Jianpeng Ma

Pattern Division for Massive MIMO Networks With Two-Stage Precoding

In massive multiple-input multiple-output networks with two-stage precoding, the user clusters with serious angle-spreading-range (ASR) overlapping should be divided into different patterns and scheduled in orthogonal sub-channels to achieve optimal performance. In this letter, we propose one graph theory-based pattern division (GT-PD) scheme to deal with the ASR overlapping with a limited number of sub-channels. First, we depict the ASR overlapping as an undirected weighted graph, where the weight of each edge indicates the strength of the ASR overlapping between two connected clusters. Then, we separately denote each user cluster and pattern as a vertex and a color, and transform the pattern division into a graph coloring problem with limited colors. In addition, the GT-PD scheme is developed with the help of the Dsatur algorithm. Finally, numerical results are provided to corroborate the efficiency of the proposed scheme.

Interference-Alignment and Soft-Space-Reuse Based Cooperative Transmission for Multi-cell Massive MIMO Networks

As a revolutionary wireless transmission strategy, interference alignment (IA) can improve the capacity of cell-edge users. However, the acquisition of the global channel state information for IA leads to unacceptable overhead in the massive MIMO systems. To tackle this problem, in this paper, we propose an IA and soft-space-reuse (IA-SSR)-based cooperative transmission scheme under the two-stage precoding framework. Specifically, the cell-center and the cell-edge users are separately treated to fully exploit the spatial degrees of freedoms. Then, the optimal power allocation policy is developed to maximize the sum-capacity of the network. Next, a low-cost channel estimator is designed for the proposed IA-SSR framework. Some practical issues in IA-SSR implementation are also discussed. Finally, plenty of numerical results are presented to show the efficiency of the proposed algorithm.

Base Station Selection for Massive MIMO Networks With Two-Stage Precoding

Two-stage precoding has been proposed to reduce the overhead of both the channel training and the channel state information feedback in massive multiple-input multiple-output systems. But the overlap of the angle-spreading-ranges (ASR) for different user clusters may seriously degrade the performance of two-stage precoding. In this letter, we propose an ASR overlap mitigating scheme through the base station (BS) selection. First, the BS selection is formulated as a sum signal-to-interference-plus-noise ratio maximization problem. Then, the problem is solved by a low-complex algorithm through maximizing the signal-to-leakage-plus-noise ratio (SLNR). In addition, we propose a low-overhead algorithm with the lower bound on the average SLNR as the objective function. Finally, we demonstrate the efficacy of the proposed schemes through numerical simulations.

Two-level scheme to maximise the number of guaranteed users in downlink femtocell networks

In this study, the authors study the downlink resource allocation optimisation in femtocell networks, to maximise the number of guaranteed users whose data rate requirements are fully met. The spectral access of femtocell networks is based on orthogonal frequency division multiple access. In their work, two challenges are solved. The first is the intractable inter-cell interference coordination brought about by the transmission delay in backhaul connections, and the second is the incorporation of physical interference model into problem formulations. To solve these problems, the authors propose a novel two-level resource allocation scheme, implemented in both radio resource management controller and femtocell base stations, based on the maximisation of the number of guaranteed users. Notably, their proposed scheme is efficient and requires low overhead. Simulation results show that the proposed scheme offers significant performance improvement in both the percentage of guaranteed users and spectrum spatial reuse over existing methods proposed in the literature.

Angle domain sparse code multiplexing for the massive MIMO networks

In this paper, we propose an angle domain sparse code multiple access (ASCMA) scheme for the massive multiple-input multiple-output (MIMO) networks to enhance the access connectivity. The basic idea of ASCMA scheme is to construct a non-orthogonal transmission policy in the sparse angle domain channel of massive MIMO networks. Firstly, an angle domain mapping structure is presented, where both the power and angle domains are taken into consideration. Then, we obtain the optimal factor graph mapping strategy through maximizing the sum rate of all users contained by one cluster. Besides, we discuss the strategy selection benchmark, and then codebook design is investigated. Simulation results verify that the ASCMA scheme achieves significant performance gain, in terms of sum rate.

Two-Stage Precoding Based Interference Alignment for Multi-cell Massive MIMO Communication

The two-stage precoding scheme was proposed to reduce the high overhead of channel estimation and channel state information (CSI) feedback for frequency division duplexing (FDD) massive MIMO systems. However, most of recent works focus only on single cell scenery. In this paper, we examine two-stage precoding for multi-cell FDD massive MIMO system. To manage the inter-cell interference and improve the data rate for cell-edge users, we propose a two-stage precoding based Interference alignment (IA) scheme for multi-cell massive MIMO system. Both theoretical analysis and numerical results show that proposed two-stage precoding based IA scheme can sufficiently improve the data rate for cell-edge users.

Routing in Disruption Tolerant Networks with Limited Storage

In this paper, we consider a disruption tolerant network (DTN) which enables data transmission with intermittent connectivity and in which instantaneous end-to-end path between a source and destination may not exist. We explore routing problems in such networks in consideration of limited storage for each intermediate node. A graph model called the storage enhanced time-varying graph (STVG) is presented, which fits the dynamic topology characteristics and time- varying parameters of DTN. We transform the nodes with finite buffer in DTN into links with limited bandwidth in STVG. Then, a Shortest Path Computation with Flow Guaranteed (SPFG) algorithm is proposed to select a path with minimum overall cost (delay), meanwhile, to guarantee the transfer of a certain flow. Simulation results show that the proposed algorithm based on the STVG model can obtain better performance in delay and delivery ratio (which can be improved to more than 90%) as compared with existing routing algorithms without considering the buffer constraints.