Distributed congestion control (DCC) is a representative algorithm for improving the performance of Cellular-V2X (C-V2X) in dense vehicle scenarios. Since the existing DCC algorithms are not designed to respond to the target value of the Quality of Service (QoS) parameter adaptively, they can improve communication performance to some extent but do not guarantee the achievement of the target value. To solve this problem, we propose a deep Q network (DQN)-based QoS adaptive DCC algorithm in C-V2X Vehicle to Vehicle (V2V) communication. In particular, considering the limitations of the V2V network, where the exchange of state and reward information between vehicles is limited, we adopt a central controller (single agent) to train the congestion control policy and then distribute the completed policy to each vehicle for distributed execution. Simulation results show that the proposed algorithm satisfies the target QoS more effectively than the standardized ETSI DCC algorithm.

Joint transmission across the cluster of radio remote heads (RRHs) by exploiting the centralized processing of the cloud-radio access network (C-RAN) is a promising technique to overcome the severe interference problems in ultra-dense small cell networks. In practical considerations, local clustering of networks is preferred over global clustering as the number of RRHs that can be jointly transmitted on the network is finite. In this study, we attempt to revisit the principles of the well-known affinity propagation (AP) clustering algorithms, especially for the naive method of determining the exemplar with a fixed threshold. To further explain the decision method, we propose a method to easily determine the threshold using the network map of converged value of messages generated by AP algorithm, by combining Otsu's threshold and density peak searching method with the existing AP clustering algorithm. The proposed algorithm provides higher spectral efficiency than the conventional AP algorithms as well as statically coordinated multi-point (CoMP) techniques, although it has similar execution time as the traditional AP algorithms.

In this paper, we propose a channel estimation method based on a complex valued regression of the neural network for the IEEE 802.11p standard. It consists of the complex weighted summation optimized by feedforward neural network with backpropagation algorithm using initial estimated channel of the pilot and the long preamble. It also exploits the shift matrix in order to mitigate the effect from a systemic problems in IEEE 802.11p standards. The major problems of IEEE 802.11p standard are wide bandwidth of 10 MHz consisting of 64 subcarriers and relatively insufficient four pilot subcarriers at single ODFM symbol, which are unsuitable for a channel of vehicular environment. Despite these problems, the proposed method performs better than the conventional channel estimation methods. The performance of proposed scheme is provided with the comparison between constructed data pilots (CDP), Spectral Temporal Averaging (STA), and proposed scheme. The proposed channel estimation scheme has low mean square error (MSE) and bit error rate (BER) throughout the whole SNR region. It is the result from properly trained weight. At the low SNR region, especially, the performance of proposed scheme is much better than CDP and STA scheme. It is because of the noise suppression effect caused by a weighted summation algorithm.

The optimal number of clusters (K) differs depending on the radio remote head (RRH) density. This paper verifies that the K values cannot be met by the conventional affinity propagation (AP) clustering algorithm. In an ultra-dense network (UDN) environment, the density of RRH is a very important factor for the bender because it is directly related to the cost of configuring the wireless communication network. Likewise, in order to provide the optimal communication environment to the user in the UDN environment, it is necessary to enable flexible clustering according to changing channel environment by utilizing semi-dynamic clustering technology. As a result, we propose an AP algorithm that finds a better K value than the conventional method. To this end, the proposed algorithm additionally utilizes a non-coordinated multi-point (CoMP) interference power that varies depending on the RRH density, user position, and the variations in propagation channel. The simulation results show that the proposed algorithm shows a better average capacity than the conventional algorithm.

A minimum mean square error estimation (MMSE) using a virtual pilot channel estimation scheme was proposed by our researchers for improving the error performance of the IEEE 802.11p. However, compared to the STA channel estimation scheme, the MMSE scheme cannot provide an acceptable error performance in a low SNR region. To alleviate this, we propose an adaptive channel estimation scheme, which selectively uses a channel estimation scheme that has an error performance in-between that of the STA and the MMSE, through a decision method using a long preamble. Simulation results demonstrate that the proposed scheme can select a better channel estimation scheme, thereby demonstrating an excellent performance over the entire SNR range of interest.

Polarization division multiple access (PDMA) is a method to simultaneously transmit two independent signals by using vertical and horizontal polarization in the same frequency band. In PDMA systems, the received signal can experience significant interference from cross-polarized signals if the transmit and receive antennas are misaligned in terms of polarization state. In this paper, a precoding technique to solve this problem is discussed and a channel model to disentangle vertical and horizontal components from the received signal and include the rotation of receive antenna is proposed. The precoding technique is designed and analyzed using a channel model assuming a line-of-sight environment. To measure the performance of the precoding technique, a testbed is implemented using a software-defined radio platform. Simulation and measurement show that enhanced channel gain is achieved even when the polarization state between the transmit and receive antennas is mismatched. Comparison between simulation and measurements proves a basic and crucial principle concerning the capability of controlling the polarization state of a transmitted signal.

This paper analyzes the effects of polarization-mismatch and space-correlation to a multiple-input and single-output (MISO) channel which is observable in the near future cellular communications environments such as large-scale antenna arrays and small cells. The analysis is based on a polarization-mismatched and space-correlated MISO channel which is modeled from the conventional dual-polarized channel. In the MISO channel, polarization-mismatch is described by the polarization-mismatch angle which is uniformly distributed from 0 to the maximum polarization-mismatch angle and space-correlation is described by the exponential correlation model. Assuming high SNR, approximate expressions of the ergodic capacity are derived as a function of the transmit power, number of transmit antennas, maximum polarization-mismatch angle, and space-correlation coefficient in four representative environments: narrowly or widely spread polarization-mismatch angles and slightly or highly correlated channels. Further, the capacity loss introduced by polarization-mismatch and space-correlation is derived with respect to the maximum polarization-mismatch angle and space-correlation coefficient. It is shown that the capacity loss introduced by polarization-mismatch is upper bounded by 2 bit/s/Hz. Whereas, the capacity loss introduced by space-correlation increase with the number of transmit antennas and is upper bounded by 0.832 bit/s/Hz. Required resources to compensate for the capacity loss is derived as well.

In broadcast channels, network utility maximization formulates a scheduling problem in order to maximize a sum of given utility functions. It is known that the network utility maximization is achieved if a gradient method is used. In this paper, however, we show that the network utility maximization is achieved only if a gradient method is used. That proves the equivalence between a problem formulated by the network utility maximization and a problem with a gradient method. The gradient method simplifies the object function of a scheduling problem by modifying utility functions to a gradient form, so that it makes easy to deal with the problem. We apply the gradient method to a problem with utility functions given by generalized proportional fairness. It is revealed that using the gradient method for the generalized proportional fairness is equivalent to applying a binomial approximation. Simulation results are presented with various scheduling parameters.

This paper proposes a self-optimized coverage coordination scheme for two-tier femtocell networks, in which a femtocell base station adjusts the transmit power based on the statistics of the signal and the interference power that is measured at a femtocell downlink. Furthermore, an analytic expression is derived for the coverage leakage probability that a femtocell coverage area leaks into an outdoor macrocell. The coverage analysis is verified by simulation, which shows that the proposed scheme provides sufficient indoor femtocell coverage and that the femtocell coverage does not leak into an outdoor macrocell.

This paper proposes two interference mitigation strategies that adjust the maximum transmit power of femtocell users to suppress the cross-tier interference at a macrocell base station (BS). The open-loop and the closed-loop control suppress the cross-tier interference less than a fixed threshold and an adaptive threshold based on the noise and interference (NI) level at the macrocell BS, respectively. Simulation results show that both schemes effectively compensate the uplink throughput degradation of the macrocell BS due to the cross-tier interference and that the closed-loop control provides better femtocell throughput than the open-loop control at a minimal cost of macrocell throughput.

In this paper, for spatial multiplexing with limited feedback, a quantized principal component selection (QPCS) precoding scheme is proposed that achieves comparable capacity to the closed-loop multiple-input multiple-output (MIMO) and furthermore adapts to various fading channel conditions without any additional feedback bits and transmit channel state information (CSI). We propose a systematic design method for a codebook consisting of a finite number of unitary matrices based on a maximizing minimum distance criterion in the one- dimensional angular domain and show that the method outperforms the Grassmannian subspace packing method in various fading channel conditions. The proposed QPCS precoding scheme allows for adjustment of the precoding matrix based on limited feedback information on the principal vectors approximating a MIMO channel in the angular domain according to various channel conditions. Furthermore, for practical implementation of the QPCS precoding scheme, we propose a structured precoder optimization procedure and show that the proposed procedure induces a negligible capacity loss compared with the exhaustive precoder optimization, even with considerably reduced complexity.

In this paper, for spatial multiplexing with limited feedback, we propose quantized principal component selection (QPCS) precoding, wherein the active bases are selected at the receiver from a finite number of basis sets known at the both receiving and transmitting ends, conveyed to the transmitter using limited feedback, and assembled into a precoding matrix at the transmitter. The selected bases are conveyed to the transmitter using feedback information on both the index of the selected basis set, which defines the most appropriate set of coordinates for describing a multiple-input multiple-output (MIMO) channel, and the principal bases with the largest gain in the selected basis set. A systematic design method for a finite number of basis sets using the antenna array processing is presented. We show that the proposed QPCS precoding scheme provides comparable capacity with the closed-loop MIMO with full channel state information (CSI), even with limited feedback. Furthermore, for practical implementation of the QPCS precoding scheme, we propose a low complex structured precoder optimization procedure and show that the proposed procedure induces a negligible capacity loss compared with the exhaustive precoder optimization, even with considerably reduced complexity.

This paper designs a dual polarized channel utilizing the Spatial Channel Model (SCM), and investigates the performance for a certain environment. Using the proposed channel model; it is possible to estimate the channel capacity as a function of the cross polarization discrimination (XPD) and the spatial fading correlation. Through the channel characteristics, an algorithm using Eigen Value of channels is proposed to determine a transmit antenna configuration having the optimal capacity for the time varying propagation environments.

This letter proposes a low-complexity algorithm for transmit-antenna selection in a multi-input multi-output spatial multiplexing system with per-antenna rate control and an ordered successive interference cancellation receiver. The active antenna subset and its transmission rate are determined at the receiver, and conveyed to the transmitter using limited feedback. We propose a serial antenna-selection procedure consisting of a successive process that tests whether transmit-antenna selection gain exists when the antenna with the lowest signal-to-interference-plus-noise ratio (SINR) is discarded at each selection stage. Furthermore, we show that "reverse-detection ordering", whereby the signal with the lowest SINR is decoded at each stage of successive decoding, widens the disparities among fractions of the whole capacity allocated to each individual antenna, and thus maximizes a gain of antenna selection. Numerical results show that the proposed reverse-detection-ordering-based serial antenna selection induces a negligible capacity loss, compared with the exhaustive selection strategy, even with considerably reduced complexity.