Range-based positioning systems require accurate locations of anchor nodes; however, obtaining these locations is often costly and time-consuming. In this paper, we propose a novel framework to automatically estimate the locations of Wi-Fi access points (APs) using unlabeled data passively collected from mobile users as they naturally navigate the environment. The proposed framework is primarily based on the observation that vertical movement within a building is constrained to a limited number of transition points, such as escalators and elevators. Accordingly, the framework first detects vertical transitions of users by analyzing barometer readings. By simultaneously considering the transitions of all users, it determines the absolute floor level of each user throughout the building. Next, user trajectories are reconstructed using inertial sensor measurements and partitioned into segments corresponding to individual floors. A cost function is designed to align each segmented trajectory on the determined floor level by selecting the most probable entry and exit points where vertical transitions are possible. The locations of APs are then estimated using Wi-Fi signal strengths measured along the aligned trajectories. The effectiveness of the proposed framework was evaluated in a large-scale, 10-floor shopping mall, where each floor spans up to 30,000 m. Using unlabeled datasets collected from multiple users, the locations of 1,214 APs operating on the 2.4 GHz band were estimated. Based on this database, user locations could be obtained during the online phase, demonstrating an average accuracy of less than 5 m. This result confirms the successful operation and practical applicability of the proposed framework.

For high-level autonomous driving, the development of an effective vehicle-to-vehicle (V2V) communication system is an utmost concern. Among several candidates, the millimeter-wave (mmWave) V2V communication system is considered as a promising one, as it is expected to provide high data rate. To overcome the high path loss at mmWave frequencies, the codebook-based beam-alignment approach has been widely studied as a means of generating narrow and strong beams. Despite its potential, many existing codebooks are designed to generate codewords with equal beamwidths, which may not be optimal for V2V communications. Our focus is on designing a beamforming codebook for V2V communications, taking into account the angular distribution characteristics of vehicles on the road. To achieve this, we propose an analytical model to represent the traffic density of vehicles on roads. Based on the proposed traffic density model, we derive the angular distribution of directional communication links between vehicles by considering realistic road structures, including straight roads, intersections, and roundabouts. Utilizing the angular distribution between vehicles, we propose a codebook design framework specifically tailored for V2V communication systems. Simulation results verify that our model fits well for realistic urban grid road environments, and the proposed codebook outperforms codebooks with equal beamwidths.

In this paper, we propose a power allocation algorithm to guarantee quality of experience (QoE) while considering the impact of imperfect channel state information (CSI) in multi-connectivity multiple-input multiple-output (MIMO) systems. Multi-connectivity is a promising solution for wireless technologies to fulfill ever-increasing demands for QoE via massive MIMO. However, the performance of MIMO systems mostly depends on the accuracy of the channel estimation algorithm that provides CSI. Under imperfect CSI scenarios, increasing transmit power to satisfy QoE for one user exacerbates interference with others, thus leading to a tradeoff between power consumption and user satisfaction. To guarantee QoE while minimizing transmission power under imperfect CSI, the effect of imperfect CSI is quantified, and a closed form signal-to-interference-plus-noise ratio (SINR) is derived. Then, a two-stage algorithm categorizes UEs into two groups: those that only need single connectivity and those that need dual connectivity (i.e., adaptive connectivity). After classification, transmission power is allocated to guarantee QoE via a power control strategy that minimizes the transmission power. By comparing performance with a single connectivity–based algorithm and fixed multi-connectivity–based algorithms, we show that our proposed algorithm not only satisfies all the UEs in the system but also consumes less transmission power than the benchmarks.

This paper proposes a beamforming design for millimeter-wave (mmWave) backhaul systems with dual-polarization antennas in uniform planar arrays (UPAs). The proposed design method optimizes a beamformer to mimic an ideal beam pattern, which has flat gain across its coverage, under the dominance of the line-of-sight (LOS) component in mmWave systems. The dual-polarization antenna structure is considered as constraints of the optimization. Simulation results verify that the resulting beamformer has uniform beam pattern and high minimum gain in the covering region.

The following topics are dealt with: wireless channels; optimisation; resource allocation; cellular radio; learning (artificial intelligence); MIMO communication; telecommunication network reliability; telecommunication traffic; 5G mobile communication; radiofrequency interference.

This paper proposes hybrid beamforming designs for millimeter-wave (mmWave) multiple-input multiple-output (MIMO) backhaul systems equipped with uniform planar arrays (UPAs) of dual-polarization antennas at both the transmit and receive base stations. The proposed beamforming designs are to near-optimally solve optimization problems taking the dual-polarization UPA structure into account. Based on the solutions of optimization problems, this paper shows it is possible to generate the optimal dual-polarization beamformer from the optimal single-polarization beamformer sharing the same optimality. As specific examples, squared error and magnitude of inner product are considered respectively for optimization criteria. To optimize proposed beamformers, partial channel information is needed, and the use of low overhead pilot sequences is also proposed to figure out the required information. Simulation results verify that the resulting beamformers have the most uniform gain (with the squared error criterion) or the highest average gain (with the magnitude of inner product criterion) in the covering region with the UPA of dual-polarization antennas.

In this paper, a new method of fast beam search and refinement is proposed for millimeter-wave hybrid beamforming systems. The proposed method is based on a two-level phased array approach in which the first-level phased array is formed by actual analog-domain subarrays, and the second-level phased array is virtually formed by the outputs of the first-level subarray phased combiners. Exploiting the fact that the overall beam pattern of the two-level array is the product of the beam patterns of the two levels, the proposed method searches angle-of-arrivals by sweeping coarse-resolution analog training beams at the first-level phased array and matching the first-level subarray outputs with fine-resolution digital training beams by parallel fast Fourier transform (FFT) filtering. In this way, the overall beam search resolution of the proposed method is given by the fine resolution of the second-level virtual array only with the overhead of coarse beam sweeping at the first-level subarrays. Hence, the proposed method provides a very efficient way of beam training and refinement. The performance of the proposed method including the directional ambiguity, beam search latency, and computational complexity is analyzed. Numerical results show the effectiveness of the proposed method.

Fifth generation wireless networks are expected to utilize wide bandwidths available at millimeter wave (mmWave) frequencies for enhancing system throughput. However, the unfavorable channel conditions of mmWave links, such as, higher path loss and attenuation due to atmospheric gases or water vapor, hinder reliable communications. To compensate for these severe losses, it is essential to have a multitude of antennas to generate sharp and strong beams for directional transmission. In this paper, we consider mmWave systems using uniform planar array (UPA) antennas, which effectively place more antennas on a 2-D grid. A hybrid beamforming setup is also considered to generate beams by combining a multitude of antennas using only a few radio frequency chains. We focus on designing a set of transmit beamformers generating beams adapted to the directional characteristics of mmWave links assuming a UPA and hybrid beamforming. We first define ideal beam patterns for UPA structures. Each beamformer is constructed to minimize the mean squared error from the corresponding ideal beam pattern. Simulation results verify that the proposed codebooks enhance beamforming reliability and data rate in mmWave systems.

Uniform planar antenna (UPA) structures are expected to become popular for massive multiple-input multiple-output (MIMO) systems because they enable deployment of a large number of antennas in limited space. In frequency division duplexing (FDD) systems, quantized channel state information (CSI) should be fed back from users to base stations to make adaptive transmission possible. However, it is difficult to accurately quantize massive MIMO channels due to their large dimensions. In this paper, we propose practical CSI quantizers for full-dimension (FD) MIMO systems employing UPAs. We focus on quantizing and combining dominant beams in channels by keeping realistic channel properties and antenna structures into account. To scan a channel space, we also develop a multi-round beam search approach. Numerical results verify that the proposed quantizers show better quantization performance than the previous channel quantization techniques.

Small cell networks utilizing millimeter wave (mmWave) links are expected to enhance system throughput, since the wide bandwidths at mmWave frequencies can afford high data rates. Due to mmWave's unfavorable channel conditions, it is necessary for mmWave communication systems to use beamforming with a large number of antennas to generate sharp and strong beams. In this paper, we propose a codebook design algorithm for the beamforming by considering the directional characteristic of mmWave links. The proposed codebook design algorithm can be easily adapted to different kinds of antenna arrays. Simulation results show that the proposed codebook outperforms previously reported codebooks for mmWave systems.

Fifth-generation wireless systems are expected to employ multiple-antenna communication at millimeter wave (mmWave) frequencies using small cells within heterogeneous cellular networks. The high path loss of mmWave and the physical obstructions make communication challenging. To compensate for the severe path loss, mmWave systems may employ a beam alignment algorithm that facilitates highly directional transmission by aligning the beam direction of multiple antenna arrays. This paper discusses a mmWave system employing dual-polarized antennas. First, we propose a practical soft-decision beam alignment (soft-alignment) algorithm that exploits orthogonal polarizations. By sounding the orthogonal polarizations in parallel, the equality criterion of the Welch bound for training sequences is relaxed. Second, the analog beamforming system is adapted to the directional characteristics of the mmWave link, assuming a high Ricean K-factor and poor scattering environment. A soft-alignment algorithm enables the mmWave system to align a large number of narrow beams to the channel subspace in an attempt to effectively scan the mmWave channel. Third, we propose a method to efficiently adapt the number of channel sounding observations to the specific channel environment based on an approximate probability of beam misalignment. Simulation results show that the proposed soft-alignment algorithm with adaptive sounding time effectively scans the channel subspace of a mobile user by exploiting polarization diversity.