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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]

Given the optimal transmit beamforming vector, the user who has the worst signal-to-noise ratio (SNR) is referred to as a bottleneck user for a single-group multicasting system with multiple antennas at the transmit side. In this paper, we present some interesting insights into the bottleneck users, such as the maximum number as well as the optimal beamformer when the transmitter has an arbitrary number of antenna elements. The $\max$– $\min$ fair SNR problem is employed and the closed-form solutions are proposed from the weighted sum-SNR maximization perspective for different numbers of possible bottleneck users. A new heuristic beamforming algorithm based on the bottleneck users is also presented to improve the system performance in terms of the achievable rate for physical-layer multicasting. [ABSTRACT FROM PUBLISHER]

The objective of maximizing the minimum Euclidean distance between two received data vectors via real-valued linear precoding is considered for multiple-input multiple-output (MIMO) systems with arbitrary dimensions. Assuming that perfect channel state information (CSI) is available at both the transmitter side and the receiver side, a novel low-complexity precoding algorithm is proposed to recursively construct the full-rank, the rank-deficient and the rank-one precoders with respect to particular channel realization. From the lattice theoretical perspective, the full-rank precoder is generated by using well-known dense packing lattices, which can also be recursively involved into the construction of higher-dimensional rank-deficient precoders. Moreover, the optimal solution is clearly figured out for the specific case of rank-one precoder. The closed-form expression of the achievable minimum distance is obtained and the analytical results reveal the performance bounds for different rank-constraint precoders. Simulation results validate the efficiency of the proposed precoder as compared with the state of the art. [ABSTRACT FROM PUBLISHER]

In 3-D integrated circuits (3-D ICs), through silicon via (TSV) is a critical technique to provide vertical connections. However, the yield and reliability challenge of TSV in industry is one of key obstacles to adopt the 3-D ICs technology. Various fault-tolerance structures by using additional spare TSVs (s-TSVs) to repair faulty functional TSVs (f-TSVs) have been proposed in literature for yield and reliability enhancement. However, these structures are formed in standard cell placement stage where all the f-TSVs are already placed. In reality, since the s-TSVs can be only inserted into the whitespace, the quality of the generated repair solution is strongly dependent on the whitespace distribution. In this paper, we propose an efficient TSV planning and repair framework in floorplanning stage, which takes nonuniform TSV distribution and clustered TSV defect-distribution into account. The proposed framework mainly consists of four stages: 1) a whitespace redistribution algorithm that uses a probability-based strategy to make the whitespace distribution more reasonable for the f-TSV planning. Subsequently, a convex-cost flow-based model for f-TSV allocation considering the fault clustering; 2) a top-down globally partitioning combined with a bottom-up locally merging to partition f-TSVs into groups with minimum hardware cost; 3) the min-cost max-flow algorithm for s-TSV allocation with minimum wirelength overhead; and 4) an integer linear programming-based model to form a fault-tolerance structure with minimum multiplexer delay overhead. The experimental results demonstrate that the proposed repair framework can improve the yield with minimum hardware cost and multiplexer delay overhead. [ABSTRACT FROM AUTHOR]