Whole Scenario of 5G Communication Access in the Digital Transformation of Medium and Large Enterprises

With the evolution and upgrading of the fifth-generation mobile communication technology, the mobile network will support a larger mobile user group and more diverse business scenarios, greatly enhance the Internet service experience, and fully support IoT-aware applications. As the forefront of the Internet of Everything, the wireless access network plays an important role in the evolution of the network. The continuous innovation of a new generation of information technology, especially the application of technologies such as big data, cloud computing, artificial intelligence, and the Internet of Things, has triggered an upsurge in the digital transformation of enterprises. Digital transformation has had a profound impact on the production and operation activities of enterprises and has also changed the organization and management of enterprise innovation activities. From the perspective of the innovation ecosystem, the digital transformation of a single enterprise leads to changes in innovation activities, which will converge and emerge new characteristics at the system level. The purpose of this article is to study the full scenarios of 5G communication access in the digital transformation of medium and large enterprises. This article starts with the development trend of wireless access networks, analyzes network slicing technologies in detail, and points out that wireless access networks are moving towards 5G. Important challenges are faced in the development of the times. Based on the adaptive random access model proposed in this paper, a feedback adaptive optimization method is proposed. This method estimates the real access load through feedback and calculates the access threshold through the access load. Then, broadcast the threshold value to each user equipment through downlink broadcast. The results show that the normalized throughput rate of random access methods using AC-RACH and FC-RACH is higher than that of RA-RACH. Especially, when the arrival rate of user requests is getting higher and higher, the throughput rate of RA-RACH access will continue to decrease with the collision until it approaches zero. By comparing the key data of normalized throughput rate and access success rate, it is concluded that the feedback adaptive method has a significant performance improvement under heavy load conditions compared with the previous method.

Exploiting Capture Diversity for Performance Enhancement of ALOHA-Based Multi-Static Backscattering Systems in the 6G Perspective

In this paper, we consider the emerging context of ALOHA-based multi-static backscattering communication systems. By assuming an architecture consisting of a set of passive backscattering nodes, an illuminator, and a set of spatially dislocated receivers, we firstly propose a cross-layer framework for performance analysis. The model jointly accounts for the shared wireless channel, including fading and capture effect, and channel contention strategy, which is regulated by a Framed Slotted ALOHA protocol. Furthermore, based on the inherent macroscopic diversity offered by the multi-static settings, we introduce the concept of capture diversity, which is shown to enable multiple packet detection in slots with multiple transmissions. In order to characterize the multiple access interference and approximate the capture probabilities, we enforce a log-normal approximation of the inverse Signal-to-Interference Ratio that relies on moment matching. Numerical results show the impact of deployment scenarios and the relative positions of illuminator, backscattering nodes, and receivers on the system normalized throughput. We show how the number of detection points impacts the system performance under various channel conditions. Moreover, the accuracy of the proposed approximation rationale is validated via Monte Carlo simulations. Finally, we analyze the optimal frame length in the presence of capture diversity.

Joint Spreading Factor and Channel Assignment in Multi-Operator LoRaWAN Deployments

LoRaWAN is a popular internet of things (IoT) solution over the unlicensed radio band. It sustains low-cost, durable, and long range IoT wireless communications. Nonetheless, with over 24 billion connected IoT devices being expected by the end of the year, and over 50 billion by 2025, the concurrent and legacy approaches to spreading factor and channel assignment in LoRaWAN networks can no longer keep up. This is exacerbated with the growing densification of IoT device deployments and, with the increasing requirements for better throughput and packet delivery ratios. In this paper, we propose a proportional fair-based joint optimal formulation for spreading factor and channel assignment in multi-operator LoRaWAN deployments. The objective of this problem is to maximize the total sum of the logarithmic normalized throughput. We split the problem into two subproblems, and propose a game theoretic approach to solving them. We prove that our games converge towards a pure Nash equilibrium and, afterwards, solve the optimization problems using both semi-distributed and completely distributed algorithms. Via simulations, we show that our algorithms greatly improve the total normalized throughput for LoRaWAN as well as the packet success rate, in comparison to the legacy approaches.

Analysis of Throughput and Delay for an Underwater Multi-DATA Train Protocol with Multi-RTS Reception and Block ACK

We propose an underwater multi-DATA train protocol with multi-RTS reception and block ACK (BACK) for underwater acoustic sensor networks. Due to long underwater acoustic propagation delay, some RTS frames may not overlap at a sink node, even if the RTS frames were sent to the sink node simultaneously by different sensor nodes. We consider that our underwater sink node can recover these nonoverlapping RTS frames. Since our RTS frame contains ID of the RTS sending node and a timestamp, the sink node calculates the propagation delay between the RTS sending node and the sink node, then broadcasts a CTS frame. Since our CTS frame contains when each RTS sending node can transmit a DATA frame to the sink node, multiple DATA frames transmitted by different sensor nodes can be formed as a train at the sink node. We also propose an underwater BACK protocol which is analogous to our proposed underwater multi-DATA train protocol. We analyze normalized throughput and mean access delay of our proposed protocols and the conventional protocols. The analytical and simulation results show that our analysis is accurate and our proposed protocols outperform the conventional protocols. Our proposed protocol can shorten the delay and increase the throughput via the multi-DATA train, multi-RTS reception, and BACK.

Bidirectional Relaying MMW Radio over Plastic Optical Fiber Systems in Home Area Networks

In this paper, we propose a novel home area networks (HAN) architecture using MMW radio-over-plastic optical fiber (MMW/RoPOF) to provide high-speed wireless connections to end users. The proposed HAN can support bidirectional half-duplex communication between any pair of mobile stations (MSs) thanks to the use of analog network coding in optical domain implemented at a center station (CS). We analyze the performance of an end-to-end communication link between two MSs under the impact of major physical impairments originated from both fiber and wireless links. The numerical results in terms of bit error rate (BER) and normalized throughput demonstrate that it is feasible to deploy MMW/RoPOF in HAN. In addition, useful information for the design of HAN can be obtained from this paper.

Asymptotically Maximal Throughput in Tandem Systems with Flexible and Dedicated Servers

For a system of two tandem queues with a finite intermediate buffer, we study the asymptotically maximal throughput as the number of servers in each station grows to infinity. First, we study the system with only dedicated servers, and then we examine the system with both dedicated and flexible servers. We assume that travel times between the two stations are negligible and that each server can only work on one customer at a time. We model the system as a birth–death Markov process, derive a closed form solution for the stationary distribution, and quantify the maximal asymptotic normalized throughput as the number of servers grows to infinity. We show that flexibility is more favorable for small systems, and as the number of servers grows, the benefits of flexibility decrease. Furthermore, we prove that when the number of servers goes to infinity, there is no need of flexibility at all, as the maximum value of the throughput is obtained. However, flexibility still has a secondary beneficial effect — a little flexibility (on the order of the square root of the number of dedicated servers at each station) guarantees that all dedicated servers are busy and results in faster convergence to the maximum throughput.

Wlan Performance Improvement by Header Compression and Packet Overhearing Reduction

For wireless systems, it is of vital importance to use the scarce radio resources in an efficient way. The main targets are to reduce the power consumption and increase the throughput of wireless devices. Using header compression both throughput can be increased and power can be saved. Using packet overhearing reduction power can be saved. This paper presents the achieved performance improvements when using these two methods. The simulation results are confirmed by analytical calculations. We show that we can achieve up to 35% power save when combining both methods and close to 0.1 additional normalized throughput.

Joint-Optimization Method of Detection Period and Detection Time with Spectrum Detection in Cognitive Radio

Spectrum detection is a critical technology in cognitive radio, which involves two very important parameters: detection period and detection time. Firstly, in order to improve spectrum efficiency and reduce the interference to authorized users, detection period optimization is adopted to maximize spectrum access opportunities and minimize the interference to authorized users. Secondly, under the target detection probability constraints, detection time optimization is adopted to maximize the achievable normalized throughput when CR user detects the channel. At last, we develop the joint-optimization algorithm of detection period and detection time. Compared with the method in literature [9], it can be found that our joint-optimization algorithm can actually improve spectrum access opportunities. Compared with single-optimization method of detection period and detection time, our algorithm can also greatly improve the achievable normalized throughput when CR user detects the channel