Session 18: PHY-4: MIMO
Thursday, 9 June 2022, 16:00-17:30
Session Chair: TBD ( , )
On the Performance Analysis of Different Selecting Strategies for Type-II Codebook
Xiaotian Fu and Didier Le Ruyet (CNAM, France); Raphael Visoz and Thierry Clessienne (Orange Labs, France)
The Type-II codebook is standardized in recent 5G new radio (NR) physical layer release, which supports high resolution channel state information feedback for advanced MIMO transmission. In this paper, we propose strategies to select the standardized Type-II precoder matrix. We first extend a rank1 solution in the literature to rank2 which is referred to approach1. This approach is subject to its prerequisite which wastes certain precoder matrix indicator bits. To further exploit the feedback bits, we proposed a new selecting strategy, named approach2, for Type-II precoder search. Besides, we explore the subband scalar quantization in the standard and reveal the fact that the approaches compliant to the standard encounter the trade-off between the orthogonality and the gains of the formed beams. In the simulation, the approach2 is more efficient than the approach1 in high and medium correlation channels, which is the opposite in the low correlation channel. On the other hand, the approach1 is simpler than the approach2 in terms of computational complexity.
Limited Feedback Design for Massive Full Dimension MIMO Systems
Berna Özbek (Izmir Institute of Technology, Turkey); Caner Arslan (Ulak Haberlesme, Turkey); Mahmut Demirtas (Bahcesehir University, Turkey); Hüsne Şahan (Nokia Solutions and Networks, Poland & Istanbul Medipol University, Turkey); Furkan Kerim Kadı ( & Ulak Haberleşme, Turkey); Erdem Elcı (Telecommunication Company & ULAK Communication Inc., Turkey)
Massive Multiple-input Multiple-output (MIMO) systems where the base station (BS) is equipped with hundreds of antennas serve simultaneously multiple users to increase spectral efficiency in wireless communication systems. Using two dimension antenna design for massive MIMO systems namely massive FD-MIMO, the overall system performance is further improved. For the massive FD-MIMO systems, the availability of channel state information (CSI) at the BS is essential to achieve overall performance gain. In this paper, we design limited feedback algorithm for massive FD-MIMO by considering two separate codebooks for horizontal and elevation domains to improve the sum data rate performance. The simulation results
are provided for the proposed scheme by considering 3-dimension wireless channel models.
Multi-Cell MIMO Power Minimization via Rate Balancing with Partial CSIT
Imène Ghamnia (Sequans Communications, France); Dirk Slock (EURECOM, France); Yi Yuan-Wu (Orange Labs, France)
In this work, we consider the power allocation problem via rate balancing optimization w.r.t. imperfect Channel State Information at the Transmitter (CSIT), namely: the ergodic user rate balancing. In particular, we study two closely related optimization problems: maximizing the minimum ergodic user rate under per cell transmit power constraints, and minimizing the transmit power while satisfying per user rate targets. The max-min rate approach combines an operation of balancing at the user level and sum rate maximization at the level of the user streams. For imperfect CSIT, we exploit an approximation of the expected rate as the Expected Signal and Interference Power (ESIP) rate, based on an original minorizer for every individual rate term. Then, the transmit power is minimized while fulfilling user rate requirements when the latter are feasible. Also, we handle the power minimization problem with two variations: minimizing the total transmit power and minimizing the maximum cell transmit power. Simulation results show the effectiveness of the proposed solutions.
Statistical Analysis of Received Signal Strength in Industrial IoT Distributed Massive MIMO Systems
Eduardo Noboro Tominaga and Onel L. A. López (University of Oulu, Finland); Richard Demo Souza (Federal University of Santa Catarina, Brazil); Hirley Alves (University of Oulu, Finland)
The Fifth Generation (5G) of wireless networks introduced native support for Machine-Type Communication (MTC), which is a key enabler for the Internet of Things (IoT) revolution. Current 5G standards are not yet capable of fully satisfying the requirements of critical MTC (cMTC) and massive MTC (mMTC) use cases. This is the main reason why industry and academia have already started working on technical solutions for beyond-5G and Sixth Generation (6G) networks. One technological solution that has been extensively studied is the combination of network densification, massive Multiple-Input Multiple-Output (mMIMO) systems and user-centric design, which is known as distributed mMIMO or Cell-Free (CF) mMIMO. Under this new paradigm, there are no longer cell boundaries: all the Access Points (APs) on the network cooperate to jointly serve all the devices. In this paper, we compare the performance of traditional mMIMO and different distributed mMIMO setups, and quantify the macro diversity and signal spatial diversity performance they provide. Aiming at the uplink in industrial indoor scenarios, we adopt a path loss model based on real measurement campaigns. Monte Carlo simulation results show that the grid deployment of APs provide higher average channel gains, but radio stripes deployments provide lower variability of the received signal strength.
Comparing Minimum Codeword Distances and Error Performance for Index Modulation and Maximum Distance Separable Coded Modulation
Ferhat Yarkin and Justin P Coon (University of Oxford, United Kingdom (Great Britain))
Index modulation (IM) that embeds information into combinations of activated codeword elements have desirable properties in terms of error performance, low-complexity implementation, and compatibility with existing communication techniques. However, a recent modulation concept based on a simple maximum distance separable (MDS) code, i.e., MDS modulation, achieves better performance than IM while preserving a low-complexity structure. In this paper, we conduct further numerical comparisons among the MDS and IM techniques to comprehend to what extent the MDS methods can outperform the IM methods in terms of distance properties and bit error rate (BER) performance. Our numerical distance comparisons show that the MDS techniques are more beneficial than the IM techniques when \(\eta > 2\), where \(\eta\) is spectral efficiency (SE) per codeword element. Moreover, our BER results support the distance results and show the effectiveness of the MDS techniques against the IM techniques for a wide range of SE.