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Massive Machine-Type Communication (mMTC) is proposed by ITU to provide a large scale of connectivity services for billions of machine-type communications devices (MTCDs) via cellular networks. However, MTCDs are generally energy-constrained, security capability limited and short-packet transmission dominated. It has great challenges for massive MTCDs to perform short-packet transmission simultaneously while guaranteeing the confidentiality and energy efficiency of the information transmission. In this paper, we investigate the energy-efficient secure short-packet transmission of Non-Orthogonal Multiple Access (NOMA) assisted mMTC networks, in which MTCDs aim to transmit secrecy messages to the destination base station via trusted relays with a passive eavesdropper present. To realize secure, reliable and energy-efficient transmission, we maximize the secrecy energy efficiency (SEE) by implementing joint relay and power level selection. Furthermore, we formulate a decentralized stateless Q-learning-based multi-agent reinforcement learning (MARL) algorithm for the dynamic and complex mMTC systems. The simulation results show that adopting the proposed decentralized MARL algorithm and relaying with NOMA can improve the performance of SEE while reducing information exchange overhead. [ABSTRACT FROM AUTHOR]

Millimeter-wave (mmWave) and ultra-dense networks are two key technologies for the fifth-generation (5G) and beyond communication system. However, the ultra-dense deployment of small base stations (SBSs) might introduce severe interference to users that connect to SBSs. This paper analyzes the performance of 5G communication networks where the SBSs with coordinated beamforming, operating at mmWave frequency band and macro base stations (MBSs) operating at sub-6 GHz coexist. First, by utilizing a stochastic geometry approach, we obtain the cell association probability expressions in terms of different cell association biases, base station density ratios and probabilities of line of sight (LoS) link. Furthermore, we propose a clustering method to choose some SBSs to eliminate intra-cell interference. Then, we put forward an average distance from the Kth SBS to a user to obtain signal-to-interference-ratio (SINR) and rate coverage probability expressions. The simulation results validate the correctness of the expressions, and indicate that the optimal cardinality of coordinated SBSs increases with the density of SBSs. In addition, the relationship between the cluster size K and the average energy efficiency is obtained, which can be used to guide the coordination principle in 5G and beyond communication systems. [ABSTRACT FROM AUTHOR]

The 5th-generation (5G) mobile networks and beyond need to support massive machine-type communications (MTC) devices with limited available radio resources. In this paper, we study the power-domain non-orthogonal multiple access (NOMA) technology to support energy-efficient massive MTC networks, where MTC devices exchange information using sporadic and low-rate short packets. We investigate the subchannel allocation and power control policy to maximize the achievable effective energy efficiency (EE) for uplink NOMA-based massive MTC networks, taking into account of short-packet communication characteristics. We model the subchannel allocation problem as a multi-agent Markov decision process and propose an efficient Q-learning algorithm to solve it. Furthermore, we obtain the optimal transmission power policy by approximating the achievable effective rate of uplink NOMA-based short packet communications. Compared with the existing OFDMA scheme, simulations validate that the proposed scheme can improve the achievable effective EE of massive MTC networks with 5.93%. [ABSTRACT FROM AUTHOR]

Algae CO 2 biofixation provides a promising opportunity due to earn carbon credits and valuable end uses. For balancing technology, energy and economy issues in practical utilization, this approach quantitively interprets the contradictions from upstream CO 2 source with a wide range of initial concentration to downstream CO 2 biofixation product including edible algae and algal biomass. The influence of upstream CO 2 deliverable on algal quantity and quality have been assessed, and the influence of CO 2 concentration on CO 2 transport mode choice has been also assessed coupling the transportation distance. In downstream algal fixation, quantitively relationship of algal growth have been established. The assessment discovered that direct energy consumptions complied with logarithmic relationship with specific productivities while both direct energy and indirect energy consumption complied with linear relationship with protein content. According to sensitive uncertainty analysis, initial CO 2 concentration is a critical parameter to influence significantly energy consumption in upstream CO 2 deliverables and algal quality while the contents of protein and specific productivity are the critical sensitive parameters in downstream algae deliverables. Potential modification systems are achieved for significantly reducing energy consumption by improving specific productivity and carbon abundance with low protein content in algae. Unlabelled Image • Establish quantitively relationship of algal growth and CO 2 fixation efficiency • Correlation of upstream flue gas and downstream CO 2 biofixation product choice • Effects of algae growth rate and protein contents on energy consumption • Effects of initial CO 2 concentration on algal products quality • Key impact factors extracted by the sensitive uncertainty analysis [ABSTRACT FROM AUTHOR]