PCA-Aided precoding for correlated MIMO broadcast channels

In this paper, we propose an efficient precoding solution for a correlated Multi-Input Multi-Output (MIMO) broadcast channels. Indeed, we apply the principal component analysis (PCA) to uncorrelate (whiten) the channel prior to codeword selection. As such, the channel state information at the transmitter (CSIT), picked up through finite-rate feedback, corresponds to the uncorrelated channel version. Thus, the optimality of the approach. The simulation results compared with conventional MIMO precoders for various levels of spatial correlation as well as different receive antenna settings, show that the proposed scheme provides greater system performance enhancement in terms of sum rate

Limited Feedback Precoding for Massive MIMO

The large-scale array antenna system with numerous low-power antennas deployed at the base station, also known as massive multiple-input multiple-output (MIMO), can provide a plethora of advantages over the classical array antenna system. Precoding is important to exploit massive MIMO performance, and codebook design is crucial due to the limited feedback channel. In this paper, we propose a new avenue of codebook design based on a Kronecker-type approximation of the array correlation structure for the uniform rectangular antenna array, which is preferable for the antenna deployment of massive MIMO. Although the feedback overhead is quite limited, the codebook design can provide an effective solution to support multiple users in different scenarios. Simulation results demonstrate that our proposed codebook outperforms the previously known codebooks remarkably

Blind CSI acquisition for multi-antenna interference mitigation in 5G networks

Future wireless communication networks are required to satisfy the increasing demands of traffic and capacity. The upcoming fifth generation (5G) of the cellular technology is expected to meet 1000 times the capacity that of the current fourth generation (4G). These tight specifications introduce a new set of research challenges. However, interference has always been the bottleneck in cellular communications. Thus, towards the vision of the 5G, massive multi-input multi-output (mMIMO) and interference alignment (IA) are key transmission technologies to fulfil the future requirements, by controlling the residual interference. By equipping the base-station (BS) with a large number of transmit antennas, e.g, tens of hundreds of antennas, a mMIMO system can theoretically achieve significant capacity with limited interference, where many user equipment (UEs) can be served simultaneously at the same time and frequency resources. A mMIMO offers great spatial degrees of freedom (DoFs), which boost the total network capacity without increasing transmission power or bandwidth. However, the majority of the recent mMIMO investigations are based on theoretical channels with independent and identically distributed (i.i.d) Gaussian distribution, which facilitates the computation of closed-form rate expressions. Nonetheless, practical channels are not spatially uncorrelated, where the BS receives different power ratios across different spatial directions between the same transmitting and receiving antennas. Thus, it is important to understand the behavior of such new technology with practical channel modeling. Alternatively, IA is known to break the bottleneck between the capacity of the network and the overall spectral efficiency (SE), where a performance degradation is observed at a certain level of connected user capacity, due to the overwhelming inter-user interference. Theoretically, IA guarantees a linear relationship between half of the overall network SE and the online capacity by aligning interference from all transmitters inside one spatial signal subspace, leaving the other subspace for desired transmission. However, IA has tight feasibility conditions in practice including high precision channel state information at transmitter (CSIT), which leads to severe feedback overhead. In this thesis, high-precision blind CSIT algorithms are developed under different transmission technologies. We first consider the CSIT acquisition problem in MIMO IA systems. Proposed spatial channel estimation for MIMO-IA systems (SCEIA) shows great offered spatial degrees of freedom which contributes to approaching the performance of the perfect-CSIT case, without the requirements of channel quantization or user feedback overhead. In massive MIMO setups, proposed CSIT strategy offered scalable performance with the number of the transmit antennas. The effect of the non-stationary channel characteristics, which appears with very large antenna arrays, is minimized due to the effective scanning precision of the proposed strategy. Finally, we extend the system model to the full dimensional space, where users are distributed across the two dimensions of the cell space (azimuthal/elevation). Proposed directional spatial channel estimation (D-SCE) scans the 3D cell space and effectively attains additional CSIT and beamforming gains. In all cases, a list of comparisons with state-of-the-art schemes from academia and industry is performed to show the performance improvement of the proposed CSIT strategies

LTE-Advanced radio access enhancements: A survey

Long Term Evolution Advanced (LTE-Advanced) is the next step in LTE evolution and allows operators to improve network performance and service capabilities through smooth deployment of new techniques and technologies. LTE-Advanced uses some new features on top of the existing LTE standards to provide better user experience and higher throughputs. Some of the most significant features introduced in LTE-Advanced are carrier aggregation, enhancements in heterogeneous networks, coordinated multipoint transmission and reception, enhanced multiple input multiple output usage and deployment of relay nodes in the radio network. Mentioned features are mainly aimed to enhance the radio access part of the cellular networks. This survey article presents an overview of the key radio access features and functionalities of the LTE-Advanced radio access network, supported by the simulation results. We also provide a detailed review of the literature together with a very rich list of the references for each of the features. An LTE-Advanced roadmap and the latest updates and trends in LTE markets are also presented

Compressive Sampled CSI Feedback Method Based on Deep Learning for FDD Massive MIMO Systems

Accurate downlink channel state information (CSI) is required to be fed back to the base station (BS) in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems in order to achieve maximum antenna diversity and multiplexing. However, downlink CSI feedback overhead scales with the number of transceiver antennas, a major hurdle for practical deployment of FDD massive MIMO systems. To solve this problem, we propose a compressive sampled CSI feedback method based on deep learning (SampleDL). In SampleDL, the massive MIMO channel matrix is sampled uniformly in time/frequency dimension before being fed into neural networks (NNs), which will reduce the computational resource/time at user equipment (UE) as well as enhance the CSI recovery accuracy at the BS. Both theoretical analysis and normalized mean square errors (NMSE) results confirm the advantages of the proposed method in terms of time complexity and recovery accuracy. Besides, a suitable CSI feedback period is explored by link level simulations, which aims to further reduce the overhead of CSI feedback without degrading the communication quality

Coding on Flag Manifolds for Limited Feedback MIMO Systems

The efficiency of the physical layer in modern communication systems using multi-input multi-output (MIMO) techniques is largely based on the availability of channel state information (CSI) at the transmitter. In many practical systems, CSI needs to be quantized at the receiver side before transmission through a limited rate feedback channel. This is typically done using a codebook-based precoding transmission, where the receiver transmits the index of a codeword from a pre-designed codebook shared with the transmitter. To construct such codes one has to discretize complex flag manifolds. For single-user MIMO with a maximum likelihood receiver, the spaces of interest are Grassmann manifolds. With a linear receiver and network MIMO, the codebook design is related to discretization of Stiefel manifolds and more general flag manifolds. In this thesis, coding in flag manifolds is studied. In a first part, flag manifolds are defined as metric spaces corresponding to subsurfaces of hyperspheres. The choice of distance defines the geometry of the space and impacts clustering and averaging (centroid computation) in vector quantization, as well as coding theoretical packing bounds and optimum constructions. For two transmitter antenna systems, the problem reduces to designing spherical codes. A simple isomorphism enables to analytically derive closed-form codebooks with inherent low-implementation complexity. For more antennas, the concept of orbits of symmetry groups is investigated. Optimum codebooks, having desirable implementation properties as described in industry standardization, can be obtained using orbits of specific groups. For large antenna systems and base station cooperation, a product codebook strategy is also considered. Such a design requires to jointly discretize the Grassmann and Stiefel manifolds. A vector quantization algorithm for joint Grassmann-Stiefel quantization is proposed. Finally, the pertinence of flag codebook design is illustrated for a MIMO system with linear receiver