Channel Direction Information Research Articles

LKC-Net: large kernel convolution object detection network

Deep learning-based object detection methods have achieved great performance improvement. However, since small kernel convolution has been widely used, the semantic feature is difficult to obtain due to the small receptive fields, and the key information cannot be highlighted, resulting in a series of problems such as wrong detection, missing detection, and repeated detection. To overcome these problems, we propose a large kernel convolution object detection network based on feature capture enhancement and vast receptive field attention, called LKC-Net. Firstly, a feature capture enhancement block based on large kernel convolution is proposed to improve the semantic feature capturing ability, and depth convolution is used to reduce the number of parameters. Then, the vast receptive filed attention mechanism is constructed to enhance channel direction information extraction ability, and it is more compatible with the proposed backbone than other existing attention mechanisms. Finally, the loss function is improved by introducing the SIoU, which can overcome the angle mismatch problem between the ground truth and prediction box. Experiments are conducted on Pascal VOC and MS COCO datasets for demonstrating the performance of LKC-Net.

Optimal Transmit Power and Channel-Information Bit Allocation With Zeroforcing Beamforming in MIMO-NOMA and MIMO-OMA Downlinks

In downlink, a base station (BS) with multiple transmit antennas applies zeroforcing beamforming to transmit to single-antenna mobile users in a cell. We propose the schemes that optimize transmit power and the number of bits for channel direction information (CDI) for all users to achieve the max-min signal-to-interference plus noise ratio (SINR) fairness. The optimal allocation can be obtained by a geometric program for both non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA). For NOMA, 2 users with highly correlated channels are paired and share the same transmit beamforming. In some small total-CDI rate regimes, we show that NOMA can outperform OMA by as much as 3 dB. The performance gain over OMA increases when the correlation-coefficient threshold for user pairing is set higher. To reduce computational complexity, we propose to allocate transmit power and CDI rate to groups of multiple users instead of individual users. The user grouping scheme is based on K-means over the user SINR. We also propose a progressive filling scheme that performs close to the optimum, but can reduce the computation time by almost 3 orders of magnitude in some numerical examples.

PCCA-Model: an attention module for medical image segmentation.

Convolutional neural networks have been increasingly employed in the field of medical image segmentation. Based on the idea that the human visual cortex differs in terms of the size of the receptive field and can sense the stimulus location, we propose the pyramid channel coordinate attention (PCCA) module to fuse multiscale features in the channel direction, aggregate local and global channel information, combine them with the location information in the spatial direction, and then integrate them into the existing semantic segmentation network. We conducted numerous experiments on the datasets, namely LiTS, ISIC-2018, and CX, and obtained state-of-the-art results.

Framed Fidelity MAC: Losslessly packing multi-user transmissions in a virtual point-to-point framework

As the number of subscribers in wireless communication systems grows rapidly, it has become important to design efficient medium access control (MAC) protocols to realize resources sharing among multiple users. When designing a MAC, effective management of co-channel interference (CCI) among multiple concurrent transmissions is critical to enhancing system’s spectral efficiency (SE). Although MACs based on existing signal processing can suppress/mitigate CCI among multiple concurrent transmissions, they either consume communication resources or attenuate the desired signals’ transmission, hence incurring loss of transmission performance. To remedy this deficiency, we propose a Framed Fidelity MAC (F2MAC) to losslessly pack multi-user data transmissions into a virtual point-to-point (p2p) framework. Under F2MAC, the common receiver (Rx) selects a virtual framed-channel (a.k.a. framework) and broadcasts it to all mobile terminals. The latter calculate their precoding vectors based on the received virtual framework and their respective communication channels with respect to (w.r.t.) the Rx. Then, the pre-processed signals of multiple users are sent to the Rx via different eigenmodes of the framed-channel. By applying a receive filter to the received mixed signal, Rx can recover users’ desired data without interference. Since F2MAC can decouple the channel quality information (CQI) from channel direction information (CDI) related to the user’s signal transmission, — i.e., CQI is exclusively determined by the user’s communication channel while CDI depends on the unified framework, it can avoid the transmission performance loss incurred by existing signal processing based MAC which adjusts both CQI and CDI simultaneously. Moreover, we propose a Flexible Framed Fidelity MAC (F3MAC) to further improve the system’s SE. By dynamically re-constructing the virtual framed-channel at each mobile user, the user’s transmission can be realized with the principal eigenmode’s gain of his/her communication channel. Our in-depth analysis and simulation results have shown the proposed MAC methods to significantly improve the SE of multi-user communication systems.

Intelligent Optimization of Base Station Array Orientations via Scenario-Specific Modeling

Fifth-generation (5G) wireless communications confront explosive growth in mobile data demand and massive intensive user equipment (UE) connections. The optimization of base station (BS) array orientations in large-scale networks can provide high potential performance gain to meet these requirements. However, traditional schemes are highly dependent on experience and difficult in implementation due to their demand on repeated drive tests and large amounts of data samples. In this paper, via exploiting UE locations and channel direction information, we propose an intelligent network optimization framework to maximize the long-term network rate performance. For the augmentation of limited drive test data, a deep Gaussian process regression (DGPR) model is designed to construct a scenario-specific channel modeling. In addition, via a domain-knowledge driven fusion of convolutional neural network (CNN) and multi-layer perceptron (MLP), we propose a multi-branch deep neural network (DNN) to accurately map BS array orientations and concise channel measurements to the network performance. Finally, based on the scenario-specific modeling, an efficient gradient search approach is proposed to optimize BS array orientations via neural network (NN) backpropagation. Both simulations and field tests in 5G experimental networks validate the effectiveness and efficiency of our proposed framework, especially with limited drive test data.

On Optimizing Feedback-Rate Allocation for Downlink MIMO-NOMA With Quantized CSIT

We consider multiple-input multiple output (MIMO) non-orthogonal multiple access (NOMA) downlink channels with zeroforcing beamforming transmission. The base station has multiple transmit antennas while all mobile devices have single receive antenna. With a limited feedback rate, channel direction information (CDI) needs to be quantized and fed back from mobile users to a base station. Thus, the accuracy of the quantized channel state information at the transmitter (CSIT) will depend on the feedback rate. To increase spectral efficiency, 2 active users in some clusters share the same beamforming vector and thus, will interfere fully with each other. Given a total-feedback rate, we analyze the feedback allocation for clusters with single active user and 2 active users. The objective is to either minimize the maximum outage probability or maximize the minimum rate among all active users in a cell. We show that the proposed feedback allocation is close to the optimum. Some numerical examples show that the resulting rate performance with the proposed feedback allocation is increased by 100% over that with the uniform feedback allocation.

Adaptive feedback bits and power allocation for dynamic TDD systems

In next-generation wireless communication systems, a dynamic time division duplex (TDD) system is promising to support simultaneous uplink (UL) and downlink (DL) transmissions. We herein consider a fixed number of feedback bits per DL user, where each DL user reports its quantized channel direction information (CDI) of the interference channel to a serving DL base station via its dedicated feedback channel. Based on the quantized CDI feedback, we design the UL and DL transmit zero-forcing beamforming (ZFBF) to mitigate the interference signals caused by the coexistence of the UL and DL transmissions. To maximize the DL data rate, the challenge of interference cancellation lies in how many feedback bits are adaptively allocated for the transmission of the quantized CDI of each channel link. This paper proposes an adaptive feedback bits allocation scheme which minimizes the mean rate loss between perfect channel knowledge and limited feedback system. We also propose the UL transmit power allocation scheme under the interference power constraint from the UL user to the DL user when the number of feedback bits for the quantized CDI is given. To jointly solve the feedback bits allocation and the UL transmit power allocation problems, we use an iterative scheme to maximize the DL data rate while satisfying the constraint of interference power. Finally, we investigate how many feedback bits per DL user are required to maintain a constant mean rate loss. The simulation results show that the proposed adaptive feedback bits allocation scheme significantly increases the DL data rate compared with the conventional schemes.

Antenna Diversity for Downlink MIMO-NOMA Systems With Partial Channel State Information

In this letter, antenna diversity is used to improve the outage performance of downlink multiple-input multiple-output non-orthogonal multiple access systems with partial channel state information, of which the transmitter only knows the distance and channel direction information associated with each user. We analyze the ergodic rate and the outage probability of the proposed system. Moreover, we derive the closed-form expression of each user’s outage probability. Numerical results confirm the accuracy of our analysis.

Joint Beamforming and Power Optimization for D2D Underlaying Cellular Networks

This paper studies the optimal joint beamforming and power control strategy for device-to-device (D2D) communication underlaying multiuser multiple-input multiple-output cellular networks. We consider multiple antennas at the base station (BS) and a single antenna at each cellular user (CU), D2D transmitter (DT), and D2D receiver (DR). We aim to minimize the total transmission power of the system by jointly designing the transmit beamforming at the BS and the transmit powers for both BS and DTs, while satisfying the signal-to-interference-plus-noise ratio based quality-of-service constraints for both CUs and DRs. Due to the nonconvex nature of the problem, we apply the semidefinite relaxation technique to find the optimal solution, which always satisfies the rank-one constraint. We also investigate three suboptimal fixed beamforming schemes: zero-forcing (ZF), regularized ZF, and hybrid maximum ratio transmission-ZF, where the focus is to minimize the total transmission power while reducing complexity. When perfect channel information is not available, we propose a robust transmit power minimization strategy with ZF beamforming that only requires limited feedback based channel direction information at the BS. Finally, computer simulation results are presented to demonstrate the effectiveness of the proposed schemes.

Cooperative Feedback Bits Allocation and Transmit Power Control in Underlay Cognitive Radio Networks.

In this paper, we consider an underlay cognitive radio network where the spectrum is shared with the primary network. Due to the coexistence of primary and secondary networks, primary users (PUs) are interfered with by the inter-network interference, at the same time secondary users (SUs) counteract the intra-network (inter-user) interference. Based on the cooperative feedback between the primary network and the secondary network, the secondary transmitter (ST) applies the cognitive beamforming to suppress the interference to PUs while improving the sum rate of SUs. We herein propose an adaptive feedback bits allocation among multiple PUs and SUs where the quantized channel direction information (CDI) for the interference channel is forwarded to the ST in order to utilize the beamforming. Moreover, based on the cognitive beamforming, we adjust the transmit power of the ST under the constraint of the average interference at PUs. To jointly solve the feedback bits allocation and the transmit power control problems, we formulate an optimization problem which requires a little iterations compared with the separated feedback bits allocation and the transmit power control problems. Numerical results show that the proposed scheme significantly improves the sum rate of SUs while satisfying the average interference constraint at PUs.

Limited Feedback Strategy For MU-MIMO Systems Under Spatially Correlated Channels

This paper studies limited feedback transmission strategy for downlink multiuser multiple-input multiple-output (MU-MIMO) systems under spatially correlated channels. To exploit the channel statistics for improving the performance of precoding at the base station (BS) with quantized channel direction information (CDI), we propose a two-level weighted DFT codebook (WDFT). It is composed of a long term DFT codebook representing the average CDIs and a short term codebook serving as an instantaneous weighting vector on them. The proposed codebook can adapt to various correlated channels without the need of channel correlation matrix at the BS. To mitigate the sum rate ceiling in high signal-to-noise ratio region caused by the quantized CDI with fixed size codebooks in less correlated channels, we propose a channel quality information (CQI) feedback strategy where each user helps the BS schedule users. By comparing its own CDI quantization error with a pre-determined threshold, each user feeds back one bit to indicate its preference of the transmission mode together with a corresponding CQI. With these information the BS first selects users separately from two user sets and finally selects users for downlink transmission. Simulation results show that the proposed limited feedback strategy including the WDFT codebook and the CQI feedback strategy outperforms existing schemes in various correlated channels.

Non-orthogonal multiple access in a downlink multiuser beamforming system with limited CSI feedback

Non-orthogonal multiple access (NOMA) has been recognized as a promising multiple access technology for fifth generation (5G) mobile communication system. However, the advantage of NOMA is only verified under the ideal condition that the transmitter has the perfect knowledge of channel state information (CSI). In this paper, NOMA downlink multiuser system, in which the transmitter acquires the CSI through limited feedback channel, is studied. Two traditional beamforming technologies, zero-forcing beamforming and random beamforming, are investigated in the NOMA downlink multiuser system. Making use of the imperfect CSI feedback, channel direction information (CDI), and channel quality indicator (CQI), we propose the user selection scheme to reduce the interference between the NOMA users. Furthermore, a power allocation scheme is proposed to improve the sum-rate of NOMA system. Numerical results show that NOMA system with limited feedback channel still gains larger system rate than traditional orthogonal multiple access system. It is also shown that random beamforming is more suitable for the NOMA system with limited CSI feedback.

Limited-Feedback Massive MISO Systems With Trellis-Coded Quantization for Correlated Channels

In this paper, we propose trellis coded quantization (TCQ) based limited feedback techniques for massive multiple-input single-output (MISO) frequency division duplexing (FDD) systems in temporally and spatially correlated channels. We exploit the correlation present in the channel to effectively quantize channel direction information (CDI). For multiuser (MU) systems with matched-filter (MF) precoding, we show that the number of feedback bits required by the random vector quantization (RVQ) codebook to match even a small fraction of the perfect CDI signal-to-interference-plus-noise ratio (SINR) performance is large. With such large numbers of bits, the exhaustive search required by conventional codebook approaches make them infeasible for massive MISO systems. Motivated by this, we propose a differential TCQ scheme for temporally correlated channels that transforms the source constellation at each stage in a trellis using 2D translation and scaling techniques. We derive a scaling parameter for the source constellation as a function of the temporal correlation and the number of BS antennas. We also propose a TCQ based limited feedback scheme for spatially correlated channels where the channel is quantized directly without performing decorrelation at the receiver. Simulation results show that the proposed TCQ schemes outperform the existing noncoherent TCQ (NTCQ) schemes, by improving the spectral efficiency and beamforming gain of the system. The proposed differential TCQ also reduces the feedback overhead of the system compared to the differential NTCQ method.

Tucker Decomposition For Rotated Codebook in 3-D MIMO System Under Spatially Correlated Channel

This correspondence proposes a new rotated codebook for a three-dimensional (3-D) multiple-input multiple-output (MIMO) system under a spatially correlated channel. To avoid the problem of high dimensionality led by a large antenna array, the rotation matrix in the rotated codebook is proposed to be decomposed by Tucker decomposition into three low-dimensional units, i.e., statistical channel direction information in horizontal and vertical directions, respectively, and statistical channel power in the joint horizontal and vertical direction. A closed-form suboptimal solution is provided to reduce the computational complexity in Tucker decomposition. The proposed codebook has a significant dimension reduction from conventional rotated codebooks and is applicable for a 3-D MIMO system with an arbitrary form of antenna array. Simulation results demonstrate that the proposed codebook works very well for various 3-D MIMO systems.

Feedback Overhead Analysis for Base Station Cooperative Transmission

In this paper, we analyze the feedback overhead of channel direction information for downlink coherent base station (BS) cooperative transmission. The per-cell codebooks are considered, which are of practice importance. Instead of analyzing the required number of bits for feedback to keep a constant rate loss, we analyze the required overhead to ensure a target average signal-to-interference-plus-noise ratio (SINR) of each user. To this end, we formulate an optimization problem of bit allocation among the codebooks for local and cross channels that minimizes the total number of bits under the constraint of the average SINR, and find the explicit expression of the solution. We proceed to study the impact of various system parameters and channel features on the overall feedback overhead. Analytical and simulation results reveal that the overhead scales linearly with the overall number of transmit antennas, but decreases with the grow of the cell-edge signal-to-noise ratio. Moreover, the overhead can be significantly reduced by exploiting the diverse performance requirements of the users and the heterogeneous channel features through allocating the number of bits for feedback among multiple users and multiple per-cell links. This provides useful insight for the feedback strategy design of BS cooperation systems.

Interwoven random unitary beamforming for MIMO broadcast channels

AbstractWe consider a multiple‐input multiple‐output broadcast channel where the transmitter performs unitary beamforming using the channel state information obtained through limited feedback from the receivers. A new feedback structure is proposed which considerably boosts the performance of the system compared with the conventional beamforming techniques like zero forcing beamforming or per user unitary and rate control. Because of the structure of the proposed feedback design, the new scheme is named interwoven random unitary beamforming or IRUB. In IRUB, the multiple‐input multiple‐output channel is in fact continuously decomposed into M orthogonal channels where the signal transmissions over them have timing offsets. User scheduling and beamforming in IRUB is very simple. User selection in IRUB is accomplished only based on the SINR feedbacks, and in each scheduling phase, only the scheduled users are inquired to send their channel direction information to the transmitter. Thereby, the feedback load of the system is considerably reduced. We provide an analysis for the achievable rate and its scaling law for the proposed scheme. Also, a 1‐bit feedback strategy is applied over IRUB to further reduce the total feedback load of the system. We further generalize IRUB for multi antenna receivers. The effectiveness of the IRUB method is validated through simulations. Copyright © 2016 John Wiley & Sons, Ltd.

Sensitivity Analysis for RVQ-Based Tomlinson–Harashima Precoded MIMO Systems

Tomlinson-Harashima precoded (THP) multiple-input-multiple-output (MIMO) systems are developed to cancel the intersymbol interference (ISI) at the transmitter. However, the THP at least requires perfect channel direction information (CDI) to completely mitigate the interference at the transmitter, which is not practical in the real world. The limited feedback systems are then practically developed to convey the quantized CDI to the transmitter. Using the quantized CDI to conduct the THP, an unavoidable interference degrades the performance at the receiver. In this paper, we investigate the performance degradation resulted from the random vector quantization (RVQ), which is a kind of CDI-based limited feedback mechanism. Here, we measure the performance degradation as the excess mean-squared error (EMSE). Since the formulation of the EMSE is a complicated function of the quantization error, we proposed using the second-order Taylor expansion to derive an analytical result. Simulation results have also verified our proposed analysis, showing that the derived analysis can match the actual EMSE.

Coordinated Regularized Zero-Forcing Precoding for Multicell MISO Systems with Limited Feedback

We investigate coordinated regularized zero-forcing precoding for limited feedback multicell multiuser multiple-input single-output systems. We begin by deriving an approximation to the expected signal-to-interference-plus-noise ratio for the proposed scheme with perfect channel direction information (CDI) at the base station (BS). We also derive an expected SINR approximation for limited feedback systems with random vector quantization (RVQ) based codebook CDI at the BS. Using the expected interference result for the RVQ based limited feedback CDI, we propose an adaptive feedback bit allocation strategy to minimize the expected interference by partitioning the total number of bits between the serving and out-of-cell interfering channels. Numerical results show that the proposed adaptive feedback bit allocation method offers a spectral efficiency gain over the existing coordinated zero-forcing scheme.

Vector Perturbation Precoding Under Quantized CSI

This paper focuses on the design of vector perturbation (VP) precoding for multiuser multiple-input–single-output (MU-MISO) downlink transmission under quantized channel side information (CSI). In particular, each receiver decomposes its downlink channel vector in forms of channel direction information (CDI) and channel magnitude information (CMI) for feedback to the transmitter. The CMI contribution is studied in two scenarios: i) perfect CMI available to the transmitter and ii) only CMI statistics known at the transmitter. Under these two scenarios, using the quantized CDI and quantization error statistics, the authors propose a unified approach to design the VP precoders that minimize the mean square error (MSE) between channel input and output. Closed-form expressions to the precoders are then derived. Bit-error-rate (BER) results indicate that the proposed VP precoder designs are less sensitive to quantization errors, and CMI availability offers significant performance improvements.

Codebook Size Design for RVQ-Based Tomlinson–Harashima Precoded MIMO Broadcast Channels

In this paper, we study the codebook size design strategies in the multiple-input–multiple-output (MIMO) broadcast channel (BC) systems. We adopt the nonlinear Tomlinson–Harashima precoder (THP), which is designed by the imperfect channel direction information (CDI) acquired from the random vector quantization (RVQ) feedback mechanism. Under the limitation of the total feedback rate, we flexibly allocate the size of each user's codebook so that the overall channel capacity is maximized. Since the design problem is not convex, directly finding out the optimum allocation of codebook size is not attainable. However, by sophisticatedly using the discrete majorization theory, we can derive the codebook size allocation strategies for the high- and the low-signal-to-noise-ratio (SNR) regions. Interestingly, the results show that the equal size codebook is preferable in the low-SNR region, whereas the codebook allocates the whole available codebook size to a single user in the high-SNR region. Simulations certify the theoretical result of our design.