Recent studies have shown that the proliferation of wireless applications and services, experienced in the last decade, is leading to the challenging spectrum shortage problem.
We provide a general overview regarding the spectrum shortage problem from the point of view of different technologies.
First, we propose solutions based on multi-radio multi-channel wireless mesh networks in order to improve the usage of unlicensed wireless resources.
Then, we move our focus on cognitive networks in order to analyze issues and solutions to opportunistically use licensed wireless resources.
In wireless mesh networks, the spectrum shortage problem is addressed equipping each device with multiple radios which are turned on different orthogonal channels.
We propose G-PaMeLA, which splits in local sub-problems the joint channel assignment and routing problem in multi-radio multi-channel wireless mesh networks.
Results demonstrate that G-PaMeLA significantly improves network performance, in terms of packet loss and throughput fairness compared to algorithms in the literature.
Unfortunately, even if orthogonal channels are used, wireless mesh networks result in what is called spectrum overcrowding.
In order to address the spectrum overcrowding problem, careful analysis on spectrum frequencies has been conducted.
These studies identified the possibility of transmitting on licensed channels, which are surprisingly underutilized.
With the aim of addressing the resources problem using licensed channels, cognitive access and mesh networks have been developed.
In cognitive access networks, we identify as the major problem the self-coexistence, which is the ability to access channels on a non-interfering basis with respect to licensed and unlicensed wireless devices.
We propose two game theoretic frameworks which differentiate in having non-cooperative (NoRa) and cooperative (HeCtor) cognitive devices, respectively.
Results show that HeCtor achieves higher throughput than NoRa but at the cost of higher computational complexity, which leads to a smaller throughput in cases where rapid changes occur in channels' occupancy.
In contrast, NoRa attains the same throughput independent of the variability in channels' occupancy, hence cognitive devices adapt faster to such changes.
In cognitive mesh networks, we analyze the coordination problem among cognitive devices because it is the major concern in implementing mesh networks in environments which change in time and space.
We propose Connor, a clustering algorithm to address the coordination problem, which establishes common local control channels.
Connor, in contrast with existing algorithms in the literature, does not require synchronization among cognitive mesh devices and allows a fast re-clustering when changes occur in channel's occupancy by licensed users.
Results show that Connor performs better than existing algorithms in term of number of channels used for control purposes and time to reach and stay on stable configurations.