OpenCarrier: Breaking the User Limit for Uplink MU-MIMO Transmissions With Coordinated APs

The global IoT market is experiencing a fast growth with a massive number of IoT/wearable devices deployed around us and even on our bodies. This trend incorporates more users to upload data frequently and timely to the APs. Previous work mainly focus on improving the up-link throughput. However, incorporating more users to transmit concurrently is actually more important than improving the throughout for each individual user, as the IoT devices may not require very high transmission rates but the number of devices is usually large. In the current state-of-the-arts (up-link MU-MIMO), the number of transmissions is either confined to no more than the number of antennas (node-degree-of-freedom, node-DoF) at an AP or clock synchronized with cables between APs to support more concurrent transmissions. However, synchronized APs still incur a very high collaboration overhead, prohibiting its real-life adoption. We thus propose novel schemes to remove the cable-synchronization constraint while still being able to support more concurrent users than the node-DoF limit, and at the same time minimize the collaboration overhead. In this paper, we design, implement, and experimentally evaluate OpenCarrier, the first distributed system to break the user limitation for up-link MU-MIMO networks with coordinated APs. Our experiments demonstrate that OpenCarrier is able to support up to five up-link high-throughput transmissions for MU-MIMO network with 2-antenna APs.

Interference Suppression in Heterogeneous Massive MIMO Systems with Imperfect CSI

Interference management is of paramount importance in heterogeneous massive mimo networks (HetNet). In this paper, an algorithm has been suggested to suppress the interference in large-MIMO HetNets with imperfect channel state information(CSI). The proposed technique controls both the intra-tier and cross-tier interference of the macrocell as well as the small cells. The intra-tier interference of the macrocell as well as the cross-tier interference have been minimized under maximum transmission power and minimum signal to interference and noise ratio (SINR) constraint. The channel estimation error matrix has also been modeled using the joint sparsity property. The precoding algorithm is thus achieved through the application of semi-definite relaxation and block coordinate descent techniques. The intra-tier interference of the small cells are addressed with the aid of the zero forcing scheme. The proposed method has been validated through various simulations which confirm the superiority of the algorithm over its counterparts.

Interference Alignment Inspired Opportunistic Communications in Multi-Cluster MIMO Networks with Wireless Power Transfer

In this work, we jointly investigate the issues of node scheduling and transceiver design in a sensor network with multiple clusters, which is endowed with simultaneous wireless information and power transfer. In each cluster of the observed network, S out of N nodes are picked, each of which is capable of performing information transmission (IT) via uplink communications. As for the remaining idle nodes, they can harvest energy from radio-frequency signals around their ambient wireless environments. Aiming to boost the intra-cluster performance, we advocate an interference alignment enabled opportunistic communication (IAOC) scheme. This scheme can yield better tradeoffs between IT and wireless power transfer (WPT). With the aid of IAOC scheme, the signal projected onto the direction of the receive combining vector is adopted as the accurate measurement of effective signal strength, and then the high-efficiency scheduling metric for each node can be accordingly obtained. Additionally, an algorithm, based on alternative optimization and dedicated for transceiver design, is also put forward, which is able to promote the achievable sum rate performance as well as the total harvested power. Our simulation results verify the effectiveness of the designed IAOC scheme in terms of improving the performance of IT and WPT in multi-cluster scenarios.