Massive MU-MIMO Power Allocation Under Quantization Distortion
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Solution Overview
Problem
Massive MU-MIMO systems face challenges with near-far effects due to the use of low resolution data converters, which cause quantization distortion, impairing signals from users with low received power amidst those with high received power, especially in varying power conditions.
Innovation Solution
A method for a wireless communication device that estimates communication channels and quantization distortion to jointly determine optimal transmission power and resources for each user, optimizing performance metrics such as worst-case or overall throughput, thereby mitigating near-far problems and accommodating users with different power conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If low resolution data converters are used to reduce power consumption, then power consumption is reduced, but quantization distortion increases and impairs signals from users with low received power
Solution Approach 1:
The patent changes the parameter of quantization resolution dynamically based on user conditions. Low resolution quantization is applied to users with high received power (strong users) to reduce power consumption, while high resolution quantization is applied to users with low received power (weak users) to maintain signal detection accuracy. This parameter adaptation resolves the contradiction between power consumption and signal reliability.
Solution Approach 2:
The patent applies different quantization resolutions to different users based on their local conditions (received power levels). Instead of using uniform quantization resolution for all users, the system tailors the quantization quality locally - using low resolution for strong users and high resolution for weak users. This local quality differentiation allows the system to reduce overall power consumption while maintaining reliability for critical weak user signals.
2Device complexity
If low resolution data converters are used, then device complexity is reduced, but near-far effects cause strong user signals to drown weak user signals in quantization distortion
Solution Approach 1:
The patent dynamically changes the quantization resolution parameter based on user received power levels and channel conditions. For strong users experiencing high attenuation compensation, low resolution quantization suffices, reducing device complexity. For weak users, high resolution quantization preserves signal information. This adaptive parameter adjustment resolves the contradiction between complexity reduction and information loss prevention.
Solution Approach 2:
The patent applies different quantization qualities locally to different users based on their specific channel conditions and received power levels. Strong users receive low resolution quantization while weak users receive high resolution quantization. This local differentiation ensures that device complexity is reduced where possible while signal information is preserved where critical.
3Reliability
If power control directs more power to high attenuation UEs, then coverage is improved, but quantization distortion from low resolution converters significantly impairs signals to low attenuation UEs
Solution Approach 1:
The patent changes the quantization resolution parameter adaptively based on the power control strategy and user conditions. When high power is allocated to high attenuation UEs, the system applies high resolution quantization to these users to prevent signal distortion. For low attenuation UEs receiving lower power, low resolution quantization is sufficient. This dynamic parameter adjustment resolves the contradiction between coverage reliability and signal information preservation.
Solution Approach 2:
The patent applies different quantization qualities locally to different users based on their attenuation levels and allocated power. High attenuation users receiving high power transmission get high resolution quantization to prevent distortion, while low attenuation users get low resolution quantization. This local quality adaptation ensures coverage reliability is maintained for edge users while avoiding unnecessary information loss for users with good channel conditions.
Data Source
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AI summary
Disclosed is a method of a first wireless communication device configured for massive multi-user multiple-input multiple output (MU-MIMO) communication with two or more second wireless communication devices. The first wireless communication device comprises a plurality of antenna ports, each antenna port associated with at least one of a digital-to-analog converter (DAC) and an analog-to-digital converter (ADC). The method comprises acquiring an estimation of a communication channel between the first wireless communication device and the second wireless communication devices and acquiring an estimation of a quantization distortion caused by either DACs or ADCs. The method also comprises jointly determining (for the two or more second wireless communication devices) a transmission power and a transmission resource for each of the second wireless communication devices, wherein the joint determination is based on the estimation of the communication channel and on the estimation of the quantization distortion. Corresponding apparatus, network node and computer program product are also disclosed.