Spectral and Energy Efficient Low-Overhead Uplink and Downlink Channel Estimation for 5G Massive MIMO Systems

Khan, Imran; Zafar, Mohammad Haseeb; Jan, Mohammad Tariq; Lloret, Jaime; Basheri, Mohammed; Singh, Dhananjay

doi:10.3390/e20020092

Cited by 40 publications

(27 citation statements)

References 36 publications

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“…The beam control vector is solved by analog beam training, and the optimal performance can be achieved by using the exhaustive algorithm [22] to obtain the beam control vector of each sub-array. However, as the number of antenna sub-arrays increases, the excessive algorithm complexity makes the exhaustive algorithm difficult to achieve.…”

Section: Hybrid Beamforming Design Of Low Complexity Split Sub-arraymentioning

confidence: 99%

“…The sub-array beam control vector (BCV) algorithm was optimized, and the interference between sub-arrays was reduced by the proposed algorithm, and the performance achieved by this algorithm was gradually improved as the number of RF links increased. At the same time, we also compared the complexity of the proposed Algorithm 1 with other codebook-based optimized beam Spectral Efficiency (bps/Hz) Digital Beamforming Proposed (Narrow-band Block Fading Channel) Proposed (IID Rayleigh Channel) Reference [9] (Narrow-band Block Fading Channel) Reference [9] (IID Rayleigh Channel) Reference [21] (Narrow-band Block Fading Channel) Reference [21] (IID Rayleigh Channel) Reference [22] (Narrow-band Block Fading Channel) Reference [22] (IID Rayleigh Channel) Reference [23] (Narrow-band Block Fading Channel) Reference [23] (IID Rayleigh Channel) Analog Beamforming Figure 9. Comparison of the spectral efficiency of the algorithms under narrow-band block fading and i.i.d Rayleigh channel.…”

Section: Conclusion and Future Recommendationsmentioning

confidence: 99%

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An Efficient Precoding Scheme for Millimeter-Wave Massive MIMO Systems

Alemaishat¹,

Saraereh

Khan

et al. 2019

Electronics

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Aiming at the problem of high computational complexity due to a large number of antennas deployed in mmWave massive multiple-input multiple-output (MIMO) communication systems, this paper proposes an efficient algorithm for optimizing beam control vectors with low computational complexity based on codebooks for millimeter-wave massive MIMO systems with split sub-arrays hybrid beamforming architecture. A bidirectional method is adopted on the beam control vector of each antenna sub-array both at the transmitter and receiver, which utilizes the idea of interference alignment (IA) and alternating optimization. The simulation results show that the proposed algorithm has low computational complexity, fast convergence, and improved spectral efficiency as compared with the state-of-the-art algorithms.2 of 15 constant constraint so the performance achieved by analog beamforming technology will be limited [6]. Simultaneous use of beamforming in the digital domain and the analog domain can reduce the overhead of the system and compensate for the defects of analog beamforming, making hybrid beamforming a first pre-coding technique [7][8][9][10][11][12][13][14][15][16]. It can be seen from literature [5] that the hybrid beamforming architecture includes a separate sub-array architecture and a shared architecture. The signal on each transmit antenna in the shared architecture is a hybrid superposition of signals on all RF links, and one RF link needs to drive all transmitting antenna. Compared with the shared architecture, one RF link in a separate sub-array architecture is connected to only one antenna sub-array, and each RF link only needs to drive a transmit antenna on the sub-array connected thereto, so the separate sub-array architecture system can effectively reduce energy consumption and hardware complexity. Literature [8] analyzed the relationship between digital beamforming and hybrid beamforming and compared its performance. In [9], an algorithm for determining the sub-array analog beam control vector based on the codebook matching beam pair is proposed for the separate sub-array architecture system. However, the algorithm requires more beam training times, so that it has a high degree of complexity. In [10], both the transmitting and receiving ends are shared hybrid beamforming architectures, and an orthogonal matching pursuit (OMP) algorithm for simultaneous beamforming and digital beamforming is proposed for hybrid beamforming architecture. In [12], based on the codebook with the maximum received power as the criterion, the beamforming method is used to solve the analog beamforming, and then the equivalent channel is used to solve the digital beamforming.Applying interference alignment ideas [17][18][19] in wireless networks can achieve higher system capacity. By integrating multiple interfering signals into a small subspace at the receiving end, maximizing the number of non-interfering dimensions remaining in the desired signal minimizes the impact of the interfering signal on the desired signal while maximizing...

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Section: Hybrid Beamforming Design Of Low Complexity Split Sub-arraymentioning

confidence: 99%

Section: Conclusion and Future Recommendationsmentioning

confidence: 99%

An Efficient Precoding Scheme for Millimeter-Wave Massive MIMO Systems

Alemaishat¹,

Saraereh

Khan

et al. 2019

Electronics

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“…Massive multiple-input multiple-output (MIMO) systems are equipped with a large number of antennas at the base station (BS), which can signi cantly improve the spectrum e ciency and energy e ciency of the system. It is regarded as the most promising technology in the fth-generation (5G) wireless communication system [1][2][3][4][5][6]. In order to obtain the performance gain of a massive MIMO system, the BS side needs to know the channel state information (CSI).…”

Section: Introductionmentioning

confidence: 99%

A Robust Channel Estimation Scheme for 5G Massive MIMO Systems

Khan

Rodrigues

Al‐Muhtadi

et al. 2019

Wireless Communications and Mobile Computing

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Channel state information (CSI) feedback in massive MIMO systems is too large due to large pilot overhead. It is due to the large channel matrix dimension which depends on the number of base station (BS) antennas and consumes the majority of scarce radio resources. To solve this problem, we proposed a scheme for e cient CSI acquisition and reduced pilot overhead. It is based on the separation mechanism for the channel matrix. e spatial correlation among multiuser channel matrices in the virtual angular domain is utilized to split the channel matrix. en, the two parts of the matrix are estimated by deploying the compressed sensing (CS) techniques. is scheme is novel in the sense that the user equipment (UE) directly transmits the received symbols from the BS to the BS, so a joint CSI recovery is performed at the BS. Simulation results show that the proposed channel estimation scheme e ectively estimates the channel with reduced pilot overhead and improved performance as compared with the state-of-theart schemes.

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“…big MIMO (severa data unique yield) is one of the key innovations applied in reducing aspect bendy cell structures, which has an great range of radio wires at its cell base station. MIMO improves incredibly the channel limit and moreover variety use [1]. In a significant MIMO framework, a selected channel country information (CSI) is pressing with the aim that it affects framework signal popularity, beamforming, asset designation, and so forth.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Compressive Sensing-Based Channel Estimation for 5G Massive MIMO Systems

2019

IJITEE

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massive numerous statistics notable yield (MIMO) is a key innovation in a long way off correspondences to get records paces of more than one instances. vast MIMO framework has pretty big variety of reception apparatuses at the base station (BS) worked for overhauling severa clients simultaneously. it is a promising innovation for acknowledgment of immoderatethroughput far off interchanges. titanic MIMO misuses the excessive degree of spatial opportunity with the purpose that it considerably improves the channel limit and power proficiency of the MIMO framework. The massive MIMO frameworks are comprehensively stated as a full-size putting in innovation for fifth era (5G) wireless correspondence frameworks. but, in massive MIMO frameworks, a careful assurance of the channel nation information (CSI)is needed for motivation in the back of feasible sign discovery, asset distribution and beamforming and so forth. those frameworks having an large range of reception apparatuses at the base Stations, clients want to gauge channels which can be associated with severa portions of transmit recieving wires. due to this, pilot overhead seems to be excessive. on this way, acknowledgment of the right channel state statistics with a base pilot overhead can be a tough issue. Reenactment outcomes finished demonstrates that the proposed calculation can swiftly and precisely determine huge MIMO channel of the difficult to understand channel sparsity and with excessive computational productiveness whilst contrasted and distinct beyond calculations. Watchwords: 5G; substantial MIMO; compressive detecting; sparsity flexible; a channel estimationRevised Manuscript

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Spectral and Energy Efficient Low-Overhead Uplink and Downlink Channel Estimation for 5G Massive MIMO Systems

Cited by 40 publications

References 36 publications

An Efficient Precoding Scheme for Millimeter-Wave Massive MIMO Systems

An Efficient Precoding Scheme for Millimeter-Wave Massive MIMO Systems

A Robust Channel Estimation Scheme for 5G Massive MIMO Systems

Adaptive Compressive Sensing-Based Channel Estimation for 5G Massive MIMO Systems

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