This paper studies the problem of predicting the spectrum efficiency (SE) for massive multiple-input multiple-output (MIMO) empowered 5G networks based on the reference signal received power (RSRP) collected from the drive test (DT). This problem is challenging because there is no precise model between the RSRP and the SE. The SE not only depends on the RSRP, which only captures the statistic of the channel, but also the beamforming strategy of the serving base station (BS) and the interference from the neighboring cells, which are not measured at the 5G client. This paper adopts a model-assisted data-driven approach to develop a machine learning model for the SE prediction. Specifically, a joint interference and SE prediction network is built, demonstrating prediction improvement over pure data-driven neural networks. In addition, a classification-assisted SE prediction network is constructed, which substantially reduces the prediction error at the low SE regime with marginally compromising the total prediction error. It is found that the model-assisted approach generally enhances the SE prediction accuracy by 2% approximately over a purely data-driven approach.

Interference alignment (IA) is thought to be an effective technique on reducing interference in wireless communications. To mitigate the interference and improve the system performance, this paper proposes a two-stage hybrid interference alignment (THIA) scheme for the two-tiered multi-user multiple-input-multiple-output (MIMO) heterogeneous network (HetNet). Specifically, we focus on the downlink transmission of two-tiered HetNet. Firstly, we align the cross-tier interference from macro base station (MBS) to the users in the same small cell to a specific subspace, through which the first-stage transmit beamforming matrix at MBS is designed. Then, an equivalent virtual distributed method is applied to mitigate the co-tier interference among macro users. Numerical results demonstrate that the proposed THDIA scheme outperforms the conventional schemes in degrees of freedom and system sum rate.

Sparse Code Multiple Access (SCMA) is a new air interface technology that is proposed for lower delay, higher capacity and spectrum utilization in 5G communications. The inter-symbol interference(ISI) in wireless communications is serious due to the effects of multipath propagation and background noise. In order to reduce inter-symbol interference, many wireless air interface and radio access technologies adopt cyclic prefixes to obtain channel estimation. However, the spectrum utilization of this method is too low. Considering the blind equalization algorithm is often used in the environment where spectrum resources are scarce, in this paper we adop the blind equalization algorithm in the SCMA technology. In SCMA, the equalization cannot placed on receiver, so we innovatively put the equalization algorithm in the signal transmitter according to the actual situation. In addition, we propose a channel estimation algorithm. Theory and simulation show that applying blind equalization to SCMA can reduce bit error rate and improve spectrum utilization.

Millimeter wave (mmWave) communications are an important candidate technique in 5G networks for features, supporting ultra-dense small cells and mobile data offloading. However, ultra-dense nodes and increasing data traffic bring in vast interference. This paper investigates low complexity non-iterative interference alignment (IA) schemes for multiple-input multiple-output (MIMO) interference broadcast channels in mmWave communications. The authors focus on the three-cell mobile network model in which each base station supports no more than two users within its cell. There is already a closed-form IA solution for the case that one cell has two users, while the other two have one user in each, which can be denoted as {2,1,1}. This paper considers different settings and proposes corresponding IA schemes. First, two IA schemes based on multi-step for the asymmetric setting {2,2,1} are presented, five degree of freedom (DoF) could be achieved. Then, for the symmetric setting {2,2,2}, a novel IA solution with lower complexity and a joint method combining IA with non-iterative multi-user MIMO technique are proposed, and they can achieve six DoF. The simulation results indicate that our non-iterative schemes have similar sum-rate capacity performances with the iterative ones in existing work, and the complexity is effectively reduced.