Abstract
In this letter, we optimize the pilot overhead in communication with retransmission and investigate the dependence of this overhead on various system parameters, e.g., fading rate, target rate, and signal-to-noise ratio. We formalize the optimization and frame it in terms of throughput maximization; this leads to an accurate optimal pilot overhead closed-form expression. Results show that the optimal overhead expressions are roots of quartic polynomials. At a high-power regime, they decrease with the target rate and increase with the normalized Doppler frequency, and at low-power regime, they are only function of the target rate.
Highlights
Wireless networks must significantly improve their information rate in order to meet the increasing demand for highspeed data services
One key limit to the information rate is low packet efficiency: a packet typically carries less than 60% to 70% of actual data
We used the throughput formulation – not the ergodic capacity formulation – derived in our previous paper [1]. This throughput formulation led to a numerical computation of the optimal pilot overhead
Summary
Wireless networks must significantly improve their information rate in order to meet the increasing demand for highspeed data services. By allocating fewer resources to training, the number of re-transmissions may increase and the performance may be dominated by the number of re-transmissions To investigate this tradeoff, we used the throughput formulation – not the ergodic capacity formulation – derived in our previous paper [1]. The prior works – posed on maximization of the ergodic channel capacity – showed that one would select the number of pilot symbols to be as small as possible assuming that the SNR is sufficiently high [5], [8], [9] This small number can be justified : the resources allocated to training directly reduce the amount of data in the forward channel, reducing the rates in the pre-log factor outside the log(1 + SNR) (SNR is Signal to Noise Ratio) of capacity expressions for Gaussian channels. With our throughput formulation [1], the SNR-gain due to training improves rates inside the log and reduces the number of re-transmissions, which leads to more realistic tradeoffs
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