Achievable Sum Rate Optimization on NOMA-aided Cell-Free Massive MIMO with Finite Blocklength Coding

Non-orthogonal multiple access (NOMA)-aided cell-free massive multiple-input multiple-output (CFmMIMO) has been considered as a promising technology to fulfill strict quality of service requirements for ultra-reliable low-latency communications (URLLC). However, finite blocklength coding (FBC) in URLLC makes it challenging to achieve the optimal performance in the NOMA-aided CFmMIMO system. In this paper, we investigate the performance of the NOMA-aided CFmMIMO system with FBC in terms of achievable sum rate (ASR). Firstly, we derive a lower bound (LB) on the ergodic data rate. Then, we formulate an ASR maximization problem by jointly considering power allocation and user equipment (UE) clustering. To tackle such an intractable problem, we decompose it into two sub-problems, i.e., the power allocation problem and the UE clustering problem. A successive convex approximation (SCA) algorithm is proposed to solve the power allocation problem by transforming it into a series of geometric programming problems. Meanwhile, two algorithms based on graph theory are proposed to solve the UE clustering problem by identifying negative loops. Finally, alternative optimization is performed to find the maximum ASR of the NOMA-aided CFmMIMO system with FBC. The simulation results demonstrate that the proposed algorithms significantly outperform the benchmark algorithms in terms of ASR under various scenarios

Power Optimization in Multi-IRS Aided Delay-Constrained IoVT Systems

With the advancement of video sensors in the Internet of Things, Internet of Video Things (IoVT) systems, capable of delivering abundant and diverse information, have been increasingly deployed for various applications. However, the extensive transmission of video data in IoVT poses challenges in terms of delay and power consumption. Intelligent reconfigurable surface (IRS), as an emerging technology, can enhance communication quality and consequently improve system performance by reconfiguring wireless propagation environments. Inspired by this, we propose a multi-IRS aided IoVT system that leverages IRS to enhance communication quality, thereby reducing power consumption while satisfying delay requirements. To fully leverage the benefits of IRS, we jointly optimize power control for IoVT devices and passive beamforming for IRS to minimize long-term total power consumption under delay constraints. To solve this problem, we first utilize Lyapunov optimization to decouple the long-term optimization problem into each time slot. Subsequently, an alternating optimization algorithm employing optimal solution-seeking and fractional programming is proposed to effectively solve the optimization problems at each time slot. Simulation results demonstrate that the proposed algorithm significantly outperforms benchmark algorithms in terms of long-term total power consumption. Moreover, a trade-off between the number of IRS elements and system performance is also proved

Joint Transmission and Resource Optimization in NOMA-assisted IoVT with Mobile Edge Computing

Internet of Video Things (IoVT) brings much higher requirements on the transmission and computing capabilities of wireless networks than traditional Internet of Things (IoT). Non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) have been considered as two promising technologies to satisfy these requirements. However, successive interference cancellation (SIC) and grouping operations in NOMA as well as delay sensitive IoVT video tasks with different priorities make it challenging to achieve the optimal performance in NOMA-assisted IoVT with MEC. To address this issue, we formulate a joint optimization problem where both NOMA operations and MEC offloading are involved, with the goal to minimize the weighted average total delay. To tackle such intractable problem, we proposed a graph theory-based optimization framework, then decompose and transform the problem into finding \emph{negative} loops in the weighted directed graph. Specifically, we design a priority-based SIC decoding mechanism and propose convex optimization-based power allocation and computing resource allocation algorithms to calculate the adjacency matrix. Then, two negative loop searching algorithms are adopted to obtain the device association and grouping strategies. Simulation results demonstrate that compared with existing algorithms, the proposed algorithm reduces the weighted average total delay by up to 92.43% as well as improves the transmission rate of IoVT devices by up to 79.1%.</p