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To handle the issue of optimum spectral efficiency, efficient allocation of spectrum resources is essential among D2D users.
In a wireless communication environment characterized by dynamic channels, high influence of interference, bandwidth shortage and strong demand for quality of service (QoS) support, the challenge for achieving optimum spectral efficiency and high data rate is unprecedented.
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Considering the results altogether, it can be concluded that the proposed algorithm SA2 outperforms the benchmark algorithms and achieves near optimum performance in both spectral efficiency and fairness.
In this regard, in [13] we observed that the optimum α1 in terms of spectral efficiency is a random variable that depends on the particular scenario and the target function to be maximized.
In order to evaluate the channel utilization in terms of average spectral efficiency, we simulate an optimum sum rate maximization strategy.
Until recently researches have used spectral efficiency (SE) as a metric for optimum design of networks and did not pay attention to the energy efficiency (EE) of the network.
Conversely, large values for idx are indicative of a situation in which TCP can keep the queue full all the time despite wireless losses, and the optimum policy is to use the MCS with the best spectral efficiency but with larger wireless loss rates.
For TS 2, the optimum joint power allocation and subcarrier scheduling scheme, maximizing the system spectral efficiency is identified by solving the following optimization problem: (6).
Denoting by SEI, the spectral efficiency indicator, we have 0≤SEI≤1 with SEI =1 being the optimum.
In Figure 4, it can be observed that, for each transmission strategy, there exists an optimum value for the transmit duration phases so as to maximize the cellular spectral efficiency in average.
One subcarrier allocation criterion (SA2) has shown better performance in spectral efficiency and fairness comparing to the other criterion (SA1), and achieves near optimum performance.
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