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multi-user rate and power analysis in a cognitive radio network with massive multi-input multi-output

Frontiers of Information Technology & Electronic Engineering 2018年第5期19卷 674-684页

作者： Shang LIU Ishtiaq AHMAD Ping ZHANG Zhi ZHANG State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Department of Electrical Engineering University of Lahore

This paper discusses transmission performance and power allocation strategies in an underlay cognitive radio （CR） network that contains relay and massive multi-input multi-output （MIMO）. The downlink transmission performance of a relay-aided massive MIMO network without CR is derived. By using the power distribution criteria, the kth user＇s asymptotic signal to interference and noise ratio （SINR） is independent of fast fading. When the ratio between the base station （BS） antennas and the relay antennas becomes large enough, the transmission performance of the whole system is independent of BS-to-relay channel parameters and relates only to the relay-to-users stage. Then cognitive transmission performances of primary users （PUs） and secondary users （SUs） in an underlay CR network with massive MIMO are derived under perfect and imperfect channel state information （CSI）, including the end-to-end SINR and achievable sum rate. When the numbers of primary base station （PBS） antennas, secondary base station （SBS） antennas, and relay antennas become infinite, the asymptotic SINR of the kth PU and SU is independent of fast fading. The interference between the primary network and secondary network can be canceled *** performance does not include the interference temperature. The secondary network can use its peak power to transmit signals without causing any interference to the primary network. Interestingly, when the antenna ratio becomes large enough, the asymptotic sum rate equals half of the rate of a single-hop single-antenna K-user system without fast fading. Next, the PUs＇ utility function is defined. The optimal relay power is derived to maximize the utility function. The numerical results verify our analysis. The relationships between the transmission rate and the antenna nunber, relay power, and antenna ratio are simulated. We show that the massive MIMO with linear pre-coding can mitigate asymptotically the interference in a multi-user underl

关键词： massive multi-input multi-output Cognitive radio Relay network Tiansmission rate Power analysis

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Energy and Spectral Efficiencies Optimization for massive MIMO Systems Using Modified multi-objective Artificial Bee Colony Algorithm

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WIRELESS PERSONAL COMMUNICATIONS 2025年 1-31页

作者： Taspinar, Necmi Gul, Burak Kursat Erciyes Univ Dept Elect & Elect Engn Kayseri Turkiye Amasya Univ Dept Elect & Elect Engn Amasya Turkiye

Efficient energy usage is a critical concern in cellular communication systems, as in many other fields. However, it is known that energy-saving measures applied in cellular communication systems may have a negative impact on the area throughput, which is of vital importance and considered unacceptable to decrease. Energy efficiency and spectral efficiency are two essential performance metrics for modern wireless communication systems, particularly with the rollout of 5G networks and the increasing demand for high-speed data services. However, these metrics are often conflicting, and improving one may negatively impact the other, leading to a challenging optimization problem. Therefore, in this paper, the solution to this problem is focused on the optimization of energy and spectral efficiencies in massive multi-input multi-output (MIMO) systems, which are known for being successful in these two efficiencies. For these optimization processes, a new intelligent optimization algorithm is proposed to determine the optimal configurations among various parameter variations, specifically by varying the number of users (up to 70), the number of active antennas (up to 100), and the transmission power (up to 200 mW) in massive MIMO systems. The proposed modified multi-objective artificial bee colony algorithm demonstrated significant success in optimizing energy efficiency and spectral efficiency trade-off. It has been demonstrated superior convergence efficiency to optimal results after low number of iterations for the trade-off problem. The proposed algorithm has achieved a higher success rate than the multi-objective genetic algorithm, multi-objective bat algorithm, multi-objective particle swarm optimization, multi-objective differential evolution algorithm, multi-objective sperm fertilization procedure optimization algorithm and multi-objective artificial bee colony algorithms by performing Pareto optimal front estimation with an inverted generational distance value of

关键词： Energy efficiency massive multi-input multi-output multi-objective artificial bee colony algorithm Spectral efficiency

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Optimization of Spectral and Energy Efficiencies Trade-Off Using multi-objective Forest Optimization Algorithm in massive MIMO Systems

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WIRELESS PERSONAL COMMUNICATIONS 2024年第1期137卷 399-414页

作者： Gul, Burak Kursat Taspinar, Necmi Erciyes Univ Dept Elect & Elect Engn Kayseri Turkiye

Increasing the area throughput is one of the leading solutions in terms of preventing data traffic density, which is one of the current problems in cellular communication systems. It can be shown that the most effective action to increase the area throughput is to increase the spectral efficiency. However, the measures that can be made to increase the spectral efficiency negatively affect the energy efficiency, which is another critical parameter for cellular communication. In this paper, the conditions of the cells which use massive multi-input multi-output systems have been optimized with multi-objective forest optimization algorithm (MOFOA). The results obtained with MOFOA are compared with the multi-objective genetic algorithm, multi-objective particle swarm optimization, multi-objective differential evolution algorithm and multi-objective firefly algorithm. When the simulation results and performance metrics are examined, it can be seen that MOFOA is a more successful algorithm than other algorithms.

关键词： Energy efficiency massive multi-input multi-output multi-objective forest optimization algorithm Spectral efficiency

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Linearization of Fully-Connected Hybrid Beamforming Transmitters Using Analytical multi-input Models for Millimeter-Wave Communications

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IEEE TRANSACTIONS ON COMMUNICATIONS 2025年第4期73卷 2680-2694页

作者： Liu, Xin Jia, Huanhuan Lu, Yang Zhao, Ziyue Yi, Chupeng Feng, Ting Ma, Xiaohua Chen, Wenhua Feng, Zhenghe Ghannouchi, Fadhel M. Xidian Univ Natl Engn Res Ctr Wide Band Gap Semicond Sch Microelect Xian 710071 Shaanxi Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Univ Calgary Intelligent RF Radio Lab iRadio Lab Calgary AB T2N 1N4 Canada

In recent years, fully-connected hybrid beamforming (FC-HBF) architecture has aroused widespread interest for millimeter wave (mmWave) massive multi-input multi-output (MIMO) communication systems. However, the FC-HBF structure suffers from significant linearity deterioration, limiting its applications in actual mmWave transmitters. To resolve this issue, an effective digital predistortion (DPD) method utilizing analytical multi-input behavioral models is proposed in this paper for linearizing the FC-HBF system. Based on the nonlinearity analysis and behavioral modeling of the array response, three analytical multi-input models are derived by embedding the priori beamforming information in the predistorter. The complexity of the proposed analytical models is significantly reduced compared to the state-of-the-art. Numerical simulations and experimental measurement are carried out on a 4x 64 mmWave FC-HBF array and 2x16 quasi-test platform respectively to validate the performance of the proposed DPDs against the state-of-the-art DPDs, which show significant linearization abilities to compensate for the nonlinear distortions of beam signals. The proof-of-the-concept validations in this paper indicate that the proposed scheme is fully capable of linearizing an mmWave FC-HBF array.

关键词： Streams Arrays Array signal processing Analytical models Millimeter wave communication Nonlinear distortion Antenna arrays Digital predistortion fully-connected hybrid beamforming array massive multi-input multi-output nonlinear distortion power amplifiers

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massive Access in Media Modulation Based massive Machine-Type Communications

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2022年第1期21卷 339-356页

作者： Qiao, Li Zhang, Jun Gao, Zhen Ng, Derrick Wing Kwan Di Renzo, Marco Alouini, Mohamed-Slim Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Beijing Inst Technol Adv Res Inst Multidisciplinary Sci ARIMS Beijing 100081 Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2025 Australia Univ Paris Saclay CNRS Cent Supelec Lab Signaux & Syst F-91192 Gif Sur Yvette France King Abdullah Univ Sci & Technol KAUST Comp Elect & Math Sci & Engn Div Thuwal 239556900 Saudi Arabia

The massive machine-type communications (mMTC) paradigm based on media modulation in conjunction with massive multi-input multi-output base stations (BSs) is emerging as a viable solution to support the massive connectivity for the future Internet-of-Things, in which the inherent massive access at the BSs poses significant challenges for device activity and data detection (DADD). This paper considers the DADD problem for both uncoded and coded media modulation based mMTC with a slotted access frame structure, where the device activity remains unchanged within one frame. Specifically, due to the slotted access frame structure and the adopted media modulated symbols, the access signals exhibit a doubly structured sparsity in both the time domain and the modulation domain. Inspired by this, a doubly structured approximate message passing (DS-AMP) algorithm is proposed for reliable DADD in the uncoded case. Also, we derive the state evolution of the DS-AMP algorithm to theoretically characterize its performance. As for the coded case, we develop a bit-interleaved coded media modulation scheme and propose an iterative DS-AMP (IDS-AMP) algorithm based on successive inference cancellation (SIC), where the signal components associated with the detected active devices are successively subtracted to improve the data decoding performance. In addition, the channel estimation problem for media modulation based mMTC is discussed and an efficient data-aided channel state information (CSI) update strategy is developed to reduce the training overhead in block fading channels. Finally, simulation results and computational complexity analysis verify the superiority of the proposed DS-AMP algorithm over state-of-the-art algorithms in the uncoded case. Also, our results confirm that the proposed SIC-based IDS-AMP algorithm can enhance the data decoding performance in the coded case and verify the validity of the proposed data-aided CSI update strategy.

关键词： Modulation Media Signal processing algorithms Matching pursuit algorithms Detectors Uplink Wireless communication massive access media modulation massive multi-input multi-output massive machine-type communications

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Pencil and Shaped Beam Patterns Synthesis Using a Hybrid GA/l₁ Optimization and Its Application to Improve Spectral Efficiency of massive MIMO Systems

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IEEE ACCESS 2021年 9卷 38202-38220页

作者： Farghaly, Samar Ibrahim Seleem, Hussein Eltaibee Abd-Elnaby, Mustafa Mahmoud Hussein, Amr Hussein Tanta Univ Fac Engn Dept Elect & Elect Commun Engn Tanta 31527 Egypt

The synthesis of pencil beam and arbitrarily shaped beam patterns of linear antenna arrays (LAA) using reduced number of antenna elements attracts the attention of researchers in recent years. In this paper, a hybrid beamforming technique based on the combination of the genetic algorithm (GA) optimization technique and the l(1) minimization method denoted as (GA/l(1)) is introduced for LAAs synthesis. The proposed GA/l(1) beamforming technique optimizes both the elements excitations and interelement spacing to synthesize the desired LAA pattern with a minimum number of antenna elements. The GA/l(1) technique provides an excellent approximation to the desired radiation pattern with high accuracy and low complexity (less number of iterations and computational time) compared to the other synthesis approaches introduced in the literature. In addition, as an application of this work, the proposed GA/l(1) technique is used to build up a proposed hybrid precoding and beamforming (HP-BF) structure for massive multi-input multi-output (M-MIMO) systems. In this structure, the transmit antenna array is synthesized for maximum gain realization using the existing number of antenna elements. In the HP-BF structure, the proposed GA/l(1) technique is used to make full use of the existing transmit array elements to synthesize the radiation pattern of much larger size and higher gain arrays without the need for additional elements. Thereby, significant savings in the number of antenna elements and their corresponding radio frequency (RF) chains are achieved, which reduces the system complexity. In addition, the array gain maximization will maximize the received signal to noise ratio (SNR) giving rise to higher system performance in terms of spectral efficiency (SE) and power utilization.

关键词： Array synthesis analog phase shifters hybrid precoding and beamforming structure massive multi-input multi-output millimeter waves spectral efficiency uniform linear array

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Computationally Efficient Direction-of-Arrival Estimation Algorithms for a Cubic Coprime Array

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SENSORS 2022年第1期22卷 136-136页

作者： Gong, Pan Chen, Xixin Nanjing Vocat Univ Ind Technol Coll Elect Engn Nanjing 211106 Peoples R China

In this paper, we investigate the problem of direction-of-arrival (DOA) estimation for massive multi-input multi-output (MIMO) radar, and propose a total array-based multiple signals classification (TA-MUSIC) algorithm for two-dimensional direction-of-arrival (DOA) estimation with a coprime cubic array (CCA). Unlike the conventional multiple signal classification (MUSIC) algorithm, the TA-MUSIC algorithm employs not only the auto-covariance matrix but also the mutual covariance matrix by stacking the received signals of two sub cubic arrays so that full degrees of freedom (DOFs) can be utilized. We verified that the phase ambiguity problem can be eliminated by employing the coprime property. Moreover, to achieve lower complexity, we explored the estimation of signal parameters via the rotational invariance technique (ESPRIT)-based multiple signal classification (E-MUSIC) algorithm, which uses a successive scheme to be computationally efficient. The Cramer-Rao bound (CRB) was taken as a theoretical benchmark for the lower boundary of the unbiased estimate. Finally, numerical simulations were conducted in order to demonstrate the effectiveness and superiority of the proposed algorithms.

关键词： direction-of-arrival estimation massive multi-input multi-output Cramer-Rao bound ambiguity elimination total array-based multiple signal classification successive algorithm

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End-To-End Performance For User-Centric Cell-Free Mmimo Networks With multiple CPUs 11

End-To-End Performance For User-Centric Cell-Free Mmimo Netw...

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11th International Symposium on Telecommunication, IST 2024

作者： Razavi, Sara Hadi, Mohammad Pakravan, Mohammad Reza Sharif University of Technology Department of Electrical Engineering Tehran Iran

ISBN: (纸本)9798350356250

Cell-free massive MIMO network, as a promising solution for future wireless communication systems, is conven-tionally equipped with a single central processing unit. In this paper, we multiply the number of central processing units in a user-centric cell-free massive MIMO network and demonstrate its impact on communication efficiency. Specifically, we show that under a fair comparison scenario in terms of complexity and cost, having two central processing units increases the fronthaul data rate and the end-to-end peak data rate by up to 47% and 6%, respectively, compared to the conventional situation, where a single processing unit is assumed. © 2024 IEEE.

关键词： Access link cell-free end-to-end data rate fronthaul link massive multi-input multi-output user-centric

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multi-Stream Spatial Digital Predistortion for Fully-Connected Hybrid Beamforming massive MIMO Transmitters

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2021年第7期68卷 2998-3011页

作者： Liu, Xin Chen, Wenhua Chu, Jiaming Ghannouchi, Fadhel M. Feng, Zhenghe Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Univ Calgary Intelligent RF Radio Lab iRadio Lab Calgary AB T2N 1N4 Canada

In this paper, a novel multi-stream spatial digital predistortion (DPD) technique is proposed to model and linearize the fully-connected (FC) hybrid beamforming (HBF) transmitters. The proposed scheme solves the DPD implementation issue in the FC HBF array by estimating and linearizing the beam signals instead of the individual PAs. In FC HBF systems, significant intermodulation (IMD) between different transmit signals will be generated due to the analog beamforming and combining network upstream of the power amplifiers (PAs). The IMD beams will end up being radiated in different directions and some of them might fall in the linear beam directions. Therefore, multi-input DPD blocks using a practical multi-variable model are constructed for each RF chain to eliminate the complicated inner- and cross-channel IMDs of the beam signals. Simulations on a 4-stream 64-element FC HBF array and experimental tests on a 2-stream 4-element system are carried out to benchmark the proposed DPD technique against the conventional techniques. Better than 13 dB adjacent channel power ratio (ACPR) improvement and 12 dB normalized mean square error (NMSE) improvement have been achieved by the proposed DPD technique.

关键词： Radio frequency Array signal processing Nonlinear distortion Crosstalk Predistortion massive MIMO Linear antenna arrays Digital predistortion fully-connected hybrid beamforming array massive multi-input multi-output multi-variable behavioral model power amplifiers

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Compressive Sensing Based massive Access for IoT Relying on Media Modulation Aided Machine Type Communications

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2020年第9期69卷 10391-10396页

作者： Qiao, Li Zhang, Jun Gao, Zhen Chen, Sheng Hanzo, Lajos Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China BIT Adv Res Inst Multidisciplinary Sci ARIMS Beijing 100081 Peoples R China NUAA Key Lab Dynam Cognit Syst Electromagnet Spectrum Minist Ind & Informat Technol Nanjing 210016 Peoples R China Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ Hants England King Abdulaziz Univ Jeddah 21589 Saudi Arabia

A fundamental challenge of the large-scale Internet-of-Things lies in how to support massive machine-type communications (mMTC). This letter proposes a media modulation based mMTC solution for increasing the throughput, where a massive multi-input multi-output based base station (BS) is used for enhancing the detection performance. For such a mMTC scenario, the reliable active device detection and data decoding pose a serious challenge. By leveraging the sparsity of the uplink access signals of mMTC received at the BS, a compressive sensing based massive access solution is proposed for tackling this challenge. Specifically, we propose a structured orthogonal matching pursuit algorithm for detecting the active devices, whereby the block-sparsity of the uplink access signals exhibited across the successive time slots and the structured sparsity of media-modulated symbols are exploited for enhancing the detection performance. Moreover, a successive interference cancellation based structured subspace pursuit algorithm is conceived for data demodulation of the active devices, whereby the structured sparsity of media modulation based symbols found in each time slot is exploited for improving the detection performance. Finally, our simulation results verify the superiority of the proposed scheme over state-of-the-art solutions.

关键词： Media Modulation Throughput Matching pursuit algorithms Indexes Radio frequency Mirrors Internet-of-Things massive machine type communications massive access massive multi-input multi-output media modulation compressive sensing

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