RETRACTED ARTICLE: Scenario Classification of Wireless Network Optimization Based on Big Data Technology

The layering principle has been long identified as a way to increase the interoperability and to improve the design of telecommunication protocols, where each layer offers services to adjacent upper layers and requires functionalities from adjacent lower ones. In the past, layering has enabled fast development of interoperable systems, but at the same time limited the performance of the overall architecture, due to the lack of coordination among layers. This issue is particularly relevant for wireless networks, where the very physical nature of the transmission medium introduces several performance limitations for protocols designed for wired networks. To overcome these limitations, a modification of the layering paradigm has been proposed, namely, cross-layer design, or “cross-layering.” Several cross-layering approaches have been proposed in the literature so far. Nevertheless, little formal characterization of the cross-layer interaction among different levels of the protocol stack is available yet. A clear need exists for identifying approaches able to analyze and provide quantitative guidelines for the design of cross-layer solutions, and, more importantly, to decide, in each case, whether cross-layering represents an effective solution or not. This chapter provides a detailed survey of the state-of-the-art and future directions in the usage of formal methods for cross-layer modeling and optimization of wireless networks. The text starts by detailing the principles of layered (ISO/OSI and TCP/IP) protocol stacks as well as the cross-layer paradigm. An overview of the architectures of existing and perspective wireless networks is presented along with an analysis of the potential limitations deriving from the layering approach and detailed description of possible optimization solutions enabled by cross-layer design. Subsequent sections are devoted to the issues of modeling and optimization of wireless networks. The remaining sections cover performance optimization as well as architecture optimization (specifically in terms of signaling). The chapter ends with a summary and outlines about future directions of research on the topic.

Cross-layer optimization (physical, data link and network layers) in wireless networks has been limited by its combinatorial complexity (Toumpis and Goldsmith, 2003). Existing literature has traditionally been able to provide numerical analysis only for networks with less than 8 nodes (Madan et al., 2005), limiting our ability to understand the benefits of cross-layer optimization for more realistic-sized networks. In Barreto (2004) and Johansson and Xiao (2006) the authors showed that column generation could be used to reduce the complexity of the problem, with Johansson and Xiao (2006) providing in-depth analysis for 10-node networks and limited results for 20-node networks - all with minimum SNR and transmission rate restrictions. In this paper we show how decomposition methods can be used for the joint optimization of the physical (rate and power control), data link (MAC/scheduling), and networking (routing) layers, increasing the size of networks that can be analyzed. We present exact numerical solutions for ad-hoc networks with up to 30 nodes and mesh networks with up to 40 nodes, a significant scalability improvement. We continue by showing how bounded solutions for larger networks can be obtained with decreased computational complexity and present to heuristic implementations that increase the analysis to arbitrary-sized networks (Barreto and Chiu, 2006). We show how decomposition methods effectively decouple routing from the MAC/physical layers and allow us to solve the problems independently while reaching a global optimal solution.

Base station location selection and network optimization are critical to improving the performance of wireless communication networks in terms of latency reduction. To this end, the article proposes leveraging a convolutional neural network (CNN) to improve the accuracy of base station location selection and network latency reduction. The CNN method, based on a three-dimensional representation including signal strength data set, network topology data set, and transmission path data set, is used to select base station location and optimize the multihop relay network for latency reduction. The article presents a following method: location selection and network optimization for the wireless communication network. First, it collects the experimental data set of base station location selection and network optimization, and then uses the training data to train the CNN model to extract features. Once the training is done, the article further optimizes the network parameters and configurations, and ultimately obtains the optimal base station location and network configuration while minimizing network latency. As a result, simulation results indicate that the CNN model has remarkable performance in base station location selection, as well as in network optimization. In summary, the feature extraction and processing ability of CNN are powerful, enabling it to effectively capture factors leading to delay, hence improving the performance of base station location selection and network optimization. The article also demonstrates that the CNN model can be adjusted according to different environments and scenario settings through dynamic tuning.

6G mobile network technology will set new standards to meet performance goals that are too ambitious for 5G networks to satisfy. The limitations of 5G networks have been apparent with the deployment of more and more 5G networks, which certainly encourages the investigation of 6G networks as the answer for the future. This research includes fundamental privacy and security issues related to 6G technology. Keeping an eye on real-time systems requires secure wireless sensor networks (WSNs). Denial of service (DoS) attacks mark a significant security vulnerability that WSNs face, and they can compromise the system as a whole. This research proposes a novel method in blockchain 6G-based wireless network security management and optimization using a machine learning model. In this research, the deployed 6G wireless sensor network security management is carried out using a blockchain user datagram transport protocol with reinforcement projection regression. Then, the network optimization is completed using artificial democratic cuckoo glowworm remora optimization. The simulation results have been based on various network parameters regarding throughput, energy efficiency, packet delivery ratio, end–end delay, and accuracy. In order to minimise network traffic, it also offers the capacity to determine the optimal node and path selection for data transmission. The proposed technique obtained 97% throughput, 95% energy efficiency, 96% accuracy, 50% end–end delay, and 94% packet delivery ratio.

The radio network planning in TETRA digital trunking communication system including cover planning, capacity planning, frequency planning and the related optimizing technology and method such as capacity optimization and cell reselection is specifically stated in this paper. The technology and method on radio network planning and optimization is also analyzed and expressed by the practical application of Beijing TETRA digital trunking communication network.

This paper studies joint scheduling and network coding in wireless multicast networks with independent sources. Designing a network coding solution for wireless networks involves scheduling interference-free transmissions and optimizing a performance measure for the predetermined scheduling sets, followed by constructing network codes for the specific solution. In such a design process, the results of prior steps need to provide enough information to the subsequent steps. In this paper, we formulate a linear optimization problem whose results can be used to design a coding solution. We build our work on previous works and present statistics that show the importance of incorporating unequal timeshares in designing network codes. In particular, our statistics show a throughput improvement of about 35% in maximum flow problems and energy savings between 13% and 30%, depending on the network size, in minimum energy problems. We also present the requirements of code construction algorithms for wireless networks that capture the broadcast property of these networks and can design proper codes when timeshares are unequal. In particular, the adaptation of a centralized network coding scheme for wireless networks is discussed. Overall, the work reported here provides a three-step solution to derive network codes that optimize a performance criterion of interest while also solving the scheduling problem in multihop wireless networks.

In recent times, the application of autonomic soft computing techniques for design and optimization of wireless access networks is progressively becoming prevalent. These computational learning techniques are capable of handling uncertain and imprecise networking information while driving toward the optimal solution set in the problem search space. The approach proposed by this paper presents the application of the fuzzy logic inference combined with the evolutionary genetic algorithm to optimize the performance parameters in wireless networks. In particular, we consider optimal bit rate allocation and transmission power consumption through the joint design of fuzzy-genetic modeling framework. The sample training data generated through simulations of IEEE 802.11 wireless access network are analyzed for optimization by supplying it to the expert hybrid model comprising of the conjunctive design of both the computational intelligent techniques. Specifically, we contemplate the binary encoding scheme, single-point crossover, reversing mutation, and two fitness functions for executing the binary genetic operations of crossover and mutation. It is generally observed that the proposed hybrid model with polynomial fitness function yields better performance with scalable network datasets than the logarithmic fitness function in terms of higher objective value. Moreover, the results obtained through simulation experiments exhibit significant throughput gains and power efficiency for the deployed fitness functions with the evolving size of training dataset. Compared with the existing methods, our hybrid learning model demonstrates performance enhancement with higher expected fitness measure, improved throughput and power efficiency.

Age of Information (AoI) is an emerging concept to model information freshness from the perspective of destinations of information deliveries. Serving as a metric to characterize the data delivery timeliness, the peak age indicates the maximum value of the AoI prior to a data packet reception. In this paper, we present a distributed scheduling algorithm for peak age optimization in a wireless network where a number of sensor nodes attempt to deliver their sensed data to a data collector over a wireless channel. In particular, each sensor node accesses the channel for data delivery independently according to an adaptively tuned transmission probability. The beauty of our algorithm lies in that, even with neither centralized infrastructure nor coordinations among the sensor nodes, our algorithm asymptotically approximates the optimal solution by only a constant factor. We perform solid theoretical analysis and extensive simulations to verify the efficacy of our algorithm. To the best of our knowledge, it is the first fully distributed scheduling algorithm for AoI optimization in wireless networks.

Cyber-Physical System (CPS) is envisioned to tightly integrate the cyber-world of computation, communication, and control with the physical world. CPS is typically designed as a networked system of interacting sensors, actuators, and embedded computing devices to monitor and control the physical world. Thus, one of the essential building blocks of such a system is a highly efficient networking infrastructure. In this paper, we aims to develop an efficient wireless networking technology which can be utilized in CPS. More specifically, we develop a cross-layer optimization model based on the Network Utility Maximization (NUM) framework and its distributed solution for wireless multihop multicast networks exploiting multi-user diversity. It is known that the capacity of a wireless network can be increased by exploiting different channel conditions at different users, i.e., multi-user diversity; however, it is yet to be determined how much performance gain can be achieved by exploiting multi-user diversity in wireless multihop multicast networks. To address this problem, we extend the NUM framework and derive a new optimization problem including the benefits of multi-user diversity for multicasting scenarios in wireless multihop networks under a probabilistic media access control (MAC). In our problem, multi-user diversity is achieved via opportunistic scheduling. Then, we propose a distributed approximation algorithm for the problem. Our numerical results confirm that the benefit of multi-user diversity is prominent in a wireless multihop network with multicast flows.

The paper addresses the problem of inference and optimization in wireless networks. An information-geometric framework is presented with scalable solutions to infer the end-to-end rate distributions of stochastic network flows from link rate measurements. Low-complexity solutions are provided by decomposing the network inference problem to smaller-size subproblems that are solved independently. By using the inferred distributions of flow rates, a recursive mechanism is presented to optimize the network performance. The distributions of all links rates are computed first from the inferred distributions of flow rates and then the weighted sum of link outage probabilities is minimized by adapting routing decisions. This approach is iterated between network inference and optimization, and provides link outage and end-to-end throughput gains compared to static approaches with fixed network parameters. The joint network inference and optimization approach is implemented with real radios and the performance is verified with hardware-in-the-loop emulation test results obtained with actual radio transmissions over emulated channels.

In the design of modern mobile wireless networks the effort for performance improvement has to be balanced with the effort in more effective energy management strategies. In fact, the capacity of batteries is not increasing as new devices and multimedia applications requires. In this paper a new approach to routing optimization in ad-hoc wireless networks supporting multimedia communication is investigated. It exploits an energy model for the radio interface and cross-layer information to dynamically determine routing paths connecting wireless nodes, in order to reduce energy consumption and prolong network lifetime, while preserving quality of service. A network architecture based on a modified 802.11a wireless physical interface with multi-channel capability is considered. We investigate a multi-hop environment where network devices collect cross- layers information and periodically update a central repository located on a network node elected as network coordinator. The coordinator exploits the recorded information to provide best quality routing paths to devices that need to send sensible data over the network. We propose here an algorithm for routing path optimization and compare the optimized strategy with a reference implementation of the OLSR routing protocol. The numerical results show benefits in terms of increased network life-time.

The fifth-generation (5G) is coming. Quo vadis 5G? What architectures, network topologies and technologies will define 5G? Are methodologies to the analysis, design and optimization of current cellular networks still applicable to 5G? The proposed tutorial is intended to offer a comprehensive and in depth crash course to communication professionals and academics. It is aimed to critically illustrate and discuss essential and enabling transmission technologies, communication protocols and architectures that are expected to make 5G wireless communication networks a reality. More specifically, the present tutorial is focused on illustrating the critical and essential importance of channel and spatial models for an accurate system-level analysis and optimization of 5G networks, which are expected to use different frequency bands compared to state-of-the-art networks and to rely on a much denser deployment of access points and antenna-elements, to a scale that has never been observed in the past.

In this letter, we investigate the adaptive resource allocation with Lyapunov optimization for time-varying wireless networks. The transmit power minimization is characterized by a stochastic optimization model. A dynamic resource allocation (DRA) algorithm is developed to accommodate the wireless network dynamics, i.e., time-varying wireless channels and as well the queuing dynamics. We investigate the tracking error between the DRA algorithm output and the target optimal resource allocation solution. Based on these results, we further develop an adaptive-compensation resource allocation (ACRA) algorithm, which iterates only once when the network state changes for saving the huge iteration overheads. Finally, we determine a sufficient condition that the ACRA algorithm asymptotically tracks the moving equilibrium point with no tracking errors. Simulation results validate the theoretical analysis of our proposed scheme.

Path loss models are often used by radio network engineers to predict signal coverage, optimize limited network resources, and perform interference feasibility studies. However, the propagation mechanisms of electromagnetic waves depend on the physical characteristics of the wireless channel. Therefore, efficient radio network planning and optimization requires detailed information about the specific propagation environment. In this data article, the path loss data and the corresponding information that are needed for modeling radio wave propagation in smart campus environment are presented and analyzed. Extensive drive test measurements are performed along three different routes (X, Y, and Z) within Covenant University, Ota, Ogun State, Nigeria (Latitude 6°40′30.3″N, Longitude 3°09′46.3″E) to record path loss data as the mobile receiver moves away from each of the three 1800 MHz base station transmitters involved. Also, the longitude, latitude, elevation, altitude, clutter height, and the distance information, which describes the smart campus environment, are obtained from Digital Terrain Map (DTM) in ATOLL radio network planning tool. Results of the first-order descriptive statistics and the frequency distributions of all the seven parameters are presented in tables and graphs respectively. In addition, correlation analyses are performed to understand the relationships between the network parameters and the terrain information. For ease of reuse, the comprehensive data are prepared in Microsoft Excel spreadsheet and attached to this data article. In essence, the availability of these data will facilitate the development of path loss models for efficient radio network planning and optimization in smart campus environment.

This invited paper outlines some recent results on the max-min SIR balancing problem in wireless networks in which power control and beamforming are the only mechanisms for resource allocation and interference management. In addition, we describe several potential extensions and improvements to existing algorithmic solutions, as well as prove the convergence of a distributed algorithm for joint power control and receive beamforming to a global optimum of the max-min SIR balancing problem. Finally we briefly discuss a possibility of how to incorporate the optimization of transmit beamformers.