Tactile Internet applications such as remote surgery and real-time haptic control require ultra-low delay, high reliability, and steady throughput. Existing congestion control algorithms (CUBIC, Reno, BBRv2) degrade under jitter and loss. This work presents BBRv2-TI for QUIC-based TI, modeling congestion control as a multi-objective optimization over throughput, jitter, and loss with a gradient-free utility. Eleven BBRv2 parameters in QUICHE are tuned to improve responsiveness without kernel or cross-layer changes. In emulation, BBRv2TI reduces jitter by up to 98%, lowers loss by about 30%, and increases throughput by as much as 1.5× over baselines, indicating suitability for delay sensitive TI systems.

Massive multiple input multiple output (M-MIMO) technology plays a pivotal role in fifth-generation (5G) and beyond communication systems, offering a wide range of benefits, from increased spectral efficiency (SE) to enhanced energy efficiency and higher reliability. However, these advantages are contingent upon precise channel state information (CSI) availability at the base station (BS). Ensuring precise CSI is challenging due to the constrained size of the coherence interval and the resulting limitations on pilot sequence length. Therefore, reusing pilot sequences in adjacent cells introduces pilot contamination, hindering SE enhancement. This paper reviews recent advancements and addresses research challenges in mitigating pilot contamination and improving channel estimation, categorizing the existing research into three broader categories: pilot assignment schemes, advanced signal processing methods, and advanced channel estimation techniques. Salient representative pilot mitigation/assignment techniques are analyzed and compared in each category. Lastly, possible future research directions are discussed.

The surge in the number of various types of connected devices with the upcoming 6G networks may surpass the capabilities of traditional wireless infrastructure. Specifically, unmanned aerial vehicles (UAV s) can serve users to enhance network coverage and capacity in regions with limited or no existing infrastructure. However, resource allocation constraints and complex dynamics of UAV s pose significant challenges in optimal operation, such as path planning. Recently, researchers proposed simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs), which may serve the multiple users residing in the transmission and reflection regions. Unfortunately, STAR-RIS is only capable of reflecting incident signals, thus restricting its potential to enhance service quality in intricate channel conditions, particularly when the node distance is substantial. Motivated by this, we introduce a novel concept named actively simultaneously transmitting and reflecting (ASTAR)- RISs, which can amplify incident signals in addition to reflection. When mounted on UAVs, they can provide an improved signal-to-noise ratio (SNR) to numerous users in remote or inaccessible areas. We formulate the ASTAR-RIS-UAV-assisted rate maximization problem subject to the UAV mobility constraint and aim to find the UAV optimal path planning for a given flight time. While the formulated problem is non-convex, successive convex approximation and iterative algorithms are used to find the optimal solution to the problem, followed by a heuristic approach. Simulation results show that the proposed model outperforms existing approaches regarding network performance and resource allocation, highlighting the potential of adding ASTAR-RISs in UAV-assisted wireless networks.

Reconfigurable intelligent surfaces (RISs) have emerged as an efficient and cost-effective technique to enhance a great variety of possible performances of the Internet of Things (IoT) systems by re-configuring the propagation environment. Motivated by this, we investigate the RIS-assisted edge computing systems for batteryless IoT sensors (b-IoT) under Rician fading channel conditions. We consider a fixed time frame divided into three slots. A b-IoT sensor harvests energy from radio frequency signals from a nearby base station (BS) during the first time slot. While performing local computation, the b-IoT sensor offloads computation bits to the BS and an IoT sensor using device-to-device communications protocol in the second and final time slots, respectively. An offloading ratio differentiates the fraction of computational bits, offloaded to BS and IoT sensors. We formulate the optimization problem with the convex sum of computational bits as objective function and energy consumption, offloading ratio, and energy harvesting constraints. We propose a gradient descent-based iterative algorithm to solve the optimization problem. Simulation assessments depict RIS panel-assisted edge computing, and energy harvesting enhances the performance by more than 90% compared to the traditional baseline schemes, such as networks with no RIS panel.

Recent research on reconfigurable intelligent surfaces (RIS) suggests that the RIS panel, containing passive elements, enhances channel performance for the internet of things (IoT) systems by reflecting transmitted signals to the receiving nodes. This paper investigates RIS panel assisted-wireless network to instigate minimal base station (BS) transmit power in the form of energy harvesting for batteryless IoT sensors to maximize bits transmission in the significant multi-path environment, such as urban areas. Batteryless IoT sensors harvest energy through the RIS panel from external sources, such as from nearby BS radio frequency (RF) signal in the first optimal time frame, for a given time frame. The bits transmission among IoT sensors, followed by a device-to-device (D2D) communications protocol, is maximized using harvested energy in the final optimal time frame. The bits transmission is at least equal to the number of bits sampled by the IoT sensor. We formulate a non-convex mixed-integer non-linear problem to maximize the number of communicating bits subject to energy harvesting from BS RF signals, RIS panel energy consumption, and required time. We propose a robust solution by presenting an iterative algorithm. We perform extensive simulation results based on the 3GPP Urban Micro channel model to validate our model.

This paper applies non-orthogonal multiple access (NOMA) and relaying schemes in a mmWave based wireless heterogeneous system that aims to support Internet of Things (IoT) applications. The system consists of high power base stations, low-power relays, and low-power IoT devices. Due to the ad hoc deployment nature of low-power relays, they have very limited access to wireline power charging facilities. Furthermore, IoT devices normally have limited power and short battery life. The study assumes low-power relays and IoT devices are capable of energy harvest functionality. With the help of relays or IoT devices, downlink NOMA transmission consists of two phases. In the first phase, the BS sends a composite signal to a UE and a selected relay simultaneously by applying NOMA. After receiving the signal, relay or the IoT device split the signal into two parts. One part is for information decoding and the other part is for energy harvesting. In the second phase, the BS sends another message to UE 1 while the relay sends the decoded message to UE 2 by using the harvested energy in phase 1. The outage problem of the proposed scheme is analyzed and simulations results are presented to verify the theoretical results.