Twin Delayed DDPG based Dynamic Power Allocation for Mobility in IoRT

The internet of robotic things (IoRT) is a modern as well as fast-evolving technology employed in abundant socio-economical aspects which connect user equipment (UE) for communication and data transfer among each other. For ensuring the quality of service (QoS) in IoRT applications, radio resources, for example, transmitting power allocation (PA), interference management, throughput maximization etc., should be efficiently employed and allocated among UE. Traditionally, resource allocation has been formulated using optimization problems, which are then solved using mathematical computer techniques. However, those optimization problems are generally nonconvex as well as nondeterministic polynomial-time hardness (NP-hard). In this paper, one of the most crucial challenges in radio resource management is the emitting power of an antenna called PA, considering that the interfering multiple access channel (IMAC) has been considered. In addition, UE has a natural movement behavior that directly impacts the channel condition between remote radio head (RRH) and UE. Additionally, we have considered two well-known UE mobility models i) random walk and ii) modified Gauss-Markov (GM). As a result, the simulation environment is more realistic and complex. A data-driven as well as model-free continuous action based deep reinforcement learning algorithm called twin delayed deep deterministic policy gradient (TD3) has been proposed that is the combination of policy gradient, actor-critics, as well as double deep Q-learning (DDQL). It optimizes the PA for i) stationary UE, ii) the UE movements according to random walk model, and ii) the UE movement based on the modified GM model. Simulation results show that the proposed TD3 method outperforms model-based techniques like weighted MMSE (WMMSE) and fractional programming (FP) as well as model-free algorithms, for example, deep Q network (DQN) and DDPG in terms of average sum-rate performance

Ber-driven resource allocation for multimedia communication over downlink OFDMA networks / Tham Mau Luen

Recent years have witnessed the growing pervasiveness of multimedia services due to significant advancements of both wireless access and multimedia compression technologies. On one hand, orthogonal frequency-division multiple access (OFDMA) is the key enabler for high-speed multiuser communications. On the other hand, nonscalable and scalable multimedia coding standards offer superior compression gains as well as strong adaptabilities to channel variations. The inherent error-prone nature of channel environments, the wireless resource scarcity along with the variations in the importance among multimedia packets, however, render the quality-of-service (QoS) provisioning for multimedia communication over wireless networks a challenging task. Leveraging multiple target bit-error-rates (BERs) in concert with resource allocation to optimize the received multimedia quality is largely unexplored. Based on the principle that more important packets should be assigned stricter target BERs, this thesis proposes three BER-driven (BRA) resource allocation methods for transmitting pre-encoded multimedia over downlink OFDMA networks, targeting at generic, nonscalable and scalable bitstreams. For the generic case, each bitstream is associated with a different static target BER. Under this constraint, the impacts on three major classes of resource allocation scheme are analyzed from an optimization perspective. For both nonscalable and scalable cases, the target BER of each packet is dynamically adjusted based on scheduling, subcarrier assignment, bit and power allocation, channel quality, and importance level of that packet. The calculation of packet-importance metric takes into account decoder-based error concealment, where both simple and complex techniques are investigated. An additional constraint of strong decoding dependency is considered for the scalable case. Simulation results show that BRA schemes significantly outperform existing BER-unaware techniques in terms of spectral efficiency, power efficiency and decoded multimedia quality. Further test evaluates the suitability of equal power allocation which is the common assumption in the resource-allocation literature

Two-Level Scheduling for Video Transmission over Downlink OFDMA Networks.

This paper presents a two-level scheduling scheme for video transmission over downlink orthogonal frequency-division multiple access (OFDMA) networks. It aims to maximize the aggregate quality of the video users subject to the playback delay and resource constraints, by exploiting the multiuser diversity and the video characteristics. The upper level schedules the transmission of video packets among multiple users based on an overall target bit-error-rate (BER), the importance level of packet and resource consumption efficiency factor. Instead, the lower level renders unequal error protection (UEP) in terms of target BER among the scheduled packets by solving a weighted sum distortion minimization problem, where each user weight reflects the total importance level of the packets that has been scheduled for that user. Frequency-selective power is then water-filled over all the assigned subcarriers in order to leverage the potential channel coding gain. Realistic simulation results demonstrate that the proposed scheme significantly outperforms the state-of-the-art scheduling scheme by up to 6.8 dB in terms of peak-signal-to-noise-ratio (PSNR). Further test evaluates the suitability of equal power allocation which is the common assumption in the literature

System block diagram for video over OFDMA networks.

<p>System block diagram for video over OFDMA networks.</p

Average PSNR per video for different schemes at <i>overall target BER</i> = 10⁻².

<p>Video index represents 1: <i>foreman</i>, 2: <i>carphone</i>, 3: <i>coastguard</i>, 4: <i>silent</i>, 5: <i>mobile</i> and 6: <i>news</i>.</p

Complexity analysis for different algorithms.

<p>Complexity analysis for different algorithms.</p

Empowering Non-Terrestrial Networks With Artificial Intelligence: A Survey

6G networks can support global, ubiquitous and seamless connectivity through the convergence of terrestrial and non-terrestrial networks (NTNs). Unlike terrestrial scenarios, NTNs pose unique challenges including propagation characteristics, latency and mobility, owing to the operations in spaceborne and airborne platforms. To overcome all these technical hurdles, this survey paper presents the use of artificial intelligence (AI) techniques in learning and adapting to the complex NTN environments. We begin by providing an overview of NTNs in the context of 6G, highlighting the potential security and privacy issues. Next, we review the existing AI methods adopted for 6G NTN optimization, starting from machine learning (ML), through deep learning (DL) to deep reinforcement learning (DRL). All these AI techniques have paved the way towards more intelligent network planning, resource allocation (RA), and interference management. Furthermore, we discuss the challenges and opportunities in AI-powered NTN for 6G networks. Finally, we conclude by providing insights and recommendations on the key enabling technologies for future AI-powered 6G NTNs