Pilot Reuse Factor Research Articles

Performance analysis of massive MIMO using various transmit precoding schemes for a wireless network

SummaryAn evaluation of various transmit precoding schemes is proposed in this thesis on basis of spectral efficiency (SE) of massive MIMO (m‐MIMO) system in 2 GHz spectrum. The present generation of wireless networks must be rethought due to the rapidly rising user base, huge volume data transfer, wide coverage requirement, and exponential rise in data rate. 5G mobile network includes a multi‐fold increase in network capacity, energy conservation, decreased latency, higher coverage probability, and a seamless connection. m‐MIMO has provided good solution for enhancing the data rate with the coverage, overcoming the effects of multipath fading and has also increased diversity of the system. Massiveness of MIMO system along with 5G network is the key technology to address the issue of SE, throughput, improvement of QoS (quality of service), coverage probability, power delay profile, diversity simulation, energy efficiency, and improvement in bit error rate. Parameters such as SE, throughput, and coverage probability are important for any communication system, and their improvement can increase overall capacity of cellular network system and could meet the requirement of upcoming demand for mobile network. The research paper is focused on performance analysis of m‐MIMO with reference to the SE of various transmit precoding schemes. Simulation results show that SE provided by M‐MMSE (multi‐cell minimum mean‐squared error) technique of precoding ranks highest for the same number of antennas and pilot reuse factor as compared to other schemes.

Innovative Approach of Spectral Efficiency Optimization over Various Pilot Reuse Factors

The Massive MIMO with TDD is breakthrough technology for spectral efficiency gains. The CSI is essential for spectral efficiency gains and CSI can be obtained by channel estimation methods. The channel estimation methods employ known pilot s sequences to estimate the channel before actual data transmission. However, the channel coherence is time and frequency limited, which reflects the trade-off between the resources available for pilots and those available for data in coherent block for transmission. The pilot sequences reuse in other cells can reduce pilot overhead, called pilot reuse. However potential interference is introduced, by pilot reuse, in the channel estimation phase, called pilot contamination. The aim is to determine optimum pilot reuse factor for better SE using low computational complexity estimation method. The sum averaged SE has been obtained for pilot reuse factor 1, 2, 4, 8 and 16 using MMSE, EW-MMSE and LS channel estimation methods. The obtained results reveals that the difference between values of SEs for f=1 to f=2 to f=4 is very low and performance difference between estimators decreases as pilot reuse factor increases, which allows to use LS estimator to get performance almost equal to MMSE with less computational complexity. Also, the pilot reuse factor f=4 found optimum among all for given constraint.

The Effect of Pilot Reuse Factor on Massive MIMO Spectral Efficiency

Due to their much increased spatial resolution and array gain, large-scale antenna arrays have the potential to significantly enhance the spectral efficiency of wireless systems. Recent research in the area of massive Multiple-Input Multiple-Output (MIMO) antennas shows that user channels are enhanced as the number of antennas at the Base Stations (BSs) rises, allowing achieving high signal improvement with minimum inter-user interference. The combined effects of the pilot reuse factor and the BS number of antennas on average overall spectral efficiency in huge MIMO antennas will be investigated. To examine the SE of these receivers, several channel estimators will be utilized, including LMMSE, ZF, and MR methods. Theoretically, BS could offer a substantially higher average total SE if it had more antennas.

Performance analysis of downlink massive MIMO system with precoding techniques and pilot reuse factor

Performance analysis of downlink massive MIMO system with precoding techniques and pilot reuse factor

Intelligent Network Solution for Improved Efficiency in 6G-Enabled Expanded IoT Network

The fast-moving world relies on intelligent connected networks to support the numerous applications of the expanded Internet-of-Things (IoT). The evolving communication requirements of this connected world require a new sixth generation (6G) radio to enable intelligent interaction with the massive number of connected objects. The energy management of billions of connected devices supporting massive Internet-of-Things (IoT) applications is the main challenge. These IoT devices and connected nodes are energy limited, and hence, energy-aware solutions are needed to enable seamless information flow between these communicating nodes. This paper presents an intelligent network solution for improved energy efficiency in a 6G-enabled expanded IoT network. A cell-free massive multiple input multiple output (mMIMO) technology is utilized for maximum energy efficiency with optimum network resource allocation. A practical power consumption model is proposed for the designed network topology which contains all the power components related to data transmission and circuit power. The proposed scheme aims to achieve maximum energy efficiency by the optimal allocation of pilot reuse factor and access point (AP) density for a given number of antennas at each AP and number of users. It is observed that the maximum energy efficiency of 5.2362 Mbit/Joule is achieved at the AP density of 29 and pilot reuse factor of 4 with PMMSE receive combining. In the end, the role of energy efficiency and area throughput tradeoff on the system performance is also evaluated, which suggests that both the energy efficiency and area throughput can be jointly increased until maximum energy efficiency is reached at a point.

Downlink Resource Allocation in Multiuser Cell-Free MIMO Networks With User-Centric Clustering

In this paper, we optimize user scheduling, power allocation and beamforming in distributed multiple-input multiple-output (MIMO) networks implementing user-centric clustering. We study both the coherent and non-coherent transmission modes, formulating a weighted sum rate maximization problem for each; finding the optimal solution to these problems is known to be NP-hard. We use tools from fractional programming, block coordinate descent, and compressive sensing to construct an algorithm that optimizes the beamforming weights and user scheduling and converges in a smooth non-decreasing pattern. Channel state information (CSI) being crucial for optimization, we highlight the importance of employing a low-overhead pilot assignment policy for scheduling problems. In this regard, we use a variant of hierarchical agglomerative clustering, which provides a suboptimal, but feasible, pilot assignment scheme; for our cell-free case, we formulate an <i>area-based</i> pilot reuse factor. Our results show that our scheme provides large gains in the long-term network sum spectral efficiency compared to benchmark schemes such as zero-forcing and conjugate beamforming (with round-robin scheduling) respectively. Furthermore, the results show the superiority of coherent transmission compared to the non-coherent mode under ideal and imperfect CSI for the area-based pilot-reuse factors we consider.

Optimization of Spectral Efficiency in Massive MIMO Network for Different Deployment Scenarios

Background: Spectrum efficiency is labelled to be the most discussed area of wireless communication. Today, wireless communication is one of the fastest developing fields due to which there is a tremendous increase in subscribers that leads to the demand for more spectrum. Though the growth in the spectrum is not possible but optimizing it is a good alternative. Methods: This paper provides a detailed study of different methods used for the optimization of spectrum efficiency in massive multiple inputs and outputs. The main goal of this paper is to findout how the spectrum could be used efficiently to meet user demands. It includes the technique of fixing specific parameters like coherent block length, number of reception antennas, pilots reuse factor, signal to noise ratio, and number of users to make more efficient use of the spectrum. This paper also explains the achievement of spectral efficiency under different deployment scenarios i.e., urban, suburban, and rural in ITU-R M.2135 standard. Results: As seen from the results, steep SEs are accomplished by making a schedule of many UE for mutual broadcast, while the SE per UE maybe 1-4 bit/s/Hz. These high-class demonstrations show that massive MIMO e.g an SNR of 0-5 dB with a limited pilot reuse factor of β= 3, and the fast techniques are applied for lessening the distortion noise and instrument damage. According to the simulations, it is found that massive MIMO with M = 100 can produce a 10×gain in SE in the IMT-Advanced requirements of 3 bit/s/Hz/cell. For the bigger sizes of antennas, M = 500 massive MIMO can even give 40×gain over IMT-Advanced. Conclusion: With the advancement of technology, the number of users is increasing tremendously, leading to a shortage of spectrum. Hence, the need of the hour is to make efficient use of the available spectrum while maintaining the Quality of service. Therefore, researchers have proposed specific methods that fulfill this need. The paper realizes the SE approach on different deployment scenarios like urban, suburban and rural in the ITU-R M.2135 standard and proves that under all the three scenarios, P-ZF remained unchanged. As seen from the results, steep SEs are accomplished by making a schedule of many UE for mutual broadcast, while the SE per UE maybe 1-4 bit/s/Hz. These high-class demonstrations show that massive MIMO likes an SNR of 0–5 dB with a limited pilot reuse factor of β= 3, and the fast techniques are applied for lessening the distortion noise from instrument damage. According to the simulations, it is seen that massive MIMO with M = 100 can produce a 10×gain in SE in the IMT-Advanced requirements of 3 bit/s/Hz/cell. For the bigger sizes of antennas, M = 500 massive MIMO can even give 40×gain over IMT-Advanced. The outcomes in this paper suggest the uncorrelated fading, although for decreasing inter-user interference, correlated fading is required, to raise large SE with less β. This study makes SE independent of topography. Furthermore, optimization of energy under different deployment scenarios, along with these simulations, can optimize the spectrum to a greater extent.

Towards Optimal Energy Efficiency in Cell-Free Massive MIMO Systems

Motivated by the ever-growing demand for green wireless communications and the advantages of cell-free (CF) massive multiple-input multiple-output (mMIMO) systems, we focus on the design of their downlink (DL) for optimal energy efficiency (EE). To address this fundamental topic, we assume that each access point (AP) is deployed with multiple antennas and serves multiple users on the same time-frequency resource while the APs are Poisson point process (PPP) distributed, which approaches realistically their opportunistic spatial randomness. Relied on tools from stochastic geometry, we derive a lower bound on the DL average achievable spectral efficiency (SE). Next, we consider a realistic power consumption model for CF mMIMO systems. These steps enable the formulation of a tractable optimization problem concerning the DL EE, which results in the analytical determination of the optimal pilot reuse factor, the AP density, and the number of AP antennas and users that maximize the EE. Hence, we provide useful design guidelines for CF mMIMO systems relating to fundamental system variables towards optimal EE. Among the results, we observe that an optimal pilot reuse factor and AP density exist, while larger values result in an increase of the interference, and subsequently, lower EE. Overall, it is shown that the CF mMIMO technology is a promising candidate for next-generation networks achieving simultaneously high SE and EE.

Investigation of Ideal Number User Terminals with Spectrum Efficiency in Next Generation Wireless Communication Systems

Suppose that a multi-user multiple-input multiple-output (MIMO) system is developed from scratch to equally envelop a defined region with optimal spectrum efficiency (SE) in next generation wireless communication systems such as sixth-generation (6G) and beyond networks. What are the ideal number of user terminals U, number of base stations antennas, and used pilot reuse factor? The purpose of this paper is to address this specific issue. Three interference levels are specified for this. Based on these interference levels, signal-to-interference-and-noise ratios (SINRs) are extracted. Closed-form spectrum efficiency equations are thus obtained. As a function of the base station (BS) antenna number, simulations are carried out considering multiple pilot reuse factors and diverse processing schemes such as Maximum Ratio Combining (MRC) and Zero-Forcing (ZF). From the results, it is understood that U varies according to the processing schemes. Therefore, evaluating the results considering the fixed number of users K will not give an accurate result in determining the design parameters for the next generation communication systems. In general, these results are useful statements that spectrum efficiency is maximized when the ideal number of users U is used in multi-cell systems.

Spectral efficiency enhancement in Massive MIMO system under pilot contamination

SummaryMassive multiple‐input, multiple‐output (MIMO) system is an effective technique to develop the spectral efficiency of the wireless communication systems. Since the number of users is much higher than the available number of orthogonal pilot sequences, the users in the neighboring cells can reuse the same pilots of the home cell. Thus, pilot contamination occurs, which restricts the system throughput. In this paper, Laplacian centralized scattering‐spatially correlated Rayleigh fading channel model with multi‐cell minimum mean square error (M‐MMSE) combining and precoding to mitigate pilot contamination and enhance cell throughput. This model gives a more accurate description, especially for cell‐edge users. It results in an accurate spatial channel correlation matrix calculation that enhances the channel estimation quality under pilot contamination. Simulation results indicate that the proposed Laplacian scattering‐spatially correlated Rayleigh fading model achieves better pilot contamination mitigation and larger spectral efficiency, as compared with both the one‐ring scattering‐spatially correlated Rayleigh fading and the uncorrelated Rayleigh fading channel models for different combining and precoding schemes at different pilot reuse factors. The cost of interference rejection and spectral efficiency enhancement is an increase in the system computational complexity.

Pilot Reuse Mode Based on Continuous Pilot Reuse Factors

Massive multiple-input multiple-output techniques have attracted wide attention as one of the key technologies of 5G. Pilot reuse based on the same pilot sequence is necessary among different users due to limited pilot resources. In this study, a pilot reuse mode based on continuous pilot reuse factors is proposed to pursue a flexible pilot reuse mode with high spectral efficiency (SE). In this mode, users in every cell are grouped, and different groups use different pilot reuse factors. Therefore, any overall pilot reuse factor is achieved to increase the flexibility of pilot reuse considerably. A theoretical analysis proves that the proposed pilot reuse based on continuous pilot reuse factors is superior to the traditional pilot reuse based on single pilot reuse factor to some extent in terms of SE. A new method to search the optimal pilot reuse based on continuous pilot reuse factors is also introduced. Simulation results demonstrate that, in most cases, the optimal pilot reuse mode based on continuous pilot reuse factors is better than the traditional mode. Such superiority still exists under a limited number of antennas.

A New Method in Pilot Reuse Factor Selection in Spectrum Efficient Massive MIMO Systems

Spectrum efficiency studies of Massive MIMO systems have continued and still have not been fully explored in the literature. Therefore, the spectrum efficiency definition of a particular user in a cell in a Massive MIMO network is expressed in this paper. In addition, the uplink spectrum efficiency expressions for the Rayleigh fading are included. The Monte Carlo simulations are performed to verify these expressions. Due to the expectation of reaching to a thousand antennas together with the millimetre wave structure in the next years, in this study, it is contributed to the literature by explaining how to select the pilot reuse factor. The realization of this contribution is based on Zero Forcing (ZF) and Maximum Ratio Combining (MRC) schemes. The results of this study are useful for optimal design conditions, such as the number of antennas on the base station and pilot reuse factor selection for the next generation networks in order to obtain spectrum efficiency in Massive MIMO systems.

Massive MIMO: Achievable Energy Efficiency for 5G systems with Multi-user Environment

Massive Multi-Input and Multi-Output (MIMO) antenna system potentially provides a promising solution to improve energy efficiency (EE) for 5G wireless systems. The aim of this paper is to enhance EE and its limiting factors are explored. The maximum EE of 48 Mbit/Joule was achieved with 15 user terminal (UT)s. This problem is related to the uplink spectral efficiency with upper bound for future wireless networks. The maximal EE is obtained by optimizing a number of base station (BS) antennas, pilot reuse factor, and BSs density. We presented a power consumption model by deriving Shannon capacity calculations with closed-form expressions. The simulation result highlights the EE maximization with optimizing variables of circuit power consumption, hardware impairments, and path-loss exponent. Small cells achieve high EE and saturate to a constant value with BSs density. The MRC scheme achieves maximum EE of 36 Mbit/Joule with 12 UTs. The simulation results show that peak EE is obtained by deploying massive BS antennas, where the interference and pilot contamination are mitigated by coherent processing. The simulation results were implemented by using MATLAB 2018b.

Pilot decontamination techniques based on beam‐domain channel characteristics in millimetre‐wave sparse channels

The upcoming 5G system is expected to utilise large-scale antenna arrays to increase the users' data rate and system capacity. With a large number of antennas, channel estimation becomes a very critical design issue due to the adverse effects of pilot contamination. Some problems in the existing channel estimation methods studied in the literature include: (i) the complexity of signal processing on the spatial domain is very high. (ii) Channels are quite frequency selective in the spatial domain. (iii) It is not easy to obtain the second-order statistics of channels. In this study, the channel estimation issue is investigated using the signal processing in the beam domain. It is due to the salient channel sparsity at millimetre-wave frequencies that only a few beam channels need to be estimated. The authors exploit these beam channels to propose some pilot decontamination techniques: basic pilot allocation, beam-set non-overlapping allocation, intra-cell interference mitigation allocation and maximum beam power based allocation (MBPBA). Both large pilot reuse factor and beam-domain power control are also presented to supplement the proposed techniques. Simulation results show that the best method is MBPBA in terms of overhead and normalised square error.

Network Deployment for Maximal Energy Efficiency in Uplink With Multislope Path Loss

This work aims to design the uplink (UL) of a cellular network for maximal energy efficiency (EE). Each base station (BS) is randomly deployed within a given area and is equipped with $M$ antennas to serve $K$ user equipments (UEs). A multislope (distance-dependent) path loss model is considered and linear processing is used, under the assumption that channel state information is acquired by using pilot sequences (reused across the network). Within this setting, a lower bound on the UL spectral efficiency and a realistic circuit power consumption model are used to evaluate the network EE. Numerical results are first used to compute the optimal BS density and pilot reuse factor for a Massive MIMO network with three different detection schemes, namely, maximum ratio combining, zero-forcing (ZF) and multicell minimum mean-squared error. The numerical analysis shows that the EE is a unimodal function of BS density and achieves its maximum for a relatively small density of BS, irrespective of the employed detection scheme. This is in contrast to the single-slope (distance-independent) path loss model, for which the EE is a monotonic non-decreasing function of BS density. Then, we concentrate on ZF and use stochastic geometry to compute a new lower bound on the spectral efficiency, which is then used to optimize, for a given BS density, the pilot reuse factor, number of BS antennas and UEs. Closed- form expressions are computed from which valuable insights into the interplay between optimization variables, hardware characteristics, and propagation environment are obtained.

Spectral efficiency analysis and pilot reuse factor optimisation for multi‐cell massive SC‐SM MIMO

As a combination of spatial modulation (SM) system and massive multiple-input multiple-output (MIMO) system, massive SM aided MIMO (SM-MIMO) system is recently proposed. In broadband scenarios, single-carrier (SC) modulation is introduced to massive SM-MIMO system, thus massive SC-SM MIMO system is proposed for uplink multi-user transmission over frequency-selective fading channels. In this study, the uplink spectral efficiency (SE) of multi-cell massive SC-SM MIMO system is analysed, meanwhile the pilot contamination effect is taken into consideration. A tight SE lower bound of multi-cell massive SC-SM MIMO system is proposed with maximum ratio (MR) combining, which also takes into account the imperfect channel estimation, transmit antenna correlation and path loss. The tightness of the authors' proposed closed-form SE lower bound is shown via simulation results. The optimal pilot reuse factor can be determined with different system configurations by simulations, and the pilot reuse factor that is larger than one is more suitable for less TAs and user equipments.

Group Zero-Forcing for Dense Small Cell Network With Static Pilot Reuse

In this letter, we consider the uplink in a dense small cell network (DSCN) with static pilot reuse, where the user terminals (UTs) in far-distant small cells using the same pilot are assigned to one group, whereas the UTs in neighboring small cells employing orthogonal pilots are allocated to different groups. With large pilot reuse factor, the DSCN has less intra-group interference resulting from pilot contamination in the same group. However, it is difficult to mitigate the inter-group interference caused by co-channel interference in the different groups, which will severely degrade the system performance. To this end, we propose a group zero-forcing (GZF) receiver which actively nullifies the strongest inter-group interferers among all neighboring user groups. For the proposed scheme, we derive exact expression of the uplink per-user achievable rate with GZF. Simulation results verify our theoretical analysis and demonstrate that proposed GZF scheme is very effective in suppressing inter-group interferences.

Novel Multi-cell Precoding Schemes for TDD Massive MIMO Systems

Two novel multi-cell precoding schemes, maximum ratio combining precoding with base station cooperation (MRC-BSC) and zero forcing precoding with BS cooperation (ZF-BSC), are proposed for time division duplex massive multiple-input multiple-output system. By making full use of other cells (not target cell) channel estimate information and changing the matrix structure of classical single-cell maximum ratio combining (MRC) precoding and zero forcing (ZF) precoding, proposed precoding schemes are obtained to reduce inter-cell interference with low complexity. The expressions of lower bound per-user achievable rate of proposed precoding schemes for different uplink pilot reuse factors situations are derived through theoretic analysis. Simulation results demonstrate that the proposed MRC-BSC and ZF-BSC can always achieve higher rates than single-cell MRC precoding and ZF precoding. For each precoding scheme, the downlink achievable rate can be improved greatly when enlarging the uplink pilot reuse factor. In addition, the proposed precoding schemes does not affect (or even improve) other cells' performance while improving the target cell's performance.

Massive MIMO with multi-cell MMSE processing: exploiting all pilots for interference suppression

A new state-of-the-art multi-cell minimum mean square error (M-MMSE) scheme is proposed for massive multiple-input-multiple-output (MIMO) networks, which includes an uplink MMSE detector and a downlink MMSE precoder. Contrary to conventional single-cell schemes that suppress interference using only channel estimates for intra-cell users, our scheme shows the optimal way to suppress both intra-cell and inter-cell interference instantaneously by fully utilizing the available pilot resources. Specifically, let K and B denote the number of users per cell and the number of orthogonal pilot sequences in the network, respectively, where β=B/K is the pilot reuse factor. Our scheme utilizes all B channel directions that can be estimated locally at each base station, to actively suppress both intra-cell and inter-cell interference. Our scheme is practical and general, since power control, imperfect channel estimation, and arbitrary pilot allocation are all accounted for. Simulations show that significant spectral efficiency (SE) gains are obtained over the conventional single-cell MMSE scheme and the multi-cell zero-forcing (ZF) scheme. Furthermore, large-scale approximations of the uplink and downlink signal-to-interference-and-noise ratios (SINRs) are derived, which are tight in the large-system limit. These approximations are easy to compute and very accurate even for small system dimensions. Using these SINR approximations, a low-complexity power control algorithm is further proposed to maximize the sum SE.

Achievable Rates in Uplink Massive MIMO Systems with Pilot Hopping

Massive MIMO promises large spectral and power efficiency gains. However, pilot contamination limits these aspired gains. In this paper, we propose a structured, i.e., non-random, pilot hopping and a weighted moving average channel estimation for time-varying massive MIMO systems. The proposed algorithm diversifies the contaminating (interfering) sources, such that all the users enjoy the hopping advantages. We analyze the performance of multi-cell network, where the base stations perform matched filtering or zero-forcing for data detection. Using polynomial expansion approximations, we obtain achievable rates that are tight even at finite (not very large) number of antennas. It is shown that the spectral efficiency gain achieved by pilot hopping is not a monotonic function of the pilot reuse factor $\beta $ in some cases and, therefore, brings to attention that careful cell planning is required when applying pilot hopping. Moreover, the proposed algorithm involves a weighted moving average that uses the previously obtained channel estimates (of the preceding blocks) if they are correlated with the current one. Due to the massive MIMO properties, the algorithm is robust to channel time-variations and can be used with any previously proposed channel estimation method.