Hybrid precoding matlab code

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Relay Hybrid Precoding Design in Millimeter-Wave Massive MIMO Systems

The weights are computed using an orthogonal matching pursuit algorithm. Together, the precoding weights approximate the optimal full digital precoding weights of chanmat. The input nrrf specifies the number of RF chains in the receive array. All subcarriers are assumed to have the same noise power. Show that the hybrid weights can support transmitting two data streams simultaneously.

Calculate the effective channel matrix from the weights. A diagonal effective channel matrix indicates the capability of simultaneous transmission of multiple data streams.

Channel response matrix, specified as an N t -by- N r matrix or a complex-valued L -by- N t -by- N r array where. N t is the number of elements in the transmitting array.

MATLAB code- Millimeter Wave Beam Alignment using MR code book and beamforming

N r is the number of elements in the receiving array. Number of independent data streams propagated through the channel, specified as a positive integer.

Collection of possible analog weights for wprfspecified as a complex-valued matrix or array. When chanmat is an N t -by- N r matrix, at is a complex-valued N t -by- P matrix. Each column represents a vector of analog weights. Each page is an N t -by- P matrix. P is the number of vectors of analog weights in the collection. When chanmat is an N t -by- N r matrix, ar is a complex-valued N r -by- Q matrix. Each page is an N r -by- Q matrix. Q is the number of vectors of analog weights in the collection.

Noise power in each receive antenna element, specified as a nonnegative scalar. All subcarriers have the same noise power. When chanmat is an N t -by- N r matrix, wpbb is a complex-valued N s -by- N trf matrix. N s is the number of independent data streams specified by the ns argument. N trf is the number of RF chains in the transmit array specified by the ntrf argument. When chanmat is an N t -by- N r matrix, wprf is a complex-valued N trf -by- N t matrix.

When chanmat is an N t -by- N r matrix, wcbb is a complex-valued N rrf -by- N s matrix. N rrf is the number of RF chains in the receive array specified by the nrrf argument.

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When chanmat is an N t -by- N r matrix, wcrf is a complex-valued N r -by- N rrf matrix. The matrix product of the precoding weights wpbb x wprf approximates the optimal full digital precoding weights of the channel matrix chanmat.

The combining weights wcbb and wcrftogether with the precoding weights, diagonalize the channel into independent subchannels. The matrix product wpbb x wprf x chanmat x wcrf x wcbb is approximately diagonal. Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select:. Select the China site in Chinese or English for best site performance.

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Use of this web site signifies your agreement to the terms and conditions. Personal Sign In. For IEEE to continue sending you helpful information on our products and services, please consent to our updated Privacy Policy. Email Address. Sign In. Channel Estimation and Hybrid Precoding for Millimeter Wave Cellular Systems Abstract: Millimeter wave mmWave cellular systems will enable gigabit-per-second data rates thanks to the large bandwidth available at mmWave frequencies.

To realize sufficient link margin, mmWave systems will employ directional beamforming with large antenna arrays at both the transmitter and receiver.

hybrid precoding matlab code

Due to the high cost and power consumption of gigasample mixed-signal devices, mmWave precoding will likely be divided among the analog and digital domains. The large number of antennas and the presence of analog beamforming requires the development of mmWave-specific channel estimation and precoding algorithms. This paper develops an adaptive algorithm to estimate the mmWave channel parameters that exploits the poor scattering nature of the channel.

To enable the efficient operation of this algorithm, a novel hierarchical multi-resolution codebook is designed to construct training beamforming vectors with different beamwidths. For single-path channels, an upper bound on the estimation error probability using the proposed algorithm is derived, and some insights into the efficient allocation of the training power among the adaptive stages of the algorithm are obtained.

The adaptive channel estimation algorithm is then extended to the multi-path case relying on the sparse nature of the channel. Simulation results show that the proposed low-complexity channel estimation algorithm achieves comparable precoding gains compared to exhaustive channel training algorithms. The results illustrate that the proposed channel estimation and precoding algorithms can approach the coverage probability achieved by perfect channel knowledge even in the presence of interference.

Article :. Date of Publication: 01 July DOI: Need Help?Updated 05 Mar This is an implementation of the narrowband hybrid precoding algorithms for mmWave single-user MIMO systems. The reference paper is O. Ayach, S. Rajagopal, S. Abu-Surra, Z. Pi and R. Wireless Commun. The code is uploaded for verification purpose, plotting Fig. Retrieved July 14, Thank you very much for giving this type of code. It's really practical for me to learn. I'd like to study your paper. Learn About Live Editor.

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Cite As - Narengerile Comments and Ratings 2. Updates 5 Mar 1. Tags Add Tags capacity hybrid precoding millimeter wave mimo. Discover Live Editor Create scripts with code, output, and formatted text in a single executable document.

Select a Web Site Choose a web site to get translated content where available and see local events and offers. Select web site.We propose an iterative successive approximation ISA algorithm to attain the high-approximate optimal solution to the original problem. Specifically, in the proposed ISA algorithm, we first convert the two nonconvex subproblems to convex ones by the relaxation of the constant-modulus constraints, and then we solve the three corresponding convex subproblems iteratively.

A S A promising technology for the next generation of wireless communications, millimeter-wave mmWave communication has drawn extensive research interests in the recent years. By utilizing large spectrum bands between 30GHz and GHz, mmWave communication is capable of meeting the explosive growth of data rate. Although themmWave signals undergo severe path loss, the path loss can be compensated by high antenna gain using massive multiple-input multipleoutput MIMO.

However, mmWave communications are mainly applied in line-of-sight LoS dominant scenarios, since mmWave signals are sensitive to blockage. To mitigate the negative effects caused by blockage, relay can be employed in mmWave massive MIMO systems.

In a relay-assisted mmWave system, the channels from the source to the relay and from the relay to the destination may be LoS, and the transmission range and coverage can be extended.

However, the optimal precoding design for the relay-assisted mmWave massive MIMO systems is a challenging problem due to the complicated signal processing.

Gao, L. Dai, S. Han, C. I, and X. Wireless Commun. Ayach, S. Rajagopal, S. Abu-Surra, Z. Pi, and R. The hybrid precoding is jointly realized in the digital and analog domains, where the digital precoding is realized by baseband signal processing, while the analog precoding is usually implemented by analog phase shifters.

This challenging problem is highly non-convex due to the six-order polynomial objective function, sixorder polynomial constraint, block-diagonal constraints, and constant-modulus constraints. To eliminate the blockdiagonal constraints and reduce the problem dimension, we reformulate the original problem as three subproblems.

Here, one of these three subproblems is a convex quadratically constrained quadratic programming QCQP problem, while the other two subproblems are non-convex QCQP problems with constant-modulus constraints.

To solve these three subproblems, we propose an iterative successive approximation ISA algorithm with affordable complexity. Then, for the two non-convex QCQP subproblems, we convert them to be convex by the relaxation of the constant-modulus constraints. Then, the high-approximate solution is obtained by iteratively solving these three convex problems. This section briefly introduces the mmWave channel model and the relay hybrid precoding system model with both subconnected and full-connected structures.

As shown in Figs. Here, we assume that the channels H and G are LoS. In this paper, we consider the narrowband mmWave channel model widely used in the literatures.

Due to the fact thatmmWave channels exhibit limited number of paths ,HandGoften have sparse structures, which can be characterized by low-rank matrices as follows:. For both the full-connected and the sub-connected structures as shown in Figs. Due to the fact thatmmWave channels exhibit limited number of paths ,HandGoften have sparse structures, which can be characterized by low-rank matrices as follows: Relay Hybrid Precoding For both the full-connected and the sub-connected structures as shown in Figs.

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hybrid precoding matlab code

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Build automated tests using Playwright 6 days left. Test Automation Website Testing. Small-business-website translation from English to Traditional Chinese. Article writing -- 2 6 days left. API Python.Documentation Help Center. This example introduces the basic concept of hybrid beamforming and shows how to simulate such a system. Modern wireless communication systems use spatial multiplexing to improve the data throughput within the system in a scatterer rich environment. In order to send multiple data streams through the channel, a set of precoding and combining weights are derived from the channel matrix.

Then each data stream can be independently recovered.

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Those weights contain both magnitude and phase terms and are normally applied in the digital domain. In the system diagram shown below, each antenna is connected to a unique transmit and receive TR module. The ever growing demand for high data rate and more user capacity increases the need to use the spectrum more efficiently.

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As a result, the next generation, 5G, wireless systems will use millimeter wave mmWave band to take advantage of its wider bandwidth. In addition, 5G systems deploy large scale antenna arrays to mitigate severe propagation loss in the mmWave band. However, these configurations bring their unique technical challenges.

Compared to current wireless systems, the wavelength in the mmWave band is much smaller. Although this allows an array to contain more elements with the same physical dimension, it becomes much more expensive to provide one TR module for each antenna element.

Hence, as a compromise, a TR switch is often used to supply multiple antenna elements. This is the same concept as the subarray configuration used in the radar community. One such configuration is shown in the following figure. To provide more flexibility, each antenna element can be connected to one or more TR modules.

In addition, analog phase shifters can be inserted between each TR module and antenna to provide some limited steering capability. The configuration on the receiver side is similar, as shown in the figure. In this configuration, it is no longer possible to apply digital weights on each antenna element.

Instead, the digital weights can only be applied at each RF chain. At the element level, the signal is adjusted by analog phase shifters, which only changes the phase of the signal. Thus, the precoding or combining are actually done in two stages. Because this approach performs beamforming in both digital and analog domains, it is referred to as hybrid beamforming. This section simulates a 64 x 16 MIMO hybrid beamforming system, with a element square array with 4 RF chains on the transmitter side and a element square array with 4 RF chains on the receiver side.

In this simulation, it is assumed that each antenna is connected to all RF chains. Thus, each antenna is connected to 4 phase shifters. Such an array can be modeled by partitioning the array aperture into 4 completely connected subarrays. To maximize the spectral efficiency, each RF chain can be used to send an independent data stream. In this case, the system can support up to 4 streams.

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Next, assume a scattering environment with 6 scattering clusters randomly distributed in space. Within each cluster, there are 8 closely located scatterers with a angle spread of 5 degrees, for a total of 48 scatterers. The path gain for each scatterer is obtained from a complex circular symmetric Gaussian distribution. In a spatial multiplexing system with all digital beamforming, the signal is modulated by a set of precoding weights, propagated through the channel, and recovered by a set of combining weights.

In a hybrid beamforming system, the signal flow is similar. Both the precoding weights and the combining weights are combinations of baseband digital weights and RF band analog weights. The baseband digital weights convert the incoming data streams to input signals at each RF chain and the analog weights then convert the signal at each RF chain to the signal radiated or collected at each antenna element.A simple example with how hybrid beamforming is employed at the transmit end of a massive MIMO communications system.

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This example shows how hybrid beamforming is employed at the transmit end of a massive MIMO communications system, using techniques for both multi-user and single-user systems. The example employs full channel sounding for determining the channel state information at the transmitter.

It partitions the required precoding into digital baseband and analog RF components, using different techniques for multi-user and single-user systems. Simplified all-digital receivers recover the multiple transmitted data streams to highlight the common figures of merit for a communications system, namely, EVM, and BER.

hybrid precoding matlab code

A simpler static-flat MIMO channel is also offered for link validation purposes. The ever-growing demand for high data rate and more user capacity increases the need to use the available spectrum more efficiently. Massive MIMO allows the number of BS antenna elements to be on the order of tens or hundreds, thereby also increasing the number of data streams in a cell to a large value. The next generation, 5G, wireless systems use millimeter wave mmWave bands to take advantage of their wider bandwidth.

The 5G systems also deploy large scale antenna arrays to mitigate severe propagation loss in the mmWave band. Compared to current wireless systems, the wavelength in the mmWave band is much smaller. Although this allows an array to contain more elements within the same physical dimension, it becomes much more expensive to provide one transmit-receive TR module, or an RF chain, for each antenna element.

Hybrid transceivers are a practical solution as they use a combination of analog beamformers in the RF and digital beamformers in the baseband domains, with fewer RF chains than the number of transmit elements [1]. Skip to content. Dismiss Join GitHub today GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together.

hybrid precoding matlab code

Sign up. Branch: master. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Git stats 8 commits 1 branch 0 tags. Failed to load latest commit information. View code. About A simple example with how hybrid beamforming is employed at the transmit end of a massive MIMO communications system.

Topics matlab mimo network beamforming hybrid 5g simulation mu-mimo communications example transmit system receiver transmitter hybrid-beamforming multi-user-mimo mmwave mmwave-band mimo-ofdm wireless-systems. Releases No releases published. You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window.


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