Emerging Mobile Computing Technology: Ultra-high Energy Efficiency

In this paper, we propose an efficient design methodology for energy-efficient off-chip interconnect. This approach leverages an artificial neural network (ANN) as a surrogate model that significantly improves design efficiency in the frequency-domain. This model utilizes design specifications as the constraint functions to guarantee the satisfaction of design requirements. Additionally, a specified objective function to select low-loss and low-noise structure is employed to determine the optimal case from a large design space. The proposed design flow can find the optimum design that gives maximum eye height (EH) with the largest allowable transmitter supply voltage ($V_{TX}$) reduction for minimum power consumption. The proposed approach is applied to the microstrip line and stripline structures with single-ended and differential signals for general applicability. For the microstrip line, the proposed methodology performs at a performance speed with 42.7 and 0.5 s per structure for the data generation and optimization process, respectively. In addition, the optimal microstrip line design achieves a 25%$V_{TX}$ reduction. In stripline structures, it takes 31.9 s for the data generation and 0.6 s for the optimization process per structure when the power efficiency reaches a maximum 30.7% peak to peak $V_{TX}$ decrease. Full article

Non-orthogonal multiple access (NOMA) has been considered a promising technique for the fifth generation (5G) mobile communication networks because of its high spectrum efficiency. In NOMA, by using successive interference cancellation (SIC) techniques at the receivers, multiple users with different channel gain can be multiplexed together in the same subchannel for concurrent transmission in the same spectrum. The simultaneously multiple transmission achieves high system throughput in NOMA. However, it also leads to more energy consumption, limiting its application in many energy-constrained scenarios. As a result, the enhancement of energy efficiency becomes a critical issue in NOMA systems. This paper focuses on efficient user clustering strategy and power allocation design of downlink NOMA systems. The energy efficiency maximization of downlink NOMA systems is formulated as an NP-hard optimization problem under maximum transmission power, minimum data transmission rate requirement, and SIC requirement. For the approximate solution with much lower complexity, we first exploit a quick suboptimal clustering method to assign each user to a subchannel. Given the user clustering result, the optimal power allocation problem is solved in two steps. By employing the Lagrangian multiplier method with Karush–Kuhn–Tucker optimality conditions, the optimal power allocation is calculated for each subchannel. In addition, then, an inter-cluster dynamic programming model is further developed to achieve the overall maximum energy efficiency. The theoretical analysis and simulations show that the proposed schemes achieve a significant energy efficiency gain compared with existing methods. Full article