The proliferation of delay-sensitive Internet-of-Things (IoT) applications has ushered in a need for the statistical delay quality-of-service (QoS) guarantee for the applications. In this paper, we first derive an upper bound for the queuing delay violation probability (UB-QDVP) in uplink non-orthogonal multiple access (NOMA) by applying stochastic network calculus (SNC) to the Mellin transforms of service processes. A closed-form asymptotic approximation of the UB-QDVP is developed by proving the asymptotic convergence of the Mellin transform and its finite-length truncations. Given the closed-form asymptotic UB-QDVP, we propose two power allocation schemes. The first scheme minimizes the transmit power of a NOMA user pair while guaranteeing the statistical delay QoS of the pair. The second maximizes the$\alpha $-utility function of the effective capacity of the user pair, striking a balance between the energy efficiency and user fairness of uplink NOMA systems. Simulations validate the UB-QDVP and show the superiority of the proposed schemes to conventional power allocation schemes in terms of energy efficiency and fairness.

Most existing optimization objectives considered in non-orthogonal multiple access (NOMA) power allocation schemes are non-delay-sensitive metrics. In order to apply NOMA to various Internet of Things scenarios, the delay must be considered. The effective capacity of users, which characterizes the capacity under specific expiration probabilities, can potentially be a performance metric of statistical delay quality of service (QoS). In this paper, we propose two novel dynamic power allocation schemes with statistical delay QoS guarantee in the uplink NOMA system with paired users. One of the schemes maximizes the sum effective capacity (SEC) of the strong and weak users, which is a non-convex nonlinear optimization problem and is solved by Lagrangian dual decomposition and successive convex approximation (SCA). The other one maximizes the effective energy efficiency (EEE) of uplink NOMA, which is a fractional optimization problem and is solved by integrating the Dinkelbach method, SCA, and Lagrangian dual decomposition. Numerical results show that the SEC and EEE can be significantly improved by the proposed schemes, compared to the existing NOMA and orthogonal multiple access power allocation schemes.

With the emergence of the mission-critical Internet of Things applications, ultra-reliable low-latency communications are attracting a lot of attentions. Non-orthogonal multiple access (NOMA) with multiple-input multiple-output (MIMO) is one of the promising candidates to enhance connectivity, reliability, and latency performance of the emerging applications. In this paper, we derive a closed-form upper bound for the delay target violation probability in the downlink MIMO-NOMA, by applying stochastic network calculus to the Mellin transforms of service processes. A key contribution is that we prove that the infinite-length Mellin transforms resulting from the non-negligible interferences of NOMA are Cauchy convergent and can be asymptotically approached by a finite truncated binomial series in the closed form. By exploiting the asymptotically accurate truncated binomial series, another important contribution is that we identify the critical condition for the optimal power allocation of MIMO-NOMA to achieve consistent latency and reliability between the receivers. The condition is employed to minimize the total transmit power, given a latency and reliability requirement of the receivers. It is also used to prove that the minimal total transmit power needs to change linearly with the path losses, to maintain latency and reliability at the receivers. This enables the power allocation for mobile MIMO-NOMA receivers to be effectively tracked. The extensive simulations corroborate the accuracy and effectiveness of the proposed model and the identified critical condition.

High reliability and low latency, increasingly demanded by mission critical IoT applications, are the two key requirements for modern wireless communication systems. In this paper, we consider a battery-limited wireless machine type communication network where non-orthogonal multiple access (NOMA) is embedded to support massive connectivity. Hence, energy efficient NOMA transmission under statistical delay constraints is required to prolong the battery lifetime of the devices. We firstly derive the probabilistic upper bounds of the queueing delays of NOMA devices via the (min,×) stochastic network calculus. Then, we propose a transmit power optimization algorithm based the probabilistic delay bounds. Simulation results verify the tightness of the derived upper bound of the delay violation probability and thus the effectiveness of the proposed power control algorithm.

This paper considers imperfect channel state information (CSI) in the downlink precoding of cellular multi-user large-scale distributed antenna system (DAS). Specifically, the uncertainty of channel estimation is mainly caused by the pilot reuse among users in adjacent cells. The phenomenon is termed as pilot contamination in a multi antenna system. Since the large-scale fading may vary from antenna to antenna, it's not easy to jointly estimate the channels from all antennas to a certain user. Instead, this paper only jointly estimates the downlink channels of the antennas at the same site, therefore with the same large-scale fading. Consequently, the channel estimation is executed in a distributed manner while the subsequent precoding is done in a centralized way. Based on the analysis of the properties of the DAS channel estimations, a multi-cell MMSE precoding scheme is proposed which aims to minimize the sum of intra- cell interference and the interference the cell pours to other cells. An approximation of the multi-cell MMSE precoding is further derived to reduce the computational complexity. Simulation results show significant performance gains over traditional singlecell precoding schemes.

Ultra dense network (UDN) is identified as one of the key enablers for 5G since it can provide ultra high spectral reuse factor exploiting proximal transmissions. By densifying the network infrastructure equipments, it's highly possible that each user will have one or more dedicated serving base station antennas, introducing the user-centric virtual cell paradigm. However, due to irregular deployment of large amount of base station antennas, the interference environment becomes rather complex, thus introducing severe interferences among different virtual cells. This paper focuses on the downlink transmission scheme in UDN where a large number of users and base station antennas are uniformly spread over a certain area. An interference graph is first created based on the large- scale fadings. Then, base station antennas and users in the virtual cells within the same maximal connected component are grouped together and merge into one new virtual cell cluster, where users are jointly served via zero-forcing beamforming. A multi-virtual-cell minimum mean square error precoding scheme is further proposed to mitigate the inter-cluster interference. Simulation results show that the proposed interference graph based virtual cell merging approach can attain the average user rate performance of the grouping scheme based on virtual cell overlapping with smaller virtual cell size and reduced signal processing complexity.

The aim of SON (Self-Organizing Network) is to realize the autonomic function of the wireless network by self-configuration, self-optimization and self-healing, which reduces the human intervention and improves the user experience. Self-configuration is a process where newly deployed eNodeB are configured by automatic installation procedures to get the necessary basic configuration for system operation. Self-optimization is a process that continuously monitors an environment and automatically optimizes various parameters when the environment changes. Self-healing is a process that detects and localizes failures, then fixes the problems automatically. This paper gives a comprehensive introduction to the SON functionalities and outlines the framework of self-configuration, self-optimization and self-healing. Some concrete algorithms are proposed for self-optimization and self-healing, which include capacity and coverage optimization and cell outage detection and compensation respectively. Simulation results in various scenarios are provided to evaluate the performance of the proposed algorithms.