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We proposed a data-precoded algorithm for multiple-antenna L RA based systems, which ensures spatial diversity, while maximizing spectral efficiency.
We study the problem of maximizing spectral efficiency of cognitive radio network deployments subject to an interference constraint and under specific quality of service (QoS) guarantees.
In this paper, we address the routing, wavelength assignment, and spectrum allocation problem (RWSA) in transparent FWDM networks with the objective of maximizing spectral efficiency.
Depending on the awaited quality of service, the CR can identify several criteria to optimize, such as, minimizing the bit error rate, minimizing energy consumption, maximizing spectral efficiency, etc.
In this context, this work addresses LTE-A CA and proposes an updated integrated Common Radio Resource Management (iCRRM), from [11], that performs CC scheduling to satisfy the user's quality of service (QoS) and experience (QoE) requirements while maximizing spectral efficiency.
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The employed resource allocation (RA) algorithms allocate the user packets to the available radio resources in order to satisfy the user requirements and to ensure efficient packet transport, e.g. minimize loss or maximize spectral efficiency.
The latter ensure orthogonality in real domain to maximize spectral efficiency.
We also present a closed form calculation of the optimal symbol period that maximizes spectral efficiency.
In order to maximize spectral efficiency of the offset QAM OQAMmodulationion, the symbol period T is halved.
To maximize spectral efficiency, then macro and femto base stations have to work in shared spectrum (or co-channel deployment), interfering with each other.
In [20], the authors examined a unified adaptive modulation approach where the data rate, transmitted power, and instantaneous BER were varied to maximize spectral efficiency.
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