With a tremendous amount of underutilized bandwidth, millimeter wave (mmWave) communication is a promising solution to meet the increasing data-rate demands. To deal with high pathloss in mmWave frequency band, efficient large antenna arrays become crucial to provide necessary beamforming and spatial multiplexing gains. Hybrid analog/digital beamforming has been an encouraging technique in order to reduce the complexity and power consumption of large antenna arrays. In this paper, we propose a low-complexity two-stage hybrid analog/digital precoding and combining scheme for downlink multi-user multi-RF-chain mmWave systems using out-of-band spatial information to minimize the mean-squared error (MMSE) in receiving data streams. Particularly, according to sparse spatial characteristic of the mmWave channel, we utilize more efficient Grassmannian codebooks as the training codebooks to generalize a strategy of utilizing sub-6GHz spatial information for constructing multiuser mmWave communication links. The multiuser analog beamforming problem is then formulated as a weighted sparse signal recovery problem with the weights obtained from sub-6GHz spatial information. In addition, the digital precoding and combining scheme is derived using an efficient MMSE scheme for multi precoders at transmitters and combiners at receivers. Numerical results show the obvious advantages of the proposed multi-user hybrid analog/digital precoding/combining design for various practical scenarios.

Recently, to reduce the increasing energy consumption along with the explosive growth of smart terminals and network data in Internet of Things (IoT), green IoT is attracting more and more attention from both academia and industry. Towards green IoT, this paper devotes attention to Device-to-Device (D2D) communication and social network which are two essential components in green IoT and first reviews the latest developments about them. Further, this paper proposes a social interaction assisted resource sharing scheme for D2D communication, to improve the utilization of spectrum resources in green IoT. Specifically, the proposed scheme abstracts the D2D communication system into social layer and physical layer, by combining social network with D2D communication. Extensive simulations with real social interaction data are conducted and they show that the proposed scheme can achieve improvements in terms of the transmission success rate for green IoT.

Millimeter-wave (mmWave) communications are a critical technique with next-generation network characteristics such as ultra-dense small cells can meet the skyrocketing demand for mobile data. Hybrid precoding, which combines analog and digital processing to provide both spatial diversity gains and beamforming, is commonly studied for mmWave communications to lower the power and cost consumption of radio frequency (RF) networks. However, the combination of ultra-dense small cells and ever-increasing data traffic results in massive interference. In this paper, we propose a minimum mean square error (MMSE)-based hybrid beamforming strategy for downlink mmWave MIMO two-tier heterogeneous networks (HetNets). The analog beamforming is generalized by an orthogonal matching pursuit technique. The analog beamforming problem is formulated as a sparse signal recovery problem. An MMSE-based digital beamforming algorithm is proposed to minimize the sum MSE of the user-intended data streams so that the inter- and intra-tier interferences are mitigated iteratively. The simulation results demonstrate the advantageous performance of the proposed hybrid beamforming schemes under different cellular cooperation and data transmission scenarios when hardware constraints are taken into account.

In this paper, we propose a minimum mean square error (MMSE) based transceiver design scheme for a downlink multiple-input multiple-output (MIMO) two-tier heterogeneous network with general linear equality per-cell power constraints. Three practical channel models with both perfect and imperfect channel state information are used in simulations. In each channel model, we consider two system configurations, two data transmission schemes and two cellular cooperation scenarios. Our study shows that the proposed MMSE scheme is more flexible than interference alignment (IA) based scheme. For the cases where the IA-type scheme is applicable, the proposed scheme generally outperforms IA-type scheme in terms of average sum rate and bit error rate (BER), but is computationally more complex than the IA-type scheme.