In this paper, we propose two spectrum utilization improvement approaches (SUIAs), namely SUIA 1 and SUIA 2, based on whether or not the licensed 28 GHz millimeter-wave (mmWave) spectrum is allocated equally to each mobile network operator (MNO) in a country. Principally, SUIA 1 concerns with improving spectrum utilization if the 28 GHz spectrum is allocated equally to each MNO statically, also termed as static licensed spectrum allocation (SLSA). Whereas, SUIA 2 concerns with improving spectrum utilization if the spectrum is allocated unequally to each MNO in a flexible manner, also termed as flexible licensed spectrum allocation (FLSA). Since sharing and reusing of the licensed spectrum allocated to each MNO using either SLSA or FLSA can be exploited, and the secondary spectrum trading is required only in case of SLSA to redistribute the assigned spectra among MNOs, SUIA 1 employs SLSA, spectrum trading, spectrum sharing, and spectrum reusing techniques, whereas SUIA 2 employs FLSA, spectrum sharing, and spectrum reusing techniques. We present mathematical models for each technique and derive average capacity, spectral efficiency (SE), and energy efficiency (EE) metrics for SUIA 1 and SUIA 2. Extensive numerical and simulation results and analyses for SUIA 1 and SUIA 2 are carried out. It is shown that the spectrum reusing technique influences mostly SE and EE performances. Moreover, SE and EE performances of an MNO in SUIA 1 do not differ considerably from that in SUIA 2 such that depending on the spectrum allocation policies and practices in a country, either SUIA 1 or SUIA 2 can be employed. Finally, it is shown that by applying both SUIA 1 and SUIA 2, the prospective average SE and average EE requirements for sixth-generation (6G) mobile networks can be satisfied.

Approaches to Improve Millimeter-Wave Spectrum Utilization Using Indoor Small Cells in Multi-Operator Environments Toward 6G

OMP-based DOA estimation performance analysis

Adaptive compressed spectrum sensing (ACSS) can effectively save sampling resources in wideband spectrum sensing. Almost all of the existing ACSS algorithms are based on the discrete multitone signal model. However, real-world spectra are always multiband signals. In this paper, we derive mathematical models and algorithms enabling the ACSS suitable for multiband signals, which can save sampling resources and has lower computational complexity. Firstly, we introduce the multicoset sampling system into ACSS to sample multiband signals. Besides, we propose a leave-one-out cross-validation (LOOCV) based ACSS scheme with low sampling costs. To save sampling resources, we choose only one sampling channel as a testing subset to validate reconstructed signal and repeat this several times with different sampling channels. Then, we use the mean of the multiple validation results to determine the accuracy of the reconstructed signal. To reduce computational complexity, we propose a LOOCV-ACSS algorithm, in which we only perform the least square method several times in the LOOCV procedure, rather than the complicated compressed sensing reconstruction algorithms. Numerical simulations and real-world signal test results demonstrate that our derivation and algorithms are effective to reduce the sampling cost while keeping the same performance as conventional algorithms.

Adaptive Compressed Spectrum Sensing for Multiband Signals

Network latency will be a critical performance metric for the Fifth Generation (5G) networks expected to be fully rolled out in 2020 through the IMT-2020 project. The multi-user multiple-input multiple-output (MU-MIMO) technology is a key enabler for the 5G massive connectivity criterion, especially from the massive densification perspective. Naturally, it appears that 5G MU-MIMO will face a daunting task to achieve an end-to-end 1 ms ultra-low latency budget if traditional network set-ups criteria are strictly adhered to. Moreover, 5G latency will have added dimensions of scalability and flexibility compared to prior existing deployed technologies. The scalability dimension caters for meeting rapid demand as new applications evolve. While flexibility complements the scalability dimension by investigating novel non-stacked protocol architecture. The goal of this review paper is to deploy ultra-low latency reduction framework for 5G communications considering flexibility and scalability. The Four (4) C framework consisting of cost, complexity, cross-layer and computing is hereby analyzed and discussed. The Four (4) C framework discusses several emerging new technologies of software defined network (SDN), network function virtualization (NFV) and fog networking. This review paper will contribute significantly towards the future implementation of flexible and high capacity ultra-low latency 5G communications.

https://www.mdpi.com/1996-1073/12/18/3449/pdf

The four-C framework for high capacity ultra-low latency in 5G networks: A review

Hybrid precoding based on adaptive RF-chain-to-antenna connection for millimeter wave MIMO systems

In most of compressed sensing (CS) contributions, the sparsity of interesting signals is usually assumed to be prior. It may incur performance degradation of CS in such environments, where the sparsity order of channel occupancy changes dynamically. Therefore, sparsity order estimation (SOE) is vital, so that the sampling rate can be adjusted adaptively to exploit fully CS techniques. This paper studies the SOE problem for wideband cognitive radio. To investigate this issue, at first, the theoretical expression of sparsity order of the considered signals is derived via random matrix theory. The theoretical analysis shows that the sparsity order can be expressed with the ratio, which consists of the trace of measured signals' covariance matrix, compression sampling ratio, signal-to-noise ratio, and so on. And then, according to the theoretical expressions, a novel SOE approach is presented. In the proposed method, the trace of measured signals' covariance matrix is first calculated, and second, the sparsity order is estimated based on the trace. Compared with the previous work, the proposed algorithm, besides the lower computational complexity, has some superior performance, such as better robustness, smaller SOE error, and so on. Theoretical analysis and simulation results demonstrate the performance of the proposed approach.

Trace-Based Sparsity Order Estimation With Sparsely Sampled Random Matrices

For cooperative cognitive radio networks (CR-Nets), spectrum sensing is a key technology to detect spectrum holes which are inadequacy exploited by primary communication system. This paper presents a novel compressed spectrum sensing method based on the structure prior information of the primary user signal for cooperative CR-Nets. In the proposed method, fusion central (FC) recombines the measurements sent by different CRs into a matrix which has block joint sparse structure. Exploiting this structure feature, a Block Stagewise Orthogonal Matching Pursuit (BStOMP) based on multiple measurement vector algorithm is proposed to quickly and effectively detect the active statuses of primary users (PUs). Simulations show that compared with the previous works, the proposed method has lower computation complexity and reliable recovery performance.

Cooperative spectrum sensing based on Block Stagewise Orthogonal Matching Pursuit

ROD-based hybrid TH precoding and combining for mmWave large-scale MIMO systems

This paper presents an effective direction-of-arrival (DOA) estimation method based on a new computation method of the $\ell _{\infty }$ -norm for a complex matrix. Making use of the new computation method of the $\ell _{\infty }$ -norm, the DOA estimation problem can be efficiently formulated as a linear programming (LP) problem. Thus, the proposed method can effectively give the DOA estimation by finding the sparse coefficients which are obtained by solving the LP problem. Except for the first singular value decomposition of the data matrix, the proposed method is effectively implemented in the light of the LP theory based on real-valued computation. It avoids solving the complicated second-order cone programming problem. Furthermore, it can primely suppress spurious peaks in DOA estimation. Simulation results demonstrate the efficiency of the presented approach.

Fulai Liu

Papers

Trace-Based Sparsity Order Estimation With Sparsely Sampled Random Matrices

Cooperative spectrum sensing based on Block Stagewise Orthogonal Matching Pursuit

ROD-based hybrid TH precoding and combining for mmWave large-scale MIMO systems

LP-W- $\ell _{\infty }$ -SVD Algorithm for Direction-of-Arrival Estimation

Givens rotation based column-wise hybrid precoding for millimeter wave MIMO systems