Distributed Compressive CSIT Estimation and Feedback for FDD Multi-user Massive MIMO Systems

To fully utilize the spatial multiplexing gains or array gains of massive MIMO, the channel state information must be obtained at the transmitter side (CSIT). However, conventional CSIT estimation approaches are not suitable for FDD massive MIMO systems because of the overwhelming training and feedback overhead. In this paper, we consider multi-user massive MIMO systems and deploy the compressive sensing (CS) technique to reduce the training as well as the feedback overhead in the CSIT estimation. The multi-user massive MIMO systems exhibits a hidden joint sparsity structure in the user channel matrices due to the shared local scatterers in the physical propagation environment. As such, instead of naively applying the conventional CS to the CSIT estimation, we propose a distributed compressive CSIT estimation scheme so that the compressed measurements are observed at the users locally, while the CSIT recovery is performed at the base station jointly. A joint orthogonal matching pursuit recovery algorithm is proposed to perform the CSIT recovery, with the capability of exploiting the hidden joint sparsity in the user channel matrices. We analyze the obtained CSIT quality in terms of the normalized mean absolute error, and through the closed-form expressions, we obtain simple insights into how the joint channel sparsity can be exploited to improve the CSIT recovery performance.Comment: 16 double-column pages, accepted for publication in IEEE Transactions on Signal Processin

Limited Feedback Design for Interference Alignment on MIMO Interference Networks with Heterogeneous Path Loss and Spatial Correlations

Interference alignment is degree of freedom optimal in K -user MIMO interference channels and many previous works have studied the transceiver designs. However, these works predominantly focus on networks with perfect channel state information at the transmitters and symmetrical interference topology. In this paper, we consider a limited feedback system with heterogeneous path loss and spatial correlations, and investigate how the dynamics of the interference topology can be exploited to improve the feedback efficiency. We propose a novel spatial codebook design, and perform dynamic quantization via bit allocations to adapt to the asymmetry of the interference topology. We bound the system throughput under the proposed dynamic scheme in terms of the transmit SNR, feedback bits and the interference topology parameters. It is shown that when the number of feedback bits scales with SNR as C_{s}\cdot\log\textrm{SNR}, the sum degrees of freedom of the network are preserved. Moreover, the value of scaling coefficient C_{s} can be significantly reduced in networks with asymmetric interference topology.Comment: 30 pages, 6 figures, accepted by IEEE transactions on signal processing in Feb. 201

Interference Alignment for Partially Connected MIMO Cellular Networks

In this paper, we propose an iterative interference alignment (IA) algorithm for MIMO cellular networks with partial connectivity, which is induced by heterogeneous path losses and spatial correlation. Such systems impose several key technical challenges in the IA algorithm design, namely the overlapping between the direct and interfering links due to the MIMO cellular topology as well as how to exploit the partial connectivity. We shall address these challenges and propose a three stage IA algorithm. As illustration, we analyze the achievable degree of freedom (DoF) of the proposed algorithm for a symmetric partially connected MIMO cellular network. We show that there is significant DoF gain compared with conventional IA algorithms due to partial connectivity. The derived DoF bound is also backward compatible with that achieved on fully connected K-pair MIMO interference channels.Comment: Submitted to IEEE Transactions on Signal Processing, accepte

CSI Feedback Reduction for MIMO Interference Alignment

Interference alignment (IA) is a linear precoding strategy that can achieve optimal capacity scaling at high SNR in interference networks. Most of the existing IA designs require full channel state information (CSI) at the transmitters, which induces a huge CSI signaling cost. Hence it is desirable to improve the feedback efficiency for IA and in this paper, we propose a novel IA scheme with a significantly reduced CSI feedback. To quantify the CSI feedback cost, we introduce a novel metric, namely the feedback dimension. This metric serves as a first-order measurement of CSI feedback overhead. Due to the partial CSI feedback constraint, conventional IA schemes can not be applied and hence, we develop a novel IA precoder / decorrelator design and establish new IA feasibility conditions. Via dynamic feedback profile design, the proposed IA scheme can also achieve a flexible tradeoff between the degree of freedom (DoF) requirements for data streams, the antenna resources and the CSI feedback cost. We show by analysis and simulations that the proposed scheme achieves substantial reductions of CSI feedback overhead under the same DoF requirement in MIMO interference networks.Comment: 30 pages, 7 figures, accepted for publication by IEEE transactions on signal processing in June, 201

Medulloblastomas overexpress the p53-inactivating oncogene WIP1/PPM1D

Medulloblastoma is the most common malignant brain tumor of childhood. Despite numerous advances, clinical challenges range from recurrent and progressive disease to long-term toxicities in survivors. The lack of more effective, less toxic therapies results from our limited understanding of medulloblastoma growth. Although TP53 is the most commonly altered gene in cancers, it is rarely mutated in medulloblastoma. Accumulating evidence, however, indicates that TP53 pathways are disrupted in medulloblastoma. Wild-typep53-induced phosphatase 1 (WIP1 or PPM1D) encodes a negative regulator of p53. WIP1 amplification (17q22-q23) and its overexpression have been reported in diverse cancer types. We examined primary medulloblastoma specimens and cell lines, and detected WIP1 copy gain and amplification prevalent among but not exclusively in the tumors with 17q gain and isochromosome 17q (i17q), which are among the most common cytogenetic lesions in medulloblastoma. WIP1 RNA levels were significantly higher in the tumors with 17q gain or i17q. Immunoblots confirmed significant WIP1 protein in primary tumors, generally higher in those with 17q gain or i17q. Under basal growth conditions and in response to the chemotherapeutic agent, etoposide, WIP1 antagonized p53-mediated apoptosis in medulloblastoma cell lines. These results indicate that medulloblastoma express significant levels of WIP1 that modulate genotoxic responsiveness by negatively regulating p53

Partial CSI feedback design for interference alignment in MIMO cellular networks

Interference alignment (IA) can achieve the optimal capacity scaling with respect to SNR but most existing IA designs require full channel state information (CSI) at the transmitters. In this paper, we consider IA processing with partial CSI feedback in MIMO cellular networks and we use the feedback dimension to quantify the first order CSI feedback cost. Conventional IA cannot be used because only partial CSI knowledge can be used to design the IA pre-coders. Therefore, we establish a new set of feasibility conditions for IA under the proposed partial CSI feedback scheme. Based on these results, we formulate the problem of CSI feedback dimension minimization subject to the constraints of IA feasibility. We further propose an asymptotically optimal solution and derive closed-form trade-off results between the CSI feedback cost and IA performance in MIMO cellular networks. © 2014 IEEE