Applications of Soft Computing in Mobile and Wireless Communications

Soft computing is a synergistic combination of artificial intelligence methodologies to model and solve real world problems that are either impossible or too difficult to model mathematically. Furthermore, the use of conventional modeling techniques demands rigor, precision and certainty, which carry computational cost. On the other hand, soft computing utilizes computation, reasoning and inference to reduce computational cost by exploiting tolerance for imprecision, uncertainty, partial truth and approximation. In addition to computational cost savings, soft computing is an excellent platform for autonomic computing, owing to its roots in artificial intelligence. Wireless communication networks are associated with much uncertainty and imprecision due to a number of stochastic processes such as escalating number of access points, constantly changing propagation channels, sudden variations in network load and random mobility of users. This reality has fuelled numerous applications of soft computing techniques in mobile and wireless communications. This paper reviews various applications of the core soft computing methodologies in mobile and wireless communications

Multi-Armed Bandits for Spectrum Allocation in Multi-Agent Channel Bonding WLANs

While dynamic channel bonding (DCB) is proven to boost the capacity of wireless local area networks (WLANs) by adapting the bandwidth on a per-frame basis, its performance is tied to the primary and secondary channel selection. Unfortunately, in uncoordinated high-density deployments where multiple basic service sets (BSSs) may potentially overlap, hand-crafted spectrum management techniques perform poorly given the complex hidden/exposed nodes interactions. To cope with such challenging Wi-Fi environments, in this paper, we first identify machine learning (ML) approaches applicable to the problem at hand and justify why model-free RL suits it the most. We then design a complete RL framework and call into question whether the use of complex RL algorithms helps the quest for rapid learning in realistic scenarios. Through extensive simulations, we derive that stateless RL in the form of lightweight multi-armed-bandits (MABs) is an efficient solution for rapid adaptation avoiding the definition of broad and/or meaningless states. In contrast to most current trends, we envision lightweight MABs as an appropriate alternative to the cumbersome and slowly convergent methods such as Q-learning, and especially, deep reinforcement learning

Attention to Wi-Fi Diversity: Resource Management in WLANs with Heterogeneous APs

Many home networks integrate a small number (typically 2-4) of Wi-Fi Access Points (APs), with heterogeneous characteristics: different 802.11 variants, capabilities and security schemes. This paper proposes the consideration of these specific characteristics in order to improve the management of network resources. Three use cases are presented in order to showcase the potential benefits. By the use of a user-space AP, which works in coordination with a controller, the network is able to assign each connected station to the AP that best fits with its characteristics. The system also manages security, avoiding the need of adding specific elements for authentication, encryption or decryption. Extensions are proposed to an existing protocol that defines the communication between the AP and the controller, in order to communicate and store the specific characteristics of each AP and end device. This includes new association and handoff schemes that do not introduce any additional delay. The system has been implemented in a real environment, and a battery of tests has been run using three hardware platforms of different characteristics. The results show that handoffs between bands are possible, and estimate the processing delays, the Round-Trip Time and the handoff delay, which is small enough in order not to produce any significant disruption to the user (10-50 ms). Finally, the scenarios of interest have been replicated in a simulation environment, showing that significant benefits can be achieved if the specific characteristics of each AP and station are considered

Performance Evaluation of a Self-Organising Scheme for Multi-Radio Wireless Mesh Networks

Multi-Radio Wireless Mesh Networks (MR-WMN) can substantially increase the aggregate capacity of the Wireless Mesh Networks (WMN) if the channels are assigned to the nodes in an intelligent way so that the overall interference is limited. We propose a generic self-organisation algorithm that addresses the two key challenges of scalability and stability in a WMN. The basic approach is that of a distributed, light-weight, co-operative multiagent system that guarantees scalability. The usefulness of our algorithm is exhibited by the performance evaluation results that are presented for different MR-WMN node densities and typical topologies. In addition, our work complements the Task Group 802.11s Extended Service Set (ESS) Mesh networking project work that is in progress

Reinforcement Learning Approaches to Improve Spatial Reuse in Wireless Local Area Networks

The ubiquitous deployment of IEEE 802.11 based Wireless Local Area Networks (WLANs) or WiFi networks has resulted in dense deployments of Access Points (APs) in an effort to provide wireless links with high data rates to users. This, however, causes APs and users/stations to experience a higher interference level. This is because of the limited spectrum in which WiFi networks operate, resulting in multiple APs operating on the same channel. This in turn affects the signal-tonoise-plus interference ratio (SINR) at APs and users, leading to low data rates that limit their quality of service (QoS). To improve QoS, interference management is critical. To this end, a key metric of interest is spatial reuse. A high spatial reuse means multiple transmissions are able to transmit concurrently, which leads to a high network capacity. One approach to optimize spatial reuse is by tuning the clear channel access (CCA) threshold employed by the carrier sense multiple access with collision avoidance (CSMA/CA) medium access control (MAC) protocol. Specifically, the CCA threshold of a node determines whether it is allowed to transmit after sensing the channel. A node may increase its CCA threshold, causing it to transmit even when there are other ongoing transmissions. Another parameter to be tuned is transmit power. This helps a transmitting node lower its interference to neighboring cells, and thus allows nodes in these neighboring cells to transmit as well. Apart from that, channel bonding can be applied to improve transmission rate. In particular, by combining/aggregating multiple channels together, the resulting channel has a proportionally higher data rate than the case without channel bonding. However, the issue of spatial reuse remains the same whereby the focus is to maximize the number of concurrent transmissions across multiple channels

Nonlinear Negotiation Approaches for Complex-Network Optimization: A Study Inspired by Wi-Fi Channel Assignment

At the present time, Wi-Fi networks are everywhere. They operate in unlicensed radio-frequency spectrum bands (divided in channels), which are highly congested. The purpose of this paper is to tackle the problem of channel assignment in Wi-Fi networks. To this end, we have modeled the networks as multilayer graphs, in a way that frequency channel assignment becomes a graph coloring problem. For a high number and variety of scenarios, we have solved the problem with two different automated negotiation techniques: a hill-climber and a simulated annealer. As an upper bound reference for the performance of these two techniques, we have also solved the problem using a particle swarm optimizer. Results show that the annealer negotiator behaves as the best choice because it is able to obtain even better results than the particle swarm optimizer in the most complex scenarios under study, with running times one order of magnitude below. Finally, we study how different properties of the network layout affect to the performance gain that the annealer is able to obtain with respect to the particle swarm optimizer.Comment: This is a pre-print of an article published in Group Decision and Negotiation. The final version is available online at https://doi.org/10.1007/s10726-018-9600-