Welcome to the website of Yongshun Technology Co.,Ltd.!Today is:
搜索
language
中国
韩国
韩国
韩国
韩国
韩国
News Center
Check category

With maximum similarity detector MIMO antenna transceiver efficiency is upgraded

  • Categories:News Center
  • Time of issue:2021-01-27 14:47
  • Views:

(Summary description)The maximum similarity detector can enhance the MIMO data transmission rate. Through the nonlinear algorithm, the maximum similarity detector can greatly increase the bit transmission rate under the same channel conditions, and at the same time help MIMO deployment spatial multiplexing without being affected by antenna correlation, thereby expanding the advantages of MIMO.

With maximum similarity detector MIMO antenna transceiver efficiency is upgraded

(Summary description)The maximum similarity detector can enhance the MIMO data transmission rate. Through the nonlinear algorithm, the maximum similarity detector can greatly increase the bit transmission rate under the same channel conditions, and at the same time help MIMO deployment spatial multiplexing without being affected by antenna correlation, thereby expanding the advantages of MIMO.

  • Categories:News Center
  • Author:Noam Dvoretzki/Zeev Kaplan
  • Origin:
  • Time of issue:2021-01-27 14:47
  • Views:
Information

The maximum similarity detector can enhance the MIMO data transmission rate. Through the nonlinear algorithm, the maximum similarity detector can greatly increase the bit transmission rate under the same channel conditions, and at the same time help MIMO deployment spatial multiplexing without being affected by antenna correlation, thereby expanding the advantages of MIMO.
Today, global companies are striving to find solutions for the higher data rate requirements of mobile devices. Because the radio spectrum is limited and expensive, how to find a better way to transmit a larger amount of data under the same bandwidth is very important; the key is to improve the spectral efficiency of the channel.
Multiple input multiple output (MIMO) can be used to improve data transmission rate and signal to noise ratio (Signal to Noise Ratio, SNR). By using multiple receiving and transmitting antennas, MIMO can open up the diversity of wireless channels, and can also be used to improve the spectral efficiency of the channel, and increase the information transmission rate for any given bandwidth.

The Dimension of MIMO depends on the number of antenna transmission and reception. A 4×4 MIMO configuration uses four transmitting antennas and four receiving antennas. Under suitable conditions, it can transmit up to four times more data with the same bandwidth.

On the one hand, a simple MIMO receiver is based on a linear algorithm. Although it is easier to implement, it cannot fully expand the advantages of MIMO. On the other hand, the optimization of Maximum a Posteriori (MAP) algorithm is similar to MIMO. Algorithms can be implemented using iterative technology, but this will lead to high latency (Latency) consequences.

There is a more practical implementation of nonlinear MIMO receivers, called Maximum Similarity (ML) or Maximum Similarity Detector (MLD), which is basically based on an exhaustive constellation search. Compared with traditional linear receivers, MLD processing is more complicated, but under the same channel conditions, it can greatly increase the bit rate. In addition, MLD is less likely to be affected by the channel due to antenna correlation (Correlation).

The operation of the high-level MIMO dimension (more than two receivers and more than two transmitting antennas) can greatly improve the spectral efficiency, but this comes at a price, because the computational complexity of the MLD receiver will increase with The dimension of MIMO increases exponentially. High-level MIMO requires considerable processing capabilities. To the extent that the MLD algorithm cannot be used directly, the Suboptimal MLD algorithm has to be used to implement User Equipment (UE).

Make good use of MIMO technology

Figure 1 Schematic diagram of beamforming of transmitter
MIMO technology can be divided into three categories, including beamforming, transmit and receive diversity, and spatial multiplexing. Beamforming is used to improve the signal-to-noise ratio of a given channel; transmit and receive diversity is used to improve channel quality or signal strength; and spatial multiplexing is used to increase the data transmission volume of a given channel.
Beamforming is a technique that uses the transmitter channel to concentrate power in the direction of the receiver. The detailed information on this channel can be obtained through the feedback on the direction and attenuation characteristics received from the receiver. By identifying the direction of the user terminal device, the transmitter can direct the beam to be in that direction, thereby amplifying the received signal. MIMO technology is most effective for channels with low signal-to-noise ratio. Figure 1 is a schematic diagram of the arrival of a directional wave front through the phase of a timing transmitting antenna.

Transmitting and receiving diversity will generate redundancy after transmitting the same data with multiple antennas. The antenna at the receiving end combines the received signals to strengthen the signal strength of a given channel. MIMO technology is most effective under the conditions of low signal-to-noise ratio and multipath (or scattering). By overcoming the attenuation of the antenna, the diversity method can maximize the channel utilization and make full use of the antenna that receives the strength signal. On the whole, the signal-to-noise ratio generated by each antenna can be improved, which can reduce decoding errors in the receiver.

Figure 2 The peak transmission volume of space-time block coding (STBC)
By improving the signal-to-noise ratio, switching to a higher modulation (such as 64QAM instead of 16QAM/QPSK) or increasing the coding rate (transmitting less redundant data), it is expected to increase the transmission volume. However, to improve the signal-to-noise ratio of the channel has its limitations. It shows that when the signal-to-noise ratio exceeds a certain level, the transmission volume gain corresponding to the signal-to-noise ratio in dB decreases rapidly. This turning point describes the highest modulation and coding rate defined by the standard (Figure 2). In order to further increase the transmission volume, it is necessary to use more advanced transmission methods.
The emergence of spatial multiplexing is to push the channel transmission volume to a new level, which can achieve effective operation with the least channels. This technology uses multipath channels in order to distinguish the data sent by different antennas, which is called Spatial Layer. Abundant multipath conditions are generated by the reflection of the transmission signal hitting obstacles (such as buildings and cars in urban environments). These reflections improve the signal separation of the receiver and enable the reconstruction of data into the original spatial domain of the transmitted data.

The number of spatial domains that can be formed is determined by the number of transmitting and receiving antennas. In the configuration of four transmitting antennas and three receiving antennas, the channel can include up to three spatial domains min (4Tx, 3Rx), and the actual number of spatial domains is determined by the multipath condition expressed as the channel rank (Channel Rank). The above-mentioned (4Tx, 3Rx) configuration has linear propagation (Line-of-sight) conditions, but without multipath reflection, the rank will be equal to 1, which means that there is only a single data space domain. As these conditions improve (the number of multipaths increases), more spatial domains can be added, in other words, the transmission data rate on the channel can be multiplied.

Currently, there is no single MIMO technology that can support all channel conditions. The eNB (base station) must adapt to the transmission architecture (depending on multipath, signal-to-noise ratio and mobility) many times per second in order to maximize the performance of each specific user terminal. Transmission capacity.

Understand the correlation of antennas

Figure 3 Effects of different natural correlations
An important factor in selecting MIMO technology is the degree of correlation between antennas. Figure 3 describes the two transmission architectures used in the configuration of a 2×2 MIMO LTE channel (with EPA 5Hz) under different antenna-related propagation conditions: space-time block coding (STBC) and space multiplexing.
It can be observed that under low signal-to-noise ratio conditions, STBC can provide excellent results; and under high signal-to-noise ratio conditions, spatial multiplexing provides nearly twice the transmission volume of STBC transmission (with a configuration of 2×2 MIMO In terms of). The number of MIMO levels defines the gain of the transmission volume when the signal-to-noise ratio is high, 3 is the gain of 3×3 MIMO, 4 is the gain of 4×4 MIMO, and so on. The intersection between the two line graphs is the signal-to-noise ratio when the spatial multiplexing starts to exceed the STBC transmission volume.

Spatial multiplexing is more sensitive to the correlation of antennas. In the case of high correlation, a higher signal-to-noise ratio is required to surpass STBC. Therefore, it is very important to choose a spatial multiplexing solution that is not easily affected by antenna correlation.

Choose MIMO receiver carefully

There are many possible implementations of MIMO receivers for user terminal equipment. Among them, the most commonly used are linear receivers, including zero-forced (Zero-Forcing, ZF) and minimum mean squared error (MMSE). Detection method. Another embodiment is a non-linear receiver based on a Maximum Likelihood (ML) detector.

Assume the following mathematical fundamental frequency signal model, which is shown in formula 1:

............................................Formula 1

y is the signal vector sampled on the receiver, the size of the vector corresponds to the number of receiving antennas; s is the symbol vector sent from multiple antennas, the size of the vector corresponds to the number of transmitting antennas (Nt); and H is the channel Impulse response matrix, which describes each channel from the transmitting antenna to the receiving antenna. The dimension of this matrix corresponds to Nr×Nt. Pn is an independent complex number vector, using ρ2 to calculate Gaussian random variables.

The performance of the receiver is evaluated using a tool called "error probability curve". In the graph of the error probability curve, the x-axis is the signal-to-noise ratio of the channel, and the y-axis is the error rate. The measurement unit of the signal-to-noise ratio of the channel is dB, and the error rate is on the logarithmic axis. It can be expressed in the following ways: bit error rate (BET), symbol error rate (SER) or packet error rate (PER).

The packet error rate is calculated by dividing the total number of packets received by the number of data packets received in error. When at least one bit is wrong, the packet is declared incorrect. For coded communication systems, the packet error rate that can be measured also includes a forward error correction (Forward Error Correction, FEC) decoder.

Figure 4 Number of diversity levels
The error probability curve of a MIMO receiver is determined by two main parameters: Diversity Order (DO) and Array Gain (AG). Figure 4 shows the number of diversity levels, while Figure 5 discusses the array gain in terms of BER-the total number of transmitted bits divided by the number of error bits.


Figure 5 Array gain
The number of diversity levels is defined as the slope of the error probability curve under the condition of high signal-to-noise ratio. The larger the DO, the greater the slope of the error probability curve. Therefore, a larger DO is better.
The array gain is defined as the horizontal shift of the error probability curve. For higher AG values, the slope of the error probability curve will shift to the left, towards a lower signal-to-noise ratio. In this case, a higher AG is better.

Table 1 describes the use of spatial multiplexing method to obtain the number of diversity levels of three types of receivers, and the array gain calculation method. Using the formula in Table 1, you can simply calculate a 4×4 MIMO (Nr=4, Nt=4) configuration with spatial multiplexing transmission.
Compared with the linear receiver (Nr-Nt+1), the diversity level of 1 (Nr-Nt+1=4-4+1=1), the ML receiver’s diversity level is 4, which is equal to the number of receiving antennas ( Nr=4); Compared with the linear scheme, the ML receiver has obvious advantages, especially when the signal-to-noise ratio is high.

Compared with the array gain of 1/4 of the linear scheme, the same calculation method makes the array gain produced by the ML receiver 1. The ML receiver once again provides better results.

It can be found through observation that the main advantage of the ML receiver lies in its high signal-to-noise ratio. Under these conditions, its DO and AG parameters are much larger than linear receivers. On the one hand, this means that under the condition of low signal-to-noise ratio, it is sufficient to implement a simpler linear receiver, or it can completely avoid the use of spatial multiplexing technology and choose a more powerful transmission architecture. In other words, when the signal-to-noise ratio is high enough, there is a spatial multiplexing technology with high transmission volume, and the ML receiver is a natural choice.

What this article refers to is the soft-output MIMO scheme, not the hard-output scheme. Instead of generating a clear bit scheme of 1 or 0, the soft output scheme includes a ratio between the probability of a clear bit 1 and the probability of a bit 0. The ratio is approximated by "soft bits" or logarithm. Than LLR (Log Likelihood Ratio) said.

Anatomy of a Turbo MIMO receiver

Figure 6 Turbo MIMO receiver
The above methods are called One Shot, because they complete the processing of the input signal or tone after the detector is activated once. Another method is to provide MAP performance with iterative solutions, including soft symbol detection and external FEC decoders.
The FEC decoder is an independent module in the receiver, responsible for performing "forward error correction". By utilizing the redundancy generated by the transmission signal, the FEC decoder can detect errors in the received bit stream, and can often correct these errors without retransmission.

The operating structure of the Turbo MIMO receiver includes two stages: soft output symbol detection and FEC decoding (Figure 6). In the first iteration, the symbol detector only generates LLR results based on the received input signal; then, according to the coding constraints, the FEC detector will then weaken or enhance the LLR; then, the symbol detector uses the front An LLR result obtained by the FEC decoder is iterated again. The two stages are iteratively exchanged data from one to the other until the receiver converges.

The symbol detector may include an implementation of a soft output ML detector, or another method is to use a simpler forced zero or minimum mean square error detector, followed by soft symbol de-mapping.

By performing this iteration process, the receiver can surpass the accuracy of the ML decoder and obtain a lower error rate. The advantage of this receiver is that very high accuracy results can be obtained, not only exceeding the ML solution, but also close to the maximum a posteriori probability (MAP) result. In addition, if you are willing to perform more iterations between the decoder and the Turbo decoder, the symbol detector can be simplified to a linear solution.

The disadvantage of this receiver is: there is a large amount of data transmission between the FEC decoder and the symbol detection method, which need to be arranged in advance and stored in the intermediate buffer; the number of delays increases due to multiple iterations and conversions; the transmission volume Decline; due to the conversion and iteration of multiple data, additional power consumption is generated.

Implementation of MLD receiver

ML receivers have obvious advantages, but they come at the cost of implementation complexity. The estimator of the maximum similarity receiver solves the result of Equation 2:

..........Formula 2

For the sake of simplicity, a single-input single-output (SISO) single-transmit and single-receive antenna configuration is used as an example for illustration. In this example, y is the signal sampled by the receiver, s is the transmit symbol, and H is the channel pulse. In response, the channel between the transmitting antenna and the receiving antenna is described.

The receiver will look for the transmitted symbol s to minimize the absolute value. s corresponds to a set of finite values ​​defined by symbol modulation. For 64QAM modulation, for example, s can have 64 different values.

Basically, this can be boiled down to an exhaustive search (Exhaustive Search). The receiver must scan all possible s values ​​to find a value that is closest to the received signal when multiplied by the estimated channel H value.

For the SISO system this is very simple, but when transferred to the MIMO system, the complexity will increase exponentially. For example, a 2×2 MIMO configuration with 64QAM modulation, s is a vector with two values, the first antenna can transmit 64 different symbols, and the second antenna can also independently transmit 64 possible symbols one of. A total of 642, or 4,096 s-values ​​must be evaluated.

For a 2×2 MIMO configuration, some algorithms are used to reduce the complexity of the ML receiver. It is worth noting that the LORD algorithm can reduce the complexity of the search, from 642 options to 64×2, that is, 128 evaluation values ​​to achieve ML accuracy.

The number of 64QAM for 4×4 MIMO has now grown to 644, which means that 16,777,216 different values ​​of s must be evaluated. A new method is needed to solve this level of complexity, where the sub-optimal ML receiver is needed to play its strengths.

The sub-optimal ML receiver tries to scan the possible signals in a more efficient manner, thereby reducing the overall complexity and approaching the result of near-ML accuracy; in terms of area and power, reducing complexity helps more The actual hardware implementation, which also allows the hardware to keep up with the high throughput defined by high-level communication standards.

To solve sub-optimal ML equations, a tree search can be defined, in which each layer of the tree corresponds to a transmission symbol, and the number of branches protruding from each node corresponds to the modulation of QAM or transmission symbol. A 4×4 MIMO configuration is represented by a four-layer tree. If it is BPSK modulation, each node will contain two branches.

Figure 7 MIMO symbol tree
Once the tree symbol definition is completed, knowledge in other fields, such as computer science, can be used to deploy Tree Traversal Algorithms (Figure 7).
In this context, sub-optimal ML receivers can be divided into two main types: breadth first search and depth first search.

Breadth first search

The best example of breadth-first search is K-best Algorithm. The decoder is a fixed-complexity solution, starting from the root of the tree and rising until it reaches the last layer of the tree. In each level of the tree, all selected branches will be evaluated, leaving K surviving nodes until the best solution (that is, the symbol closest to the received signal) is matched, hence the name "K-best". The final K is used to generate the LLR result.

The advantages of the decoder include: one-way flow is helpful to the hardware to easily implement the pipeline (Pipelining Implementation); the processing power of each layer is constant, and it is directly related to the number of surviving nodes (K) selected during execution; The transmission volume is constant, and it will conversely simplify the scheduled data flow in the system.

The defects of this decoder are: it needs to be implemented in a large area to evaluate and classify all selected tree layer nodes; the higher the accuracy requirement, the larger the K value; the transmission volume will not increase under the best signal-to-noise ratio; whether it can Obtaining the ML solution is not a guarantee, because the best solution may fall on the unselected node.

Figure 8 K-Best tree tracking
Figure 8 shows a 4×4 MIMO (4-layer) tree and QPSK modulation. K in this example is 4. The 16 nodes will be sorted individually at each level of the tree. The best 4 will be the surviving nodes in the next layer.
Depth first search

The best example of depth-first search is the Soft-Output Sphere Decoder Algorithm. Starting from the root of the tree, it mainly rises directly to the leaves, hence the name "depth first". The first solution of the tree shape determines the initial search radius or sphere. From that moment on, the decoder begins to backtrack and ascend to each level of the tree shape.

Each tree node that exceeds the search radius is trimmed together with all nodes below it. Whenever a better solution appears, the search radius is shortened accordingly. In this way, the symbol tree is scanned and pruned until the number of valid options is reduced, and the last symbol represents the ML scheme.

The advantages of the decoder are: the obtained ML scheme is guaranteed, which promotes the accuracy of the results; under the condition of high signal-to-noise ratio, the decoder executes faster, increases the transmission volume and reduces power consumption; and the equivalent breadth priority search Compared to implement smaller area.

Figure 9 Comparison of fixed complexity and adaptive complexity
Figure 9 shows the comparison of the loop count between the soft output sphere decoder with adaptive complexity and the K-best decoder with fixed complexity. When the signal-to-noise ratio increases, the loop count of the sphere decoding method will decrease, and the fixed complexity will remain unchanged regardless of the channel conditions.
The defect of the decoder is: the non-deterministic behavior of the decoder will complicate the system scheduling; the choice of the next branch can only be known after the completion of the current branch. This makes the implementation of the hardware pipeline very challenging.

Figure 10 Sphere decoder tree tracking
Figure 10 shows an example of a MIMO 4×4 (four-layer) configuration and QPSK modulation. In the following method, the depth-first search selects the symbol path of the first leaf, in order -3 (Level 1), -3 (Level 2), 1 (Level 3), 3 (Level 4), and includes the initial Radius update, backtracking to the second level of symbols; in addition, branches (thick dashed arrows) that exceed the search radius are trimmed during the search process, thereby minimizing the scope of the search tree.

The greatest similarity MIMO detector comes out

CEVA introduced the maximum similarity MIMO detector to respond to the challenge of MIMO receivers. MLD is a Tightly Coupled Extension (TCE) accelerator hardware unit. MLD has a way to process the high-end Cat.7 data stream of LTE and produce a soft output max-log ML solution.

For 4×4 or 3×3 MIMO configuration, at 12.6Mega-tones/sec, use soft output sphere decoder method; or at 2×2 ML scheme based on LORD algorithm; at 28.8Mega-tones/sec When using carrier aggregation (Carrier Aggregation), MLD accelerators can achieve sub-optimal ML solutions. MLD is designed for mobile applications and emphasizes the design concept of low power consumption.

The MLD feature set supports the following items. The first is to configure a variable transmission architecture from 2×2 to 4×4 MIMO. The configurable modulation of each layer is up to 64QAM; it supports tree search optimization (user-defined layer ordering, initial radius of each tree layer) And search radius); CEVA MLD strengthens the non-deterministic nature of soft output sphere decoding, it proposes a complete control function, including the downward and upward cycle count boundaries of pitch processing. In addition, the transmission volume of the system can be maintained through user-defined time stamp termination.

The MLD feature set also supports scalable soft-bits (Soft-bits) to signal-to-noise ratio and modulation factor; with intra-symbol and cross-layer tunability, it provides support for the arrangement of LLRs; cross-layer demapping (supports two The coding layer allows MLD to split the written data into two different destinations); Scalable hardware solutions can achieve the trade-off of performance/power/area, including the number of MLD engines and the size of the buffer And the clock ratio of the interface. In addition, the accelerator also provides extensive debugging and analysis functions.

Figure 11 4×4 MIMO spatial multiplexing performance
Figure 11 depicts the performance of CEVA MLD TCE, compared with the MMSE receiver using 4×4 multiplex MIMO. Under different signal-to-noise ratio conditions, the transmission volume of packet error rate is evaluated. The LTE channel is set at EPA 5Hz, which has low-correlation propagation conditions.

Choose the most suitable solution

MIMO is an important part of the next generation of wireless technology. In order to fully exploit the potential data transmission rate, it is necessary to deploy spatial multiplexing technology. The above shows that the MLD receiver achieves excellent results, but there are still many factors to be considered when choosing to implement MLD. The designer of the MLD receiver must choose the most suitable solution for the required application and take the following considerations:

‧    Accurate target and transfer volume requirements
‧    Delay definition
‧    Channel type fast/slow time variation (Time Variant)
‧    Hardware considerations

In addition, a scalable hardware solution is needed to meet the requirements of small area and low power consumption, and the selection of an optimized MLD receiver for mobile phone products will make a big difference.

Keyword:

Scan the QR code to read on your phone

Follow Us:

Home         Message

About         Contact

News         ENGLISH

Product      Japanese

 

Follow us
qr Chinese
qr English
qr Japanese

Contact us

Shenzhen Xinxie Office
Contact number: 18998036969
(Yang Sheng)
Contact email: benson@szxinxie.cn
Taiwan Jie Shun Office
Contact number: 09-8166-0710
(General Manager-Yang Fengyuan)
Contact email: kent@yong-shun.com.tw

Company website: https://www.rfxinxie.com Company Address: No. 5, Alley 18, Lane 1, Renxing Road, Hukou Township, Hsinchu County

 Copyright © 2019 Xinxie Technology (Shenzhen) Co., Ltd.  粤ICP备20005589号

Username used for comment:
Messages
Description:
验证码