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Pilot contamination (PC) is a stumbling block in the way of realizing massive multi-input multi-output (MIMO) systems. This contribution proposes a location-aware channel estimation enhanced massive MIMO system employing timedivision duplexing protocol, which is capable of significantly reducing the inter-cell interference caused by PC and, therefo...
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... cells as depicted in Fig. 1. In each cell, K single-antenna users are served at the same time/frequency resource. The BS within each cell is equipped with M antennas, where M ≫ K. We assume that the frequency reuse factor is 1 and the same frequency band is used by all cells. The data transmission is divided into three stages, as illustrated in Fig. 2 ...
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... shortest possible training duration of τ = K is applied for our proposed scheme. Note that given the CHI, it is vital to keep the training duration as short as possible in order to maintain the effective spectral efficiency of the massive MIMO system. Moreover, if the training duration is not shorter than the CHI, then the massive MIMO system of Fig. 2 cannot be ...
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... section, the FFT size N is on the same order of M . Therefore, our proposed location-aware channel estimation algorithm requires approximately twice complexity of the conventional channel estimation scheme. Like the conventional channel estimation scheme, our proposed scheme can operate under the fastest changing environment that the system of Fig. 2 can cope with, as it also only requires the shortest training duration of ...
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... pointed out previously, it takes at least several or several tens of seconds for a user to cross a sector, which is hundreds or thousands times longer than the typical mobile channel's coherent time. The implication is that the pilot assignment remains valid for the network operation duration over hundreds of frames, with the frame structure of Fig. 2. Only when the users' sector information have changed significantly, the pilots need to re-assign. Moreover, since the BSs have the users' sector information, when to re-start the pilot assignment procedure is automatically ...
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Cell-free (CF) massive multiple-input multiple-output (MIMO) systems have a large number of individually controllable antennas distributed over a wide area for simultaneously serving a small number of user equipment (UE). This solution has been considered as a promising next-generation technology due to its ability to offer a similar quality of ser...
The upcoming fifth-generation wireless mobile systems are projected to be commercialized in the year 2020 and are set to play the main role in massive multiple input-multiple output (mMIMO) integrated with the technologies of beamforming antenna arrays. However, the performance of mMIMO is constrained by inter-cell interference from adjacent cells...
Citations
... Since the UEs are subject to different channel conditions, the authors of [20] separates the UEs into two groups i.e center and outer UEs; therefore, the same set of the OPSs is reused for the center UEs of the overall cells, while a specific OPSs is allocated to each outer UE; however, this approach requires extra OPSs. Similar to [20], in [21], the UEs were divided into different groups based on their geographical positions, where OPSs are allocated to the UEs that belong to closer sectors; however, intra-sector interference is ignored, furthermore, the UEs are assumed to have such non-overlapping angle-of-arrivals (AoAs). Based on the UEs' LSF coef f icient , [22] computes the severity of the PC upon each UEs; therefore, the assignment of the available OPSs is controlled by the maximization of the minimum SINR. ...
Deploying base stations with a massive number of antennas give birth to massive multi-input multi-output technology, which can boost the quality of the wireless communication; however, the limitations imposed by the length of the coherence interval has, directly, limited the number of orthogonal pilot sequences (OPSs) that can be exploited for acquiring such accurate channel state information at each base station (BS). Hence, the available pilot resources are reused among several cells, which causes the problem of pilot contamination (PC). In this paper, the PC problem is analyzed, and by exploiting the large-scale fading coefficients of the users' equipment (UEs), we have classified the users within each cell; therefore, the same OPS is, solely, reused by the far-distant UEs; hence, the influence of the PC problem is made as small as possible. Since the UEs are subject to different channel conditions, we have consolidated our proposal by a power control algorithm, which boosts the performance of our proposal. Simulation results prove the effectiveness of our proposed decontaminating strategy.
... When several users in the system use the same pilots, pilot contamination will result in inaccurate channel estimation and the BS cannot obtain accurate channel state information (CSI) [23][24][25][26][27][28][29]. In the time-division duplex (TDD) massive MIMO-HWSN system which employs pilots to conduct uplink channel estimation, the pilot contamination seriously hinders the increasing trend of wireless communication system capacity and delays the process of standardization and industrialization [30,31]. ...
... This scheme uses the feature of the massive MIMO system to decompose the covariance matrix of the received signal by eigenvalue decomposition, so as to achieve the purpose of distinguishing users. References [31,32,[36][37][38][39] gave a channel estimation scheme combined with geographic location information. After the channel estimation based on the uplink pilot sequence, the scheme performs a postprocessing process based on the fast Fourier transform on the steering vector at the BS. ...
Pilot power allocation for Internet of Things (IoT) devices in massive multi-input multioutput heterogeneous wireless sensor networks (MIMO-HWSN) is studied in this paper. The interference caused by fractional pilot reusing in adjacent cells had a negative effect on the MIMO-HWSN system performance. Reasonable power allocation for users can effectively weaken the interference. Motivated by the water-filling algorithm, we proposed a suboptimal pilot transmission power method to improve the system capacity. Simulation results show that the proposed method can significantly improve the uplink capacity of the system and explain the influence of different pilot transmission power on the performance of the system, but the complexity of the system almost does not increase.
... these metrics are the power allocation, the number of active antennas deployed in each BS, and the pilot assignment. In [24], cells had been divided into sectors, and based on a greedy algorithm, users of different cells, which are wandering in close sectors, are assigned with orthogonal pilot sequences. However, [24] assumes that users of the same sector are allowed to reuse the same pilots which can lead to a problem of intra-sector interference, also, the users are assumed to employ a nonoverlapping AoAs. ...
... In [24], cells had been divided into sectors, and based on a greedy algorithm, users of different cells, which are wandering in close sectors, are assigned with orthogonal pilot sequences. However, [24] assumes that users of the same sector are allowed to reuse the same pilots which can lead to a problem of intra-sector interference, also, the users are assumed to employ a nonoverlapping AoAs. The authors of [25] focus on the enhancement of the secrecy of wireless communications against the jamming/attacks through designing robust estimators and detectors. ...
This paper provides an algorithm that aims to address the problem of Pilot Contamination (PC) within Massive MIMO systems. The algorithm is mainly derived from the asymptotic regime, where Base Stations (BSs) are equipped with a large number of antennas. Regarding the fact that the available pilot resources are limited and many users should be assigned with pilots, the reuse of the same pilots within different cells is essential. Accordingly, the pattern where pilots are reused within different cells is considered herein. Specifically, the large scale fading coefficients (LSFs) are exploited to show that the PC upon users depends on the distance between users that employ the same pilots. Hence, instead of optimizing the manner where pilots are assigned to users, the proposed algorithm assigns pilots to users of different cells based on their LSF coefficients and through the exploitation of a simple matrix arrangement. Simulation results show that the problem of PC is widely reduced.
... Nevertheless, TDD massive MIMO systems suffer from performance degradation caused by pilot contamination [5] and calibration error of radio frequency (RF) chains [6]. While pilot contamination can be mitigated or even eliminated by careful pilot designs [7]- [9], calibration error of RF chains will render the DL channel and the UL channel nonreciprocal. Moreover, massive MIMO is expected to be compatible with the current cellular systems, where frequency division duplex (FDD) protocol dominates [4]. ...
This paper proposes an virtual angular-domain channel estimation scheme for massive multiple-input multiple-output systems operating in frequency division duplex (FDD) mode. Different from the conventional scheme where orthogonal pilots are transmitted on different antennas, we propose to transfer the channel estimation problem to the virtual angular domain and utilize the channel sparsity to reduce the training and feedback overhead. An orthogonal matching pursuit with Gram-Schmidt orthogonalization algorithm is proposed to construct the unitary transformation between the spatial domain and the virtual angular domain, which achieves higher sparsity than the existing approaches. Furthermore, we propose to estimate the downlink (DL) dominant angular set, which captures most of the channel power with only a few elements, by utilizing the directional reciprocity of FDD systems, where a calibration algorithm is introduced to handle the different wavelengths of uplink and DL transmissions. Based on the estimated dominant sets, we introduce a partial orthogonal criterion for virtual angular-domain pilot design and further propose two pilot assignment algorithms which minimize pilot overhead and pilot-reuse interference, respectively. Theoretical analyses on pilot overhead and the mean square error (MSE) performance are also presented. Simulation results demonstrate that our proposed virtual angular-domain channel estimation scheme provides excellent MSE performance with much reduced pilot overhead and, consequently, enjoys much larger per-user achievable rate in comparison to the conventional schemes.
... Several pilot assignment schemes have been proposed recently based on the location-aware approach [8,9]. Authors have proposed some filtering techniques which are able to significantly remove pilot contamination from channel estimates if the training signals transmitted from users of different cells sharing a given pilot reach the considered BS with different angle-of-arrivals (AOAs). ...
... For this sake, the location-aware PA technique assigns the pilots for users in the considered cell aiming to ensure that users utilizing the same pilot have distinguishable AOAs. However, all these schemes rely on the existence assumption of a line-of-sight component between BS and users, and of small channel angle spread of the multi-path components observed at the BS, typically observed in rural and sub-urban areas, or if the BS is much higher than the surrounded structures with few scatters around [9]. Since this is not always the case, we investigate in this paper PA schemes to be deployed in a less restrictive scenarios. ...
... Then, in Sect. 4, we evaluate the power control algorithm based on the pilot assignment obtained here, and demonstrate the performance improvements of our proposed schemes for finite number of antennas in Sect. 5. SINR expressions in (9) were obtained assuming that the k -th pilot sequence 2 is assigned to the kth user, i.e., under a random strategy. However, if we assume that the p-th pilot is assigned to the c p -th user in the i-th cell, the UL SINR can be rewritten as ...
We investigate the effects of pilot assignment in multi-cell massive multiple-input multiple-output systems. When deploying a large number of antennas at base station (BS), and linear detection/precoding algorithms, the system performance in both uplink (UL) and downlink (DL) is mainly limited by pilot contamination. This interference is proper of each pilot, and thus system performance can be improved by suitably assigning the pilot sequences to the users within the cell, according to the desired metric. We show in this paper that UL and DL performances constitute conflicting metrics, in such a way that one cannot achieve the best performance in UL and DL with a single pilot assignment configuration. Thus, we propose an alternative metric, namely total capacity, aiming to simultaneously achieve a suitable performance in both links. Since the PA problem is combinatorial, and the search space grows with the number of pilots in a factorial fashion, we also propose a low complexity suboptimal algorithm that achieves promising capacity performance avoiding the exhaustive search. Besides, the combination of our proposed PA schemes with an efficient power control algorithm unveils the great potential of the proposed techniques in providing improved performance for a higher number of users. Our numerical results demonstrate that with 64 BS antennas serving 10 users, our proposed method can assure a 95%-likely rate of 4.2 Mbps for both DL and UL, and a symmetric 95%-likely rate of 1.4 Mbps when serving 32 users.
... From the closed form of η LS kj in (12), this problem is nonconvex. Fortunately, since all variables ρ kj , ∀k, j are nonnegative, the numerator of η LS kj is a posynomial function and the denominator is a monomial function so consequently, the objective function L j=1 K k=1 η LS kj is a posynomial function, and it is in the form of a geometric program (GP) [25]. ...
... From (29) and (30), we have the closed form of NMSE for the LS method as in (12). We now calculate the closed form of NMSE for MMSE method. ...
In this paper, we consider pilot and data power allocation in multi-cell multi-user massive multiple-input multiple-output (MIMO) systems to maximize the summation of spectral efficiency (sum SE). In conventional massive MIMO systems, equal pilot and data power is assigned to all users so the sum SE is not good. A common way to maximize sum SE is forming a maximization problem with the sum SE objective function containing both pilot and data power as variables. However, pilots are only used for channel estimation so pilot power should be allocated based on channel estimation quality instead of sum SE. Moreover, improving significantly channel estimation quality also helps to indirectly improve the sum SE. This motivated us to propose a disjoint pilot and data power allocation where pilot powers are optimized based on the sum normalized mean squared error (sum NMSE) channel estimation minimization problem and data powers are optimized based on sum SE maximization problem. We consider least squares (LS) and minimum mean squared error (MMSE) estimation methods which are commonly used in massive MIMO. In the first step, we formulate a minimization problem to minimize the summation of NMSE of the system under maximum pilot power per user constraint. Minimization problem for MMSE method is nondeterministic polynomial-time (NP) hard so we propose an algorithm to find the local optimum point. First step is only related to pilot powers and indirectly improves the sum SE by improving channel estimation quality. In second step, we further improve the sum SE by formulating a sum SE maximization problem with data powers as variables under maximum data power per user constraint. Since this problem is NPhard, we derive a lower bound on the sum SE and maximize this bound instead Numerical results show the advantages of our proposed approach in comparison with existing schemes.
... In this section, we demonstrate the benefits of the proposed pilot allocation algorithm with random pilot allocation [9], the greedy iterative algorithm [10], and sector-based [13] as benchmarks. Random pilot allocation is the most widely adopted pilot allocation algorithm in massive MIMO [3], [4], [6]. ...
... Greedy iterative algorithm [10] iteratively refines the sum rate by first identifying the user with the lowest rate and then searching a pilot sequence for the user which minimizes the interference. Sector-based algorithm divides the cell-area in sectors and all users in a sector are assigned the same pilot [13]. The system settings adopted in this section are summarized in Table I. ...
We propose a location-aware pilot allocation algorithm for a massive multiple-input multiple-output (MIMO) network with high-mobility users, where the wireless channels are subject to Rician fading. Pilot allocation in massive MIMO is a hard combinatorial problem and depends on the locations of users. As such, it is highly complex to achieve the optimal pilot allocation in real-time for a network with high-mobility users. Against this background, we propose a low-complexity pilot allocation algorithm, which exploits the behavior of line-of sight (LOS) interference among the users and allocate the same pilot sequence to the users with small LOS interference. Our examination demonstrates that our proposed algorithm significantly outperforms the existing algorithms, even with localization errors. Specifically, for the system considered in this work, our proposed algorithm provides up to 37.26% improvement in sum spectral efficiency (SE) and improves the sum SE of the worst interference-affected users by up to 2.57 bits/sec/Hz, as compared to the existing algorithms.
... In this section, we demonstrate the benefits of the proposed pilot allocation algorithm with random pilot allocation [9], the greedy iterative algorithm [10], and sector-based [13] as benchmarks. Random pilot allocation is the most widely adopted pilot allocation algorithm in massive MIMO [3], [4], [6]. ...
... Greedy iterative algorithm [10] iteratively refines the sum rate by first identifying the user with the lowest rate and then searching a pilot sequence for the user which minimizes the interference. Sector-based algorithm divides the cell-area in sectors and all users in a sector are assigned the same pilot [13]. The system settings adopted in this section are summarized in Table I. ...
... We highlight that (13) cannot be computed unless the locations of users and channel estimates are known to the BS. However, assuming that the BSs have estimated user locations (d ij ,θ ij ), we calculate an estimate for the first term, i.e, the pure LOS term in (13), aŝ ...
We propose a location-aware pilot allocation algorithm for a massive multiple-input multiple-output (MIMO) network with high-mobility users, where the wireless channels are subject to Rician fading. Pilot allocation in massive MIMO is a hard combinatorial problem and depends on the locations of users. As such, it is highly complex to achieve the optimal pilot allocation in real-time for a network with high-mobility users. Against this background, we propose a low-complexity pilot allocation algorithm, which exploits the behavior of line-of-sight (LOS) interference among the users and allocate the same pilot sequence to the users with small LOS interference. Our examination demonstrates that our proposed algorithm significantly outperforms the existing algorithms, even with localization errors. Specifically, for the system considered in this work, our proposed algorithm provides up to 37.26% improvement in sum spectral efficiency (SE) and improves the sum SE of the worst interference-affected users by up to 2.57 bits/sec/Hz, as compared to the existing algorithms.
... , the angle spread depends on the distance of the user from BS, D , and the maximum distance of the scatterers from the user, R . In [31][32] [17]. ...
This paper investigates downlink (DL) channel estimation of frequency division duplex (FDD) massive multiple-input-multiple-output (MIMO) with finite number of BS antennas. Based on the performance analysis of downlink channel in FDD, it is demonstrated that the interference effect cannot be ignored in the finite region. Also, excessive training overhead is required in conventional channel estimation methods when the number of BS antennas goes large. Therefore, it is necessary to design an algorithm for alleviating the interference in addition to the reduction of pilot overhead. In this paper, a general model is presented to exploit certain sparsity structure of massive MIMO channel in beam-domain, which is called nonzero-neighborhood (NZN) structure. Considering the smooth variation of channel paths during two consecutive estimation intervals, a compressed sensing (CS) based algorithm is proposed to efficiently estimate and track NZN structured DL channel. The convergence behavior and computational complexity of the proposed mobility-aware subspace pursuit (MA-SP) algorithm is analysed based on restricted isometric property (RIP). It is demonstrated that the application of proposed MA-SP algorithm efficiently reduces the required pilot length in channel estimation. Interference analysis shows that inter-cell interference is considerably alleviated in the proposed channel estimation phase without any prior information about interference. Simulation results demonstrate the effectiveness of the proposed MA-SP technique compared to the previous methods.
... Another interesting research direction in PC decontamination is based on the user statistical channel information such as AOA, channel covariance [13] [14] [15]. The author of [14] used Bayesian estimator to estimate the desired channels and they proved that, with number of BS antennas M goes to infinity and non-overlapping AOA condition between desired user and interfering users is satisfied perfectly, the Bayesian estimated channel of desired user with interfering users coincides with the case when no interference happens. ...
... [15] also needs to use the second-order statistic of user channels and also does not evaluate directly the nonoverlapping condition or the potential mutual interference between any two users. The location-based channel estimation [13] utilizes the single-frequency property of the steering vector to apply a post-processing discrete Fourier transform (DFT) filtering process after the conventional pilot-based channel estimation. The post-processing DFT filtering process can effectively distinguish the users with different AoAs even if they have the same pilot sequence, especially when in an asymptotic regime, where it has been proved that the intercell interference caused by users with non-overlapping AoAs can be completely eliminated. ...
... The post-processing DFT filtering process can effectively distinguish the users with different AoAs even if they have the same pilot sequence, especially when in an asymptotic regime, where it has been proved that the intercell interference caused by users with non-overlapping AoAs can be completely eliminated. To meet the non-overlapping AoAs criteria, [13] proposed a suboptimal pilot assignment algorithm; however this algorithm divides each cell into some sectors, and each sector has a representative user located in the center of the sector and they use the location information of these representative users to create the algorithm. This algorithm is only effective if each user is distributed evenly in one sector, which is not a practical scenario. ...
Massive multiple-input multiple-output (MIMO) is considered as a promising technique in wireless communication systems, which contains cellular base stations (BS) equipped with a very large number of antennas to serve multiple users. However, the performance of massive MIMO is limited by the impact of pilot contamination (PC) due to inter-cell interference (ICI). In conventional massive MIMO systems, pilot sequences are randomly assigned to users without any further consideration. In this paper, a vertex graph coloring-based pilot assignment (VGC-PA) algorithm is proposed in conjunction with the existing post-processing discrete Fourier transform (DFT) filtering channel estimation to mitigate the inter-cell interference between users with the same pilot sequence in the channel estimation process to improve the capacity of the whole system. Specifically, we propose a metric to measure the potential ICI strength between any two users in the system based on their angle of arrival (AoA), correlation, and distances to construct a ICI graph where each user is regarded as a node. This ICI graph denotes the potential ICI strength relationship among all users in the system. Then, we propose a VGC-PA algorithm to mitigate the ICI relationship between users with same pilot sequences by assigning different pilots to connected users with high ICI metric based on some criteria. After the pilot assignment process, we apply the existing post-processing DFT filtering in the channel estimation process. The simulation results show the significant improvement of our proposed VGC-PA algorithm compared with existing methods.
