This letter investigates the performance of non-orthogonal multiple access (NOMA) in short-packet communications. We aim to answer a fundamental question-for given reliability requirements of users: how much physical-layer transmission latency can NOMA reduce when compared with orthogonal multiple access in the finite blocklength regime? We derive closed-form expressions for the block error rates of users in NOMA. Further, we obtain the near-optimal power allocation coefficients and blocklength to ensure certain reliability. Numerical results validate our theoretical analysis and demonstrate the superior performance of NOMA in reducing transmission latency.

This paper considers the joint antenna selection (AS) problem for a classical two-user MIMO non-orthogonal multiple access (NOMA) system, where both the base station (BS) and users (UEs) are equipped with multiple antennas. Specifically, several computationally-efficient AS algorithms are developed for two commonly-used NOMA scenarios: fixed power allocation NOMA (F-NOMA) and cognitive radio-inspired NOMA (CR-NOMA). For the F-NOMA system, two novel AS schemes, namely max-max-max AS (A$^3$-AS) and max-min-max AS (AIA-AS), are proposed to maximize the system sum-rate, without and with the consideration of user fairness, respectively. In the CR-NOMA network, a novel AS algorithm, termed maximum-channel-gain-based AS (MCG-AS), is proposed to maximize the achievable rate of the secondary user, under the condition that the primary user's quality of service requirement is satisfied. The asymptotic closed-form expressions of the average sum-rate for A$^3$-AS and AIA-AS and that of the average rate of the secondary user for MCG-AS are derived, respectively. Numerical results demonstrate that the AIA-AS provides better user-fairness, while the A$^3$-AS achieves a near-optimal sum-rate in F-NOMA systems. For the CR-NOMA scenario, MCG-AS achieves a near-optimal performance in a wide SNR regime. Furthermore, all the proposed AS algorithms yield a significant computational complexity reduction, compared to exhaustive search-based counterparts.

This paper considers the joint antenna selection (AS) problem for a classical two-user non-orthogonal multiple access (NOMA) network where both the base station and users are equipped with multiple antennas. Since the exhaustive-search-based optimal AS scheme is computationally prohibitive when the number of antennas is large, two computationally efficient joint AS algorithms, namely max-min-max AS (AIA-AS) and max-max-max AS (A$^3$-AS), are proposed to maximize the system sum-rate. The asymptotic closed-form expressions for the average sum-rates for both AIA-AS and A$^3$-AS are derived in the high signal-to-noise ratio (SNR) regime, respectively. Numerical results demonstrate that both AIA-AS and A$^3$-AS can yield significant performance gains over comparable schemes. Furthermore, AIA-AS can provide better user fairness, while the A$^3$-AS scheme can achieve the near-optimal sum-rate performance.

This letter investigates a joint antenna selection (AS) problem for a MIMO cognitive radio-inspired non-orthogonal multiple access network. In particular, a new computationally efficient joint AS algorithm, namely subset-based joint AS (SJ-AS), is proposed to maximize the signal-to-noise ratio of the secondary user under the condition that the quality of service of the primary user is satisfied. The asymptotic closed-form expression of the outage performance for SJ-AS is derived, and the minimal outage probability achieved by SJ-AS among all possible joint AS schemes is proved. The provided numerical results demonstrate the superior performance of the proposed scheme.

In this paper, the finite-time stability for a class of shunting inhibitory cellular neural networks with neutral proportional delays is discussed. By employing differential inequality techniques, several sufficient conditions are obtained to ensure the finite-time stability for the considered neural networks. Meanwhile, the generalized exponential synchronization is also established. An example along with its numerical simulation is presented to demonstrate the validity of the proposed results.

This paper is concerned with the problem of finite-time stability for a class of non-autonomous shunting inhibitory cellular neural networks with multi-proportional delays. An explicit criterion for the finite-time stability of the system has been proposed by employing the differential inequality techniques. Moreover, an illustrative example and its numerical simulations are given to demonstrate the effectiveness of the obtained result.

In this letter, we develop a low-complexity transceiver design, referred to as semirandom beam pairing, for sparse multipath massive multiple-input-multiple-output (MIMO) channels. By exploring a sparse representation of the MIMO channel in the virtual angular domain, we generate a set of transmit-receive beam pairs in a semirandom way to support the simultaneous transmission of multiple data streams. These data streams can be easily separated at the receiver via a successive interference cancelation technique, and the power allocation among them are optimized based on the classical waterfilling principle. The achieved degree of freedom (DoF) and capacity of the proposed approach are analyzed. Simulation results show that, compared to the conventional singular value decomposition-based method, the proposed transceiver design can achieve near-optimal DoF and capacity with a significantly lower computational complexity.

This paper is concerned with a class of high-order cellular neural networks with neutral time-proportional delays. Based on a new differential inequality technique, some sufficient conditions are derived to ensure that all solutions of the addressed system converge exponentially to zero vector, which improve and supplement existing ones. Also, an example and its numerical simulations are given to demonstrate our theoretical results.

This paper concerns with the pseudo almost periodic solutions for a class of cellular neural networks model with multi-proportional delays. By applying contraction mapping fixed point theorem and differential inequality techniques, we establish some sufficient conditions for the existence and exponential stability of pseudo almost periodic solutions for the model, which improve and supplement existing ones. Moreover, an example and its numerical simulation are given to support the theoretical results.

In this paper, we study the open-challenging problem of antenna selection (AS) at both the relay and source nodes in MIMO two-way relay networks (TWRNs). Two near-optimal algorithms, namely the joint relay-source AS (JRSAS) and the separated relay-source AS (SRSAS), are proposed in a greedy manner. Specially, JRSAS selects antennas at both the relay and source nodes simultaneously in each AS step to maximize the increment of the system throughput. In order to further reduce the AS computational complexity, SRSAS performs AS at the relay and source nodes in two separate stages. Numerical results show that both JRSAS and SRSAS can approach the optimal exhaustive search (ES) AS algorithm but the computational complexity has been significantly reduced.

This paper concerns with exponential convergence for a class of high-order recurrent neural networks with continuously distributed delays in the leakage terms. Without assuming the boundedness on the activation functions, some sufficient conditions are derived to ensure that all solutions of the networks converge exponentially to the zero point by using Lyapunov functional method and differential inequality techniques, which correct some recent results of Chen and Yang (Neural Comput Appl. doi: 10.1007/s00521-012-1172-2 , 2012 ). Moreover, we propose a new approach to prove the exponential convergence of HRNNs with continuously distributed leakage delays.