacian Regularization (GLR) as the data integrity metric. Considering that the UAV is usually limited in energy consumption, we propose an energy efficiency (EE) maximization problem that jointly optimizes the selection of INs, the scheduling of INs, the 3D flight and the computational resource allocation of the UAV, subject to constraints related to UAV motion, resources and data integrity. Due to the non-convex nature of the considered problem, a two-stage algorithm called GDA-3DNACRA is proposed, which adopts Gershgorin Disk Alignment (GDA), Convex Relaxation, and Successive Convex Approximation (SCA) for solving it efficiently. Simulation results have shown that the proposed approach can significantly improve the EE of the UAV while ensuring the data integrity.

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is a prominent strategy where a UAV equipped with an MEC server is deployed to serve terminal devices. This paper considers a multi-UAV assisted network in which multiple UAVs and a terrestrial base station (BS) are deployed to provide MEC services to mobile users. The objective is to minimize an energy and latency-based cost function by jointly optimizing task offloading and MEC server selection decision, transmission power, UAV trajectory, and CPU frequency allocation. An alternating iterative approach based on the block descent method is proposed to solve this problem. In the first layer, task offloading and server selection decision subproblem is solved using a game theoretic approach. The second layer handles offloading and downloading transmission power allocations by utilizing a simplistic geometric waterfilling (GWF) technique, and the UAV trajectory by successive convex approximation (SCA). Whereas, the third layer solves the computation resource subproblem by performing CPU frequency allocation using a gradient descent method. The proposed method uses a segment-by-segment approach, which divides the entire UAV flight trajectory into shorter timeframe segments to reduce the computation time. Simulation results are presented to show that the proposed approach outperforms various benchmark schemes.

Due to the high altitude and deployment flexibility, unmanned aerial vehicles (UAVs) can be used as a relay to avoid obstacles and extend the coverage of wireless networks. On the other hand, ultra-reliable and low-latency communication (URLLC) is often required to deliver the information reliably and timely for many emerging applications. In this correspondence, we combine the advantages of both UAV and URLLC to investigate the resource allocation for a URLLC-enabled two-way UAV relaying system. Our goal is to maximize the transmission rate of the backward link with the constraint of URLLC requirement for the forward link. The optimization is non-convex and difficult to solve. Therefore, the optimization variables are divided to several blocks, and three sub-problems are formulated and solved. Finally, an iterative algorithm is proposed to solve these sub-problems alternately. Simulation results show that the proposed joint optimization scheme can achieve excellent performance for the URLLC-enabled two-way UAV relaying system.

Rate-Splitting Multiple Access (RSMA)is a powerful technology to enhance spectral efficiency in short packet Ultra-Reliable and Low-Latency Communication (URLLC) systems with Finite Blocklength (FBL) codes.Enhancing system performance requires larger antenna arrays to meet increasingly stringent QoS requirements, which is very costly. To overcome this limitation, we propose a novel movable antennas (MAs)-assisted RSMA short packet transmission schemes to realize more flexible beamforming and higher spatial multiplexing gains with a small number of MAs. We formulate a sum rate maximization problem for the joint optimization of beamforming and antenna position under the specified URLLC requirements. An alternating optimization (AO) algorithmis developed to address this challenging high-dimensional non-convex optimization problem.Specifically, the Successive Convex Approximation (SCA) methodis utilized to design the beamforming matrix. Additionally, we proposeda novel worst position neighborhood variation-oriented particle swarm optimization (WPNVPSO) to optimize the MA positions efficiently.Numerical results demonstrate our proposed design outperforms other benchmarks by achieving high data rates with lower latency and higher reliability, while utilizing fewer antennas.