Power Allocation and Capacity Analysis for FBMC-OQAM With Superimposed Training

Abstract

Superimposed training (ST) is a semiblind channel estimation technique, proposed for orthogonal frequency division multiplexing (OFDM), where training sequences are added to data symbols, avoiding the use of dedicated pilot-subcarriers, and increasing the available bandwidth compared with pilot symbol assisted modulation (PSAM). Filter bank multicarrier offset quadrature amplitude modulation (FBMC-OQAM) is a promising waveform technique considered to replace the OFDM, which takes advantage of well-designed filters to avoid the use of cyclic prefix and reduce the out-band-emissions. In this paper, we provide the expressions of the average channel capacity of the FBMC-OQAM combined with either PSAM or ST schemes, considering imperfect channel estimation and the presence of the pilot sequences. In order to compute the capacity expression of our proposal, ST-FBMC-OQAM, we analyze the channel estimation error and its variance. The average channel capacity is deduced considering the noise, data interference from ST, and the intrinsic self-interference of the FBMC-OQAM. Additionally, to maximize the average channel capacity, the optimal value of data power allocation is also obtained. The simulation results confirm the validity of the capacity analysis and demonstrate the superiority of the ST-FBMC-OQAM over existing proposals.

Differential Data-Aided Channel Estimation for Up-Link Massive SIMO-OFDM

“…In these equations, denotes the number of bits in one packet, , is given by (20) and , by (12). The efficiencies of CDS and NCDS in terms of the occupied resources in the unit block are…”

“…In order to obtain the best pair of constellation sizes ( and ) for a given scenario with some specific parameters, the throughput given in (29)-(31) should be maximized. Due to the difficulty to find closed-form expressions from (12) and (20) and motivated by the fact that the search space is very small (log 2 (max( )) × log 2 (max( )) combinations), we propose to resort to an exhaustive search over these few options. The throughput expressions are evaluated for a particular scenario and the values that provide the maximum throughput given in (29) are chosen.…”

“…Moreover, the computational complexity of blind and semi-blind techniques is generally higher than PSAM's due to their iterative structure. Another alternative is superimposed training (ST) [11], [12], which is able to effectively alleviate the complexity of blind techniques, where the data and pilots share the same time-frequency resources. Hence, the spectral efficiency is increased at the expense of degrading the quality of the channel estimates due to the presence of data self-interference.…”

“…The ST scheme of [12] is also taken into account for the comparison of throughput. This scheme has a parameter denoted as that indicates the power allocated for the superimposed pilot symbols, while 1 − corresponds to the power of the data symbols.…”

“…This scheme has a parameter denoted as that indicates the power allocated for the superimposed pilot symbols, while 1 − corresponds to the power of the data symbols. According to [12], we set = 0.2 which is the most frequently used value in the literature. ST requires an averaging process, which is implemented in both time and frequency dimensions and the number of resources to average is dynamically adapted for different values of Doppler and delay spreads to avoid the degradation of the channel estimates due to these effects.…”