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TABLE II: QL-AMC Parameters subcarriers and a su bearrier spacing Af = 120KHz. We con- sider the channel model defined in (5). The path loss follows a urban macro (UMa) model with non-line-of-sight (NLOS). Shadowing is mode ed according to a log-normal distribution with standard deviation of 6 dB [18]. The noise power is fixed at —123.185 dBm. A summary of the main simulation parameters is provid proposed QL-AMC ed in Table I, while the parameters of the algortihm are listed in Table II.

Table 2 II: QL-AMC Parameters subcarriers and a su bearrier spacing Af = 120KHz. We con- sider the channel model defined in (5). The path loss follows a urban macro (UMa) model with non-line-of-sight (NLOS). Shadowing is mode ed according to a log-normal distribution with standard deviation of 6 dB [18]. The noise power is fixed at —123.185 dBm. A summary of the main simulation parameters is provid proposed QL-AMC ed in Table I, while the parameters of the algortihm are listed in Table II.

Related Figures (8)

Fig. 1: Model of time scheduling of operations.
Fig. 1: Model of time scheduling of operations.
We compare the QL-AMC against the AMC based on a fixed look-up table [3] and also against the OLLA technique from [7]. In the fixed look-up table approach, a static mapping
We compare the QL-AMC against the AMC based on a fixed look-up table [3] and also against the OLLA technique from [7]. In the fixed look-up table approach, a static mapping
TABLE I: Simulation Parameters
TABLE I: Simulation Parameters
Fig. 4: Basic diagram of the proposed AMC scheme
Fig. 4: Basic diagram of the proposed AMC scheme
TABLE III: Deployment Phase Results (Average over 200 runs) Fig. 5: CDF of average spectral efficiency (bps/Hertz)
TABLE III: Deployment Phase Results (Average over 200 runs) Fig. 5: CDF of average spectral efficiency (bps/Hertz)
Related topics:
MathematicsComputer ScienceReinforcement LearningLink AdaptationSpectral Efficiency
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