Conference Paper

Multi-user Grouping Based Scheduling Algorithm in Massive MIMO Uplink Networks

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... In [14], a signal-to-interference-ratio-based user scheduling (SIRUS) method was addressed for matched filter (MF) precoding in downlink MIMO systems. For uplink massive MIMO, a multi-user grouping-based scheduling algorithm was investigated in [15], while a joint user scheduling and beam selection scheme was studied in [16] for beam-based massive MIMO systems. In [17], a greedy user selection algorithm for distributed massive MIMO was investigated. ...
... where µ R and σ 2 R are the mean and variance of R, respectively. According to (15), the maximum scheduling gain by the optimum algorithm is approximately 2σ 2 R log N K . Hereafter, we investigate the variance of the sum rate σ 2 R to understand the achievable scheduling gain with respect to the number of antennas. ...
... In SUS, a user set with near-orthogonal channel vectors is selected in the greedy manner. For comparison, the results of round robin (RR) scheduling and the achievable maximum sum rate in (15), i.e., optimal user scheduling, are presented. The RR scheduler selects the users randomly; hence, no scheduling gain occurs. ...
Article
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In this paper, the scaling laws of scheduling gain and the feasibility of user scheduling for uplink massive multiple input–multiple output (MIMO) systems are investigated by analyzing the second moment of mutual information. We consider two well-known linear receivers of matched filter (MF) and zero-forcing (ZF). The exact distribution of the signal-to-interference-plus-noise ratio (SINR) and its moment-generating function are first obtained, and the approximated variance of the mutual information for a user is derived as a closed form with a function of the number of antennas. The achievable scheduling gain under the optimal user scheduler is then derived using the Gaussianity of the sum rate. From the analyses and simulation results, it is found that the scheduling gain for the MF receiver increases with the number of base station (BS) antennas, while that for the ZF receiver decreases as the number of BS antennas increases, for most cases (except some impractical scenarios). Therefore, it is verified that user scheduling is still beneficial for the MF receiver while random user selection is sufficient for the ZF receiver in massive MIMO systems.
... Still, they do not provide optimal throughput performance for massive MIMO systems with a large number of antennas. Multi-user scheduling and joining user scheduling methods have been proposed recently to provide optimal scheduling in a massive MIMO downlink system [112,113]. Several other efficient scheduling methods are proposed in [114,115]. ...
... To improve overall system performance, a certain amount of fairness must be ensured among all the users. Several research has been conducted to achieve an efficient user scheduling algorithm [92,[105][106][107][108][109][110][111][112][113][114][115], but optimal performance has not been achieved. Further research should be conducted to find a more efficient and fair scheduling algorithm design that can provide a higher data rate and guarantee fairness among users. ...
... Least Square [74], MMSE [75,76], Improved MMSE [77,78], Blind Estimation [80,81], Compresses Sensing [82,83], MICED [84], Untraind Deep Neural Network [85], Compressed Sensing [86], Convolutional Blind Denoising [87], VAMP [88], Deep Learning based Sparse Estimation [89], CNN based Estimation [150], Machine Learning based Estimate [151,158], Deep Learning based Estimation [153,155] Precoding DPP [93], TH [94,95], VP [96], MRC [97], ZF [98,99], WF [100], MMSE [101,102] User Scheduling ZF [105], MMSE [106], DPC [92], RR [107], PF [108], Greedy [109], Multi-user Grouping [112], Gibbs Distribution Scheme [114], Pilot Efficient Scheduling [115], Machine Learning based Scheduling [159] Hardware Impairments Digital Pre-Distortion [118,119], PAPR [120], Signal Detection SD [122], SIC [123], ML [47], ZF [124], MMSE [125], NSA [132], Richardson [133], SOR [74], Jacobi [134], Gauss Siedel [135], Conjugate Gradient [131], Least Square Regression Selection [136], Huber ADMM [137], AMP [138] Compressed Sensing based Adaptive Scheme [86], CNN [140], Gauss Siedel Refinement [143], SSL and SL based Detection [162,163], APRGS [169] ...
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The global bandwidth shortage in the wireless communication sector has motivated the study and exploration of wireless access technology known as massive Multiple-Input Multiple-Output (MIMO). Massive MIMO is one of the key enabling technology for next-generation networks, which groups together antennas at both transmitter and the receiver to provide high spectral and energy efficiency using relatively simple processing. Obtaining a better understating of the massive MIMO system to overcome the fundamental issues of this technology is vital for the successful deployment of 5G—and beyond—networks to realize various applications of the intelligent sensing system. In this paper, we present a comprehensive overview of the key enabling technologies required for 5G and 6G networks, highlighting the massive MIMO systems. We discuss all the fundamental challenges related to pilot contamination, channel estimation, precoding, user scheduling, energy efficiency, and signal detection in a massive MIMO system and discuss some state-of-the-art mitigation techniques. We outline recent trends such as terahertz communication, ultra massive MIMO (UM-MIMO), visible light communication (VLC), machine learning, and deep learning for massive MIMO systems. Additionally, we discuss crucial open research issues that direct future research in massive MIMO systems for 5G and beyond networks.
... There have been extensive studies that have appeared in the literature to present the potential gains of beamforming. Most of the reported work considers single cell scenarios [8,11,12]. Moreover, in the literature presenting larger-scale assessment, only physical layer metrics have been used to evaluate system performance [3,8,11,13]. ...
... Most of the reported work considers single cell scenarios [8,11,12]. Moreover, in the literature presenting larger-scale assessment, only physical layer metrics have been used to evaluate system performance [3,8,11,13]. ...
Conference Paper
There have been many papers that show the physical layer advantages of beamforming. However, to the best of our knowledge, none have dealt with the large-scale network level performance evaluation. While it is clear that beamforming brings a positive effect on network performance, that improvement has not been quantified. This is important given that, despite its advantages, large-scale beamforming deployment also implies additional CAPEX and OPEX costs for service providers. This paper presents a large-scale network layer simulation study that aims at quantifying some network layer Key Performance Indicators improvement achieved by the massive introduction of beamforming in the network. We first show how we adapted a simulation-efficient packet scheduling procedure to our large-scale PIoT simulator engine. Then, we perform extensive simulations on a real network of the city of Montréal with 30000 UEs (User Equipment) and 3019 antennas. Among many results it was found that, on average, more than 30 ms of waiting delay reduction can be achieved with beamforming. Also, for applications with very short packet inter arrival time, the total traffic serviced more than doubled. Simulation results also show that while the increase in the number of beams improves performance, there is also a saturation effect that can be perceived at the network level. It was also found that when the traffic arrival is Poisson, the benefits of beamforming are less striking, which suggests that the type of application traffic may have an influence in the beneficial impact of beamforming at the network level, opening the path for further investigation. Finally, given that the simulator front end is publicly available at http://piotsimulation.com, the paper also shows the potentiality for the community to perform what-if cross layer analysis in a real-sized urban network.
... Examining the uplink of a massive MIMO system, [16] shows that the system EE can be improved by appropriately shutting off certain special users during the process of information transmission. [17] demonstrates that when the number of terminal users is greater than the number of the BS antennas, both higher throughput and higher EE can be obtained by adopting an appropriate user scheduling scheme before precoding. ...
... In the multicell massive MIMO system, the multiplexing of pilot sequences between cells inevitably results in PC, which affects the EE of the system. As described in [17], the highest EE can be obtained by using the largest pilot reuse factor (τ ðulÞ = 4). Therefore, we consider τ ðulÞ = 4 in the multicell massive MIMO system. ...
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As one of the key technologies in the fifth generation of mobile communications, massive multi-input multioutput (MIMO) can improve system throughput and transmission reliability. However, if all antennas are used to transmit data, the same number of radiofrequency chains is required, which not only increases the cost of system but also reduces the energy efficiency (EE). To solve these problems, in this paper, we propose an EE optimization based on the particle swarm optimization (PSO) algorithm. First, we consider the base station (BS) antennas and terminal users and analyze their impact on EE in the uplink and downlink of a single-cell multiuser massive MIMO system. Second, a dynamic power consumption model is used under zero-forcing processing, and it obtains the expression of EE that is used as the fitness function of the PSO algorithm under perfect and imperfect channel state information (CSI) in single-cell scenarios and imperfect CSI in multicell scenarios. Finally, the optimal EE value is obtained by updating the global optimal positions of the particles. The simulation results show that compared with the traditional iterative algorithm and artificial bee colony algorithm, the proposed algorithm not only possesses the lowest complexity but also obtains the highest optimal value of EE under the single-cell perfect CSI scenario. In the single-cell and multicell scenarios with imperfect CSI, the proposed algorithm is capable of obtaining the same or slightly lower optimal EE value than that of the traditional iterative algorithm, but the running time is at most only 1/12 of that imposed by the iterative algorithm.
... There have been a number of works studying the scheduling of uplink transmission resources for wireless communications. The author in [2] proposed a grouping based MU scheduling algorithm in massive MIMO uplink system, which can achieve a good performance with low complexity, but contiguous resource allocation was not considered. Recursive maximal expansion (RME) for contiguous resource scheduling was proposed in [3], which is well known for providing a balance of complexity and performance. ...
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... We consider the case where K = min (K A , K B ) pairs of active devices establish communication with each other via the relay. When K A ̸ = K B , we can use some user scheduling schemes to select the same number of active devices at each side to communicate at each time [8], [31]. Therefore, we assume K A = K B = K for convenience, and we haveK = 2K in our following paper. ...
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In wireless communication industry, the shortage of worldwide bandwidth has inspired the research and development of technology for wireless access named as massive MIMO. It is a basic and fundamental technology of 5G network. Massive MIMO is a multi-antenna technique that deploys arrays of antennas at transmitter and receiver to support several users at the same time. As massive MIMO improves the spectrum efficiency and channel capacity of communication system, it also efficiently improves the link reliability and data transmission rate. A comprehensive review on massive MIMO system is presented in this paper outlining various challenges with their mitigation techniques and future research direction. In the end, we have comprehensively investigated the potential of machine learning and deep learning techniques in mitigating the issues of massive MIMO systems.Keywords5GPrecodingChannel estimationPilot contaminationMassive MIMOMachine learningSpectral efficiencySignal detectionDeep learning
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Scheduling for virtual MIMO in single carrier FDMA (SC-FDMA) system[C]
  • J W Kim
  • I Hwang
  • C G Kang