Adaptive Modulation and Coding (AMC) in 5G
- Ravi Shekhar
- Sep 11, 2024
- 5 min read
Adaptive Modulation and Coding (AMC) is a fundamental technology that enables efficient and reliable data transmission in modern wireless communication systems, such as 5G.
By dynamically adjusting the modulation scheme and coding rate based on the channel conditions, AMC maximizes the data throughput and ensures robustness against varying interference levels. This article details into the intricacies of AMC, exploring its mathematical foundations, practical implementations, and performance benefits in 5G networks.
AMC optimizes the use of available spectrum and power resources by adapting the modulation scheme and coding rate to the instantaneous channel conditions. This adaptive approach contrasts with static schemes, where fixed modulation and coding settings are used regardless of the channel quality. The dynamic nature of AMC is particularly beneficial in mobile environments where channel conditions can change rapidly due to factors like user mobility, interference, and obstacles.
AMC is not a new concept; it has evolved over decades as wireless communication systems have advanced. Early implementations of adaptive techniques can be traced back to military communications, where reliability was paramount. The commercial adoption of AMC began with the advent of digital cellular networks in the 1990s, evolving through various generations of mobile technology.
Idea of switching modulation according to SNR is called Adaptive Modulation
We consider an AWGN channel 𝑦=𝑥+𝑛
SNR = Signal power / noise power
Capacity of above channel is
Capacity of 3 bps/Hz is achieved at SNR = 9 dB
Capacity of 5 bps/Hz is achieved at SNR = 15 dB
we'll take the following points:
SNR of -10 dB, 0 dB, 3 dB, 6 dB, 9 dB, 12 dB, 15 dB, and 20 dB.
We will calculate the capacity using the formula
We'll also mark specific points, such as:
A capacity of 3 bps/Hz achieved at an SNR of 9 dB.
A capacity of 5 bps/Hz achieved at an SNR of 15 dB.
This graph illustrates the relationship between SNR (in dB) and capacity (in bps/Hz) with specific points marked for capacities of 3 bps/Hz and 5 bps/Hz at SNR values of 9 dB and 15 dB, respectively.
Evolution Through Mobile Generations
2G (GSM): Introduced basic adaptive coding schemes.
3G (UMTS): Enhanced AMC with more sophisticated modulation and coding.
4G (LTE): Implemented advanced AMC algorithms, leveraging OFDMA for flexible resource allocation.
5G (NR): Further refines AMC, incorporating massive MIMO and beamforming to dynamically adjust to channel variations.
Practical Implementation of AMC in 5G
In 5G, various modulation schemes are used, such as Quadrature Amplitude Modulation (QAM). The order of QAM (denoted by M) determines the number of symbols and the bit rate:
4-QAM (QPSK)
16-QAM
64-QAM
256-QAM
Higher-order QAM schemes transmit more bits per symbol but require higher SNR to maintain reliable communication.
Here's the graph representing Adaptive Modulation and Coding (AMC) based on the given SNR values and their corresponding capacities.
To elaborate on the graph depicting the relationship between SNR (Signal-to-Noise Ratio) and capacity (bits per second per Hertz, bps/Hz) in the context of Adaptive Modulation and Coding (AMC), we can include more details and multiple modulation schemes. This would illustrate how different SNR values influence the capacity and the choice of modulation schemes.
4-QAM:
SNR = 5 dB, Capacity = 2 bps/Hz
16-QAM:
SNR = 12 dB, Capacity = 4 bps/Hz
64-QAM:
SNR = 18 dB, Capacity = 6 bps/Hz
256-QAM:
SNR = 24 dB, Capacity = 8 bps/Hz
Graph now illustrates how different SNR values correspond to different capacities and modulation schemes in Adaptive Modulation and Coding (AMC). Here's a detailed explanation:
1. 4-QAM
SNR = 5 dB: At this SNR, a capacity of 2 bps/Hz is achieved. This modulation scheme is suitable for lower SNRs due to its robustness against noise.
2. 16-QAM:
SNR = 12 dB: With an increase in SNR to 12 dB, a more complex modulation scheme like 16-QAM can be used, achieving a capacity of 4 bps/Hz. This scheme provides a higher data rate than 4-QAM but requires a better signal quality.
3. 64-QAM:
SNR = 18 dB: At an SNR of 18 dB, 64-QAM can be employed, offering a capacity of 6 bps/Hz. This modulation scheme provides even higher data rates but needs an even better SNR.
4. 256-QAM:
SNR = 24 dB: For very high SNRs (24 dB), 256-QAM can be used, achieving a capacity of 8 bps/Hz. This modulation scheme is highly efficient but requires excellent signal quality.
Adaptive Modulation and Coding (AMC): This process involves dynamically adjusting the modulation scheme based on the current SNR to optimize the data rate and maintain reliable communication.
Trade-off: Higher-order modulation schemes (like 64-QAM and 256-QAM) provide higher data rates but require better signal quality (higher SNR).
Flexibility: AMC provides flexibility in communication systems, allowing them to adapt to varying channel conditions to maintain optimal performance.
Coding Techniques
Forward Error Correction (FEC) codes, such as Low-Density Parity-Check (LDPC) codes, are used to enhance reliability by adding redundant bits to the transmitted data.
The coding rate (r) is defined as:
k: Number of original data bits.
n: Total number of bits after encoding, including original data and redundant bits.
The coding rate is always less than or equal to 1, and a lower rate implies more redundancy and higher error correction capability.
Encoder adds parity bits to input message bits to reduce block error rate (BLER).
Encoder should use large code-block lengths to guarantee a low BLER.
SNR uses capacity achieving Low Density Parity Check (LDPC) Codes
One popular type of FEC code is the Low-Density Parity-Check (LDPC) code. LDPC codes are used extensively in modern communication systems, including wireless and optical networks, because of their near-optimal performance and efficient decoding algorithms.
Capacity Achieving - provide low BLER with reasonable code block length at reasonable SNR offset from the capacity curve.
We'll consider a scenario where we use different modulation schemes like BPSK, QPSK, 16QAM, and 256QAM and see how the LDPC codes can achieve lower error probabilities at different SNRs.
For this example, let's consider the following:
We are using LDPC codes with a code block length of 𝑁=3000
We'll plot the performance (BLER) for BPSK, QPSK, 16QAM, and 256QAM as functions of SNR ranging from -10 dB to 20 dB.
We'll assume that the target BLER is
The idea is to showcase how well LDPC codes can perform in achieving near-capacity rates at various SNRs with different modulation schemes, keeping the error rates low.
Description of the Graph:
SNR (Signal-to-Noise Ratio) is plotted on the horizontal axis ranging from -10 dB to 20 dB.
BLER (Block Error Rate) is plotted on the vertical axis on a logarithmic scale, indicating the probability of block errors.
As the complexity of the modulation increases (from BPSK to 256QAM), the required SNR to achieve a similar BLER also increases.
LDPC codes effectively help in approaching the Shannon limit, particularly noticeable at higher SNRs where the performance of these codes nears theoretical limits.
This graph is illustrative of how LDPC codes can be employed in different communication systems to optimize the trade-off between data rate and reliability, depending on the modulation scheme and the operational SNR environment.
Comments