Jointly enhancing both energy efficiency (EE) and spectrum efficiency (SE) of modulation schemes becomes one of the main issues for 5G mobile communications. Recently, an indexed modulation (IM) technique provides an interesting tradeoff between EE and SE. Data can be conveyed through the combination of subcarriers pattern that can be divided between activated/non-activated subcarriers in the frequency domain. Maximum SE can be attained at half subcarrier activation, hence producing symbols with half energy of the conventional orthogonal frequency-division multiplexing (OFDM) system. In this paper, alternatively, the new concept of sparsely indexing modulation (SIM) on overall subcarrier space is clarified. Sparse (few) subcarrier activations provide much higher EE, while the combinatorial indexing of the sparse subcarriers on the overall subcarriers as a single group spans huge combinatorial space that provides approximately the same SE of the plain OFDM system. The fallacy of indexing difficulty on overall subcarrier space without grouping is resolved. Moreover, a further SE improvement is suggested by introducing permutation-based indexing and combinatorial indexing on over-complete dictionaries. Sparsely indexing represents the cornerstone, which enables compressive sensing tools to enforce IM gains. Based on the conducted simulations, the proposed SIM scheme outperforms the conventional OFDM system in terms of the error performance, the peak-to-average power ratio, and the EE with the same spectral efficiency without channel coding complexity. The proposed SIM scheme is considered one of the energy savingoriented modulations. INDEX TERMS Index modulation, sparse index modulation, OFDM, OFDM-IM, double data/channel sparsity, critical sparsity, combinatorial/permutational indexing, overcomplete/non-orthogonal dictionary indexing, green modulation. The associate editor coordinating the review of this manuscript and approving it for publication was Junaid Shuja. better system processing under compressive sensing (CS) based signal processing approaches. A. GREEN MODULATION Green cellular network relays on the integration of many strategies for minimizing energy at both the base station (BS) and the user equipment (UE) [1], [2]. The growing tendency for employing an energy efficient communication network is accompanied with encountering the unlimited growth in data demands/network capacity [3]. Saving in signal transmission (green radio) represents an essential aspect affecting the overall energy saving. Modulation schemes aims at maximizing both spectral efficiency (SE) and the energy efficiency (EE)
Unconstrained text recognition is an important computer vision task, featuring a wide variety of different sub-tasks, each with its own set of challenges. One of the biggest promises of deep neural networks has been the convergence and automation of feature extractors from input raw signals, allowing for the highest possible performance with minimum required domain knowledge. To this end, we propose a data-efficient, end-to-end neural network model for generic, unconstrained text recognition. In our proposed architecture we strive for simplicity and efficiency without sacrificing recognition accuracy. Our proposed architecture is a fully convolutional network without any recurrent connections trained with the CTC loss function. Thus it operates on arbitrary input sizes and produces strings of arbitrary length in a very efficient and parallelizable manner. We show the generality and superiority of our proposed text recognition architecture by achieving state of the art results on seven public benchmark datasets, covering a wide spectrum of text recognition tasks, namely: Handwriting Recognition, CAPTCHA recognition, OCR, License Plate Recognition, and Scene Text Recognition. Our proposed architecture has won the ICFHR2018 Competition on Automated Text Recognition on a READ Dataset.
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