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This paper seeks to answer a simple but fundamental question: what role can non-orthogonal multiple access (NOMA) play in massive multi-in multi-out (MIMO)? It is well established that power-domain NOMA schemes can outperform conventional orthogonal multiple access schemes in cellular networks. However, this fact does not imply that NOMA is the most efficient way to communicate in massive MIMO setups, where the base stations have many more antennas than there are users in the cell. These setups are becoming the norm in future networks and are usually studied by assuming spatial multiplexing of the users using linear multi-user beamforming. To answer the above-mentioned question, we analyze and compare the performance achieved by NOMA and multi-user beamforming in both non-line-of-sight and line-of-sight scenarios. We reveal that the latter scheme gives the highest average sum rate in massive MIMO setups. We also identify specific cases where NOMA is the better choice in massive MIMO and explain how NOMA plays an essential role in creating a hybrid of NOMA and multi-user beamforming that is shown to perform better than two standalone schemes do. [ABSTRACT FROM AUTHOR]

In this paper, we consider the downlink of a multi-cell multiple-input multiple-output system and find the jointly optimal number of base station (BS) antennas and transmission powers that minimize the power consumption while satisfying each user’s effective signal-to-interference-and-noise-ratio constraint and the BSs’ power constraints. Different from prior work, we consider a power consumption model that takes both transmitted and hardware-consumed power into account. We formulate the joint optimization problem for both single-cell and multi-cell systems. The closed-form expressions for the optimal number of BS antennas and transmission powers are derived for the single-cell case. The analysis for the multi-cell case reveals that increasing the number of BS antennas in any cell always improves the performance of the overall system in terms of both feasibility and total radiated power. A key contribution of this paper is to show that the joint optimization problem can be relaxed as a geometric programming problem that can be solved efficiently. The solution can be utilized in practice to turn on and off antennas depending on the traffic load variations. The substantial power savings are demonstrated by simulation. [ABSTRACT FROM AUTHOR]

A key challenge of massive MTC (mMTC), is the joint detection of device activity and decoding of data. The sparse characteristics of mMTC makes compressed sensing (CS) approaches a promising solution to the device detection problem. However, utilizing CS-based approaches for device detection along with channel estimation, and using the acquired estimates for coherent data transmission is suboptimal, especially when the goal is to convey only a few bits of data. First, we focus on the coherent transmission and demonstrate that it is possible to obtain more accurate channel state information by combining conventional estimators with CS-based techniques. Moreover, we illustrate that even simple power control techniques can enhance the device detection performance in mMTC setups. Second, we devise a new non-coherent transmission scheme for mMTC and specifically for grant-free random access. We design an algorithm that jointly detects device activity along with embedded information bits. The approach leverages elements from the approximate message passing (AMP) algorithm, and exploits the structured sparsity introduced by the non-coherent transmission scheme. Our analysis reveals that the proposed approach has superior performance compared with application of the original AMP approach. [ABSTRACT FROM AUTHOR]

Interference management by controlling the transmission powers is crucial for unveiling the potential of heterogeneous networks. Power allocation has mostly been investigated using ideal communication links, where it is generally assumed that information can be relayed perfectly between base stations. The impact of delays and time-outs during communication is not well understood. We propose a model with nonideal links and a power adjustment algorithm for heterogeneous networks. Contrary to the power allocation approaches in the literature, channel state information is not required for power adjustment. The convergence properties of the proposed algorithm are investigated using tools from switched system theory and it is shown to converge to the max–min solution even with nonideal communication links. The simulation results are in agreement with the theoretical analysis. [ABSTRACT FROM AUTHOR]

Interference management via resource allocation under the quality of service (QoS) provisions is a crucial challenge for next-generation cellular networks. The task becomes more problematic due to emerging services and applications with non-homogeneous QoS requirements. The resource allocation problem has mostly been investigated using ideal communication links, where it is generally assumed that information can be relayed perfectly between base stations (BSs). The impact of delays and time-outs at communication links during coordination of BSs is not well understood. Furthermore, it is generally assumed that perfect channel state information is available at BSs which may not always be possible due to large channel feedback delays or estimation errors. In this paper, we propose a power adjustment algorithm for heterogeneous networks which considers varying QoS requirements of users. Contrary to the power allocation approaches in the literature, channel state information is not required for power adjustment. The performance of the algorithm is evaluated both analytically and numerically under a setup with non-ideal communication links. The theoretical analysis unveils that the proposed algorithm achieves optimum power allocation with respect to the QoS requirements of users. The numerical results are not only in agreement with theoretical analysis, but also exhibits significant improvement in terms of overall network performance. [ABSTRACT FROM PUBLISHER]

In two-tier femtocell networks, adjusting the transmission power values of femtocell base stations (BSs) such that indoor users receive high signal quality while limiting leakage to outdoor users is an important problem. This paper proposes a novel distributed and self-optimized power adjustment algorithm for two-tier femtocell networks, in which a BS adjusts the transmission power based on the signal quality of neighboring BSs. Achieving fairness among users with minimal information exchange is desired. There are no predefined target parameters used to adjust transmission power, contrary to existing approaches in the literature. The convergence properties of the algorithm under fixed and time-varying communication infrastructures are investigated. The performance is further verified by simulations and comparisons with other algorithms for the coverage problem. [ABSTRACT FROM PUBLISHER]

In this letter, a consensus-based distributed coverage algorithm is proposed for self organization in two-tier femtocell networks. One key feature of the algorithm is that it achieves fairness among users in terms of signal quality without relying on any predefined target values. The stability of the proposed algorithm is investigated using quadratic Lyapunov functions, which also allow us to analyze its convergence rate properties. The simulation results are not only in agreement with the theoretical analysis, but also demonstrate significant improvement in terms of overall network performance. [ABSTRACT FROM PUBLISHER]