Abstract-This paper seeks to answer a simple but fundamental question: What role can NOMA play in massive MIMO? It is well-established that power-domain non-orthogonal multiple access (NOMA) schemes can outperform conventional orthogonal multiple access (OMA) 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 question, we analyze and compare the performance achieved by NOMA and multi-user beamforming in both NLOS and LOS 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. T HE fifth generation (5G) cellular technology aims to handle the 1000-fold increase in mobile data traffic that is predicted over the next decade. A vital challenge for 5G networks is to greatly improve the spectral efficiency compared to contemporary networks. One of the key technologies to increase the per-cell spectral efficiency is non-orthogonal multiple access (NOMA) which has attracted a considerable attention of researchers [1] . Another key technology is massive MIMO, which refers to the use of base stations (BSs) with a large number of antennas [2] . Now the first release of the 5G standard has been finalized and contains a variety of features, including NOMA and massive MIMO. It is important to determine which features provide the largest performance gains in practical scenarios. In this paper, we investigate the application of NOMA at BSs that are equipped with many antennas, since that appears to be the norm in the new LTEAdvanced and 5G deployments [3] , [4] . The key idea of NOMA is to serve multiple users at the same time/frequency/code resource and thereby increase the sum spectral efficiency in the cell. The typical approach to NOMA is to group users and superpose their data signals using different transmission powers before transmitting the group's signal in the same way, using the same beamforming. The users are usually grouped to have very different channel conditions (e.g,. one cell-center user is grouped with one celledge user). Users with poor channel gains are allocated more transmission power to (partially) overcome their poor channel conditions and interference created by transmissions to other The authors are with the Department of Electrical Engineering (ISY), Linköping University, Sweden. This work was supported in part by ELLIIT, Swedish Research Council (VR). users. In each group, the user with the better channel can decode the signal sent to the user with the poorer channel, and the interference can thus be eliminated by a process called successive interference cancellation (SIC) [5] . Hence, the user with the better channel does not need to be allocated a high transmission power to achieve a good communication rate. This approach is sometimes referred to as power-domain NOMA. There are two crucial drawbacks of SIC. The first and foremost drawback is that accurate channel state information (CSI) is required at the receiver side to utilize SIC, without being subject to substantial residual interference. Hence, downlink pilot signaling is essential for NOMA and the performance of SIC relies heavily on the channel estimation quality [6] . Another drawback is the additional computational complexity and buffering of the received signals that are needed by the SIC procedure. The computational burden on the user devices may be too demanding for NOMA systems [5] , at least for some applications. As indicated by the NOMA terminology, it constitutes a break with the traditional orthogonal multiple access (OMA) approaches (e.g., used in OFDMA and TDMA), where resources are exclusively allocated to users and hence, there is no intra-cell interference when using OMA. While convenient to implement, the OMA techniques suffer from inefficient utilization of the spectral resources. OMA is the common benchmark for the performance assessment of NOMA techniques and there are prior works showing that NOMA is always better than OMA in terms of sum rate, under the strong assumption of having perfect CSI. There are also a number of prior works that provide comparisons of NOMA and OMA in more realistic setups with imperfect CSI [7] , [8] . Our goal is not to compare NOMA and OMA, but to investigate how NOMA can be utilized by BSs equipped with many antennas. In less than a decade, massive MIMO has transitioned from being a far-fetched theoretical concept with an unlimited number of antennas [9] to a practical technology that has been commercially deployed in LTE-Advanced networks using 64-antenna BSs [4] . In a nutshell, massive MIMO refers to systems where the BSs are equipped with a large number of antennas, M, as compared to the number of simultaneously active users, K. In other words, M ≫ K is the characterization of a massive MIMO setup. The BS antennas are used for spatial multiplexing of the users at the same time/frequency/code resource [2], [10] . Each user is assigned a dedicated beam that is adapted to the collection of user channels, in order to balance between achieving a high array gain for the desired signal and limiting the inter-user interference [11] . Zeroforcing (ZF) is a popular interference-suppressing beamforming scheme in massive MIMO since it eliminates all the inter-user interference under perfect CSI and performs well
The gains are, however, generally limited since, to be efficient, power-domain NOMA requires UEs channels to be non-orthogonal, while a core feature of mMIMO is to make UE channels nearly orthogonal REF .
What Role Can NOMA Play in Massive MIMO?
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