Different from traditional communication systems, information-centric Internet of things (IC-IoT) as a novel smart paradigm needs to guarantee end-to-end connectivity for rapidly growing smart devices. How to meet the demands of massive connection has become a key problem for IC-IoT. Sparse code multiple access (SCMA) as a code-domain non-orthogonal multiple access (NOMA) technique has been intensively investigated. With SCMA, each user employs different sub-carrier frequencies for transmission, and different users can share the same sub-carrier frequency via superposition coding. The spectrum efficiency is thus improved. However, designing large-scale codebook sets is still an open problem for SCMA. In this paper, a new lattice-based codebook design method is proposed via mother constellation optimization. First, the optimization problem of the mother constellation is formulated. Through analysis, the problem can be converted into two sub-problems. Accordingly, we first use the lattice theory to find a constellation containing infinite points with large coding gain. After that, we search for a boundary that contains a set of points via spherical packing. Such an approach enables us to obtain a real constellation satisfying a power saving criterion. Secondly, the mother constellation with large power variance is obtained from the real multi-dimensional constellation. Finally, lattice-based codebooks are generated by combining the mother constellation and the mapping matrices with constellation rotation. Simulations demonstrate that the designed codebooks have improved bit error rate (BER) performance with large codebook size, especially at high signal to noise ratio (SNR).INDEX TERMS Information-centric Internet of Things (IC-IoT), non-orthogonal multiple access (NOMA), sparse code multiple access (SCMA), lattice theory, spherical packing.
Spectrum efficiency and energy efficiency are two critical issues in the design of wireless communication networks. Recently, energy harvesting cognitive radio networks have been proposed to attempt to solve both the issues simultaneously. In this paper, we consider a cognitive radio network in which a primary transmitter mainly occupies the channel, and a secondary transmitter equipped with an energy harvesting device is allowed to opportunistically access the primary channel at any time if it is detected to be idle. Here, we assume that energy arrival process and primary channel state are random process and two-state time-homogenous discrete Markov process, respectively. Instead of the expected number of successful spectrum access attempts per time slot as a design criterion in current literature, we use the average channel capacity as the achievable throughput to jointly optimize energy harvesting and spectrum sensing subject to the constraints on the energy causality, collision, and temporal correlation of probability of sensing the idle/occupied channel, thus achieving or almost achieving both the energy efficiency and the spectrum efficiency in certain conditions. In addition, the corresponding optimum detection threshold and the maximum achievable throughput are obtained, which are substantiated by our comprehensive computer simulations.
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