Two 4-bit active phase shifters integrated with all digital control circuitry in 0.13-mu m RF CMOS technology are developed for X- and Ku-band (8-18 GHz) and K-band (18-26 GHz) phased arrays, respectively. The active digital phase shifters synthesize the required phase using a phase interpolation process by adding quadrature-phased input signals. The designs are based on a resonance-based quadrature all-pass filter for quadrature signaling with minimum loss and wide operation bandwidth. Both phase shifters can change phases with less than about 2 dB of RMS amplitude imbalance for all phase states through an associated DAC control. For the X- and Ku-band phase shifter, the RMS phase error is less than 10 degrees over the entire 5-18 GHz range. The average insertion loss ranges from -3 dB; to -0.2 dB at 5-20 GHz. The input P-1dB for all 4-bit phase states is typically - 5.4 +/- 1.3 dBm at 12 GHz in the X- and.Ku-band phase shifter. The K-band phase shifter exhibits 6.5-13 degrees of RMS phase error at 15-26 GHz. The average insertion loss is from -4.6 to -3 dB at 15-26 GHz. The input P-1dB of the K-band phase shifter is -0.8 +/- 1.1 dBm at 24 GHz. For both phase shifters, the core size excluding all the pads and the output 50 Omega matching circuits, inserted for measurement purpose only, is very small, 0.33 x 0.43 mm(2). The total current consumption is 5.8 mA in the X- and Ku-band phase shifter and 7.8 mA in the K-band phase shifter, from a 1.5 V supply voltage.

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0.13-mu m CMOS phase shifters for X-, Ku-, and K-band phased arrays - eScholarship

A wireless network's overall performance is greatly impacted by the accuracy of its channel estimate. Deep learning has also shown to be a significant advancement in improving communication reliability and lowering processing complexity for 5G and beyond networks. Massive multiple-input multiple-output (MIMO) and mmWave communications are disruptive technologies that have been implemented into fifth-generation (5G) and subsequent wireless communication standards, such as 6G, in order to handle the increasing wireless data traffic and maintain connections. MIMO systems use space multiplex by using a variety of antennas to increase efficiency at a specifically used bandwidth. MIMO allows for numerous sources and sinks to share a single communication channel. The characteristics of these networks are their spectral variety and their ability to multiplex spatially. This study still provided an overview of MIMO, examined MIMO channel estimation methods based on the 5G and 6G wireless communication standards, and analysed MIMO channel estimation methods for the coexistence of these two wireless network generations. 

A Comprehensive Review and Comparative analysis of 5G and 6G based MIMO Channel Estimation Techniques

A wireless network's overall performance is greatly impacted by the accuracy of its channel estimate. Deep learning has also shown to be a significant advancement in improving communication reliability and lowering processing complexity for 5G and beyond networks. Massive multiple-input multiple-output (MIMO) and mmWave communications are disruptive technologies that have been implemented into fifth-generation (5G) and subsequent wireless communication standards, such as 6G, in order to handle the increasing wireless data traffic and maintain connections. MIMO systems use space multiplex by using a variety of antennas to increase efficiency at a specifically used bandwidth. MIMO allows for numerous sources and sinks to share a single communication channel. The characteristics of these networks are their spectral variety and their ability to multiplex spatially. This study still provided an overview of MIMO, examined MIMO channel estimation methods based on the 5G and 6G wireless communication standards, and analysed MIMO channel estimation methods for the coexistence of these two wireless network generations.

This paper develops a new approach to the modeling and analysis of device-to-device (D2D) underlaying multi-tier cellular network for dense hotspot communications, which consists of macro base stations (MBSs), pico BSs (PBSs), femto BSs (FBSs). A typicl user equipment (UE) can work either in D2D mode or cellular mode. Considering the dense hotspot communications, this work employs Poisson point process (PPP) to model the locations of MBSs and PBSs, and uses Poisson cluster process (PCP) to model the ones of UEs and FBSs. The locations of PBSs are also modeled as the centers of hotspots, referred to as the centers of PCPs. UEs and FBSs cluster around the common parent process PBSs. To guard the cluster-edge UEs, the clustered-UE classification and modified fractional frequency reuse (FFR) are jointly used, by which both the UEs and FBSs are classified two sets, cluster-center UEs and cluster-edge UEs, cluster-center FBSs and cluster-edge FBSs, respectively. The total frequency band is divided into two orthogonal segments, one of which is shared by D2D devices, cluster-edge FBSs, and PBSs, and the other segment of which is shared by cluster-center FBSs and MBSs. For such clustered multi-tier network, by using the methods from PPP, PCP, and PHP, this paper presents a tractable approach for modeling and analyzing the performance of cellular and D2D networks and gives the statistical descriptions of the experienced interferences at a typical D2D or cellular receiver by using the approximated Poisson hole processes (PHP) theory. This yields the derivations of the coverage probabilities of both the D2D receivers and cellular destinations. In additon, during the analysis of cellular UEs, to derive the coverage probabilities, this paper specially constructs one UE association criterion as well as the derivations of both the association probabilities and the statistical descriptions of association distances for cluster-center and cluster-edge UEs. The simulations results exploit the effect of various network parameters on the network performance and give the insights in terms of the proposed schemes as well as the comparison between cluster-center and cluster-edge UEs. 

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Modeling D2D Underlaying Cellular Network for Hotspot Communications with Poisson Cluster and Hole Processes

Channel Estimation for Sparse mm-Wave MIMO System

The mmW cellular systems of large bandwidths offer multiple times rise in capacity as compared to existing 4G networks with comparable cell density. These avoid the unnecessary cell splitting by enlarging the capacity of individual tiny cells significantly in a scenario of very high-density cell deployments. This work will provide an opportunity to achieve a particular capacity value by varying the mmW channel gain in the 5G and 6G wireless networks. The OMP algorithm is modified and the existing sparse signal processing concept is utilized for the capacity analysis of hybrid MIMO here. The capacity (b/s/Hz) of the conventional and hybrid MIMOs are calculated and compared against given SNR range (dB) in a 5G mmW heterogeneous network under different values of mmW channel gain. It has been found that capacity analysis curves of conventional and hybrid MIMOs both show a descending trend with the increase in SNR range as the channel gain is increased due to over-saturation of the used sparse mmW channel. These curves exhibit local variation, time dependence, frequency selectivity, reliable communication rate, and diversity on both ends and the use of MIMO has a gain in degrees of freedom. The hybrid MIMO due to hardware limitations of conventional MIMO utilized a large number of antennas with lesser radio frequency chains. At some point of channel gain, its capacity curve approached the capacity curve of conventional MIMO in a moderate SNR range, and approached very closely for all channel gains in low and high SNR ranges.

Capacity Analysis of Hybrid MIMO Using Sparse Signal Processing in mmW 5G Heterogeneous Wireless Networks


 Millimeter Wave (mm-Wave) - massive Multiple-Input Multiple-Output (MIMO) technology has been a subject of today’s growing interest in both industry and academia for future wireless standards and has significant potential to provide considerable gains in data rates, link reliability and Energy Efficiency (EE).Sparse recovery has great capability in Channel Estimation (CE) for mm-Wave - Massive-MIMO (Ma-MIMO) heterogeneous wireless networks and in this context; the existing better candidate “Orthogonal Matching Pursuit” (OMP) algorithm is modified for CE in such networks. This paper will provide an opportunity in setting up of such a network, and the practical observation of the effect of a change in threshold, sparsity and noise levels as well as quantity of Radio Frequency (RF) chain systems. The performance analysis of CE accuracy in terms of Normalized Mean Square Error (NMSE)verses a given Signal-to-Noise (SNR) range in dB is calculated using this modified algorithm, much better than the existing OMP methods and compared against the ideal Genie case under a wide range of noise encountered. It has been observed that in a very high noise environment, NMSE of this noise resistant algorithm is approximate (100.5 to 10-3.5) in low SNR range (-10 to 30) dB and approximate (10-1.5 to10-5.5) in high SNR range (10 to 50) dB. It comes out to be approximate (10-1 to more than 10-5) in case of combined effect involvingthe reduction of quantity of RF chain systems to half and 10 times enhancement of threshold level in a very high noise environment and low SNR range.

Sanjeev Chopra

Papers

Capacity Analysis of Hybrid MIMO Using Sparse Signal Processing in mmW 5G Heterogeneous Wireless Networks

Performance Analysis of Channel Estimation in 5G millimeter-Wave-MIMO Heterogeneous Systems