Formal Methods in Cross Layer Modeling and Optimization of Wireless Networks

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.

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 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.

With the multi network cooperative development of 4G, 3G and 2G, it is an essential and important task to comprehensively identify and classify the network optimization scenarios in the light of coverage, services, interference, malfunction and resource allocation from geography dimension on each layer, which can directly have an impact on the programs and strategies of network optimization and planning at each level. Meanwhile, precise identification of network optimization scenarios can bring an important guiding significance to the wireless network scheming, planning and optimization. In the network optimization of different scenarios, even with the same network quality, the parameters selection or adjustment during the optimization process may be different, namely, there should be the different parameter optimization models to match to different environments. Based on machine learning and big data technology, this study analyzes the wireless network multi-dimensional attributes and realize the automate identification and classification for cells under different scenarios. As such, the purpose of quantifying and identifying wireless network scenarios can be achieved.

Cross-layer optimization for wireless mesh networks with smart antennas

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.

This paper proposes a distributed optimization framework for wireless multihop sensor networks base on a game theoretic approach. We show that the cross-layer optimization problem can be decomposed into two subproblems corresponding to two separate layers (the physical and the application layers) of the overall system. By modelling each subproblem as a noncooperative game, we aim to solve the nonconvex application-layer rate- allocation and physical-layer power-allocation subproblems in a distributed manner. Further, we prove the existence, uniqueness, and stability of the Nash equilibria for both games under certain sufficient conditions. Finally, we show that by using a set of dual variables as the market prices to coordinate the physical layer supply and the application layer demand, the overall optimization process strikes a right balance between the two layers in an overall cross-layer design.

Wireless communication links are time-varying in nature, causing degradation of network's performance; this however, could be alleviated by allowing cross layer interaction in the protocol stack. Hence, optimization problems formulated in this paper considered parameters from the physical, MAC, and network layers where an optimal transmission power vector that minimizes the overall transmission power of the network, satisfying the SINR, required data rate, and maximum transmission power constraints was formulated to exist. Distributed algorithms for the joint scheduling, power control and routing were also derived and an energy-aware routing algorithm which maximizes network lifetime was proposed. The energy consumed by each node in the routing path and delays associated on each transmission were considered as link cost such that a route with minimum link cost is utilized. Basically, the routing algorithm searched an energy-efficient route, satisfying the energy constraint of each node in a routing path at a tolerable delay.

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.

Multimedia transmissions over wireless networks need continuous media play out. In order to meet best Quality of Services, higher layers data depend on lower layer parameters. That is readily known as cross-layer design. Our cross-layer design model optimizes the physical layer, MAC layer and application layer. Video summary frames are transmitted via MIMO transmit/receive diversity with AMC at the physical layer and Payload and data rate adaptation at the MAC link layer and source coding at the application layer. We, first model the finite-state Markov chain (FSMC) for the physical layer service and based on that, we then characterize the Lagrangian relaxation and Dynamic programming to find the optimal shortest path. The source coding as well as retransmission requests are based on the channel-state information (CSI) and the system controller is implemented in all the three physical, MAC layer and application layers. The numerical results revealed that our proposed cross-layer design can efficiently achieve the effective throughput and distortion gain.

This paper considers a general stochastic resource allocation problem that arises widely in wireless networks, cognitive radio networks, smart-grid communications, and cross-layer design. The problem formulation involves expectations with respect to a collection of random variables with unknown distributions, representing exogenous quantities such as channel gain, user density, or spectrum occupancy. The problem is solved in dual domain using a constant step-size stochastic dual subgradient descent (SDSD) method. This results in a primal resource allocation subproblem at each time instant. The goal here is to characterize the non-asymptotic behavior of such stochastic resource allocations in an almost sure sense. This paper establishes a convergence rate result for the SDSD algorithm that precisely characterizes the trade-off between the rate of convergence and the choice of constant step size e. Towards this end, a novel stochastic bound on the gap between the objective function and the optimum is developed. The asymptotic behavior of the stochastic term is characterized in an almost sure sense, thereby generalizing the existing results for the stochastic subgradient methods. As an application, the power and user-allocation problem in device-to-device networks is formulated and solved using the SDSD algorithm. Further intuition on the rate results is obtained from the verification of the regularity conditions and accompanying simulation results.

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.

The purpose is to realize the intelligent reform of piano online teaching and the intelligent optimization of wireless networks. Empirical research is realized with quantitative research and algorithm simulation as the starting point. First, regression fitting algorithm and Relief F weight algorithm are adopted to extract the effectiveness of each characteristic variable. Next, under the guidance of metric learning theory, K‐Nearest Neighbors (KNN) in Projected Feature Space (P‐KNN) algorithm is proposed to complete the hierarchical recognition of piano teaching influence features. Metric Learning With Support Vector Machine (ML‐SVM) classification algorithm is employed to identify the feature performance affecting piano teaching. Finally, the performance of P‐KNN algorithm and ML‐SVM algorithm is compared with KNN algorithm and Information‐Theoretic‐Metric‐Learning (ITML) algorithm. It is concluded that the recognition accuracies of P‐KNN and ML‐SVM are 82.78% and 83.97%, respectively. Based on the quantitative research on the characteristics affecting piano teaching, artificial intelligence and wireless network optimization are combined to explore the implementation path of intelligent technology in piano teaching reform, reflect the use value of modern science and technology in piano teaching, and innovate the process of music online education reform of piano teaching.

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.