Coefficient Sharing DPD

Antenna branches are grouped into clusters based on similarity of nonlinear characteristics. A single DPD coefficient set is computed for each cluster rather than per-branch.

• Clustering criteria: PA operating point, bias conditions, thermal profile, and mutual coupling environment
• Typical cluster size: 4–16 elements per shared DPD engine
• Trade-off: Cluster size vs. linearization accuracy—larger clusters reduce complexity but may undercompensate outlier branches

The computationally intensive basis function generation—constructing nonlinear terms from the input signal—is performed once per cluster and reused across all member branches.

• Eliminates redundant polynomial term calculation per antenna element
• Reduces multiply-accumulate operations by up to 80% compared to per-branch DPD
• Particularly effective for generalized memory polynomial and Volterra-based predistorters where basis function count dominates complexity

A centralized DPD adaptation engine computes coefficients and broadcasts them to all branches within a cluster. Each branch applies the shared coefficients to its own signal path independently.

• Centralized estimation: Single coefficient solver serves multiple PAs
• Distributed application: Each transmit chain applies predistortion locally
• Enables single-feedback receiver architectures where one observation path sequentially samples cluster members for adaptation

The antenna array is partitioned into sub-arrays where coefficient sharing is applied. Partitioning strategies directly impact linearization performance.

• Uniform partitioning: Equal-sized sub-arrays based on physical adjacency
• Behavior-driven partitioning: Groups formed by measured PA nonlinearity similarity
• Dynamic re-partitioning: Clusters updated as beamforming weights change and active impedance conditions shift
• Hybrid approach: Static sub-arrays with per-branch fine-tuning offsets

Coefficient sharing introduces a linearization penalty that must be balanced against resource savings. The degradation depends on nonlinearity variance within each cluster.

• ACLR degradation: Typically 1–3 dB when sharing across 8-element clusters with well-matched PAs
• EVM impact: Minimal (<0.5%) for branches with correlated nonlinear behavior
• Mitigation techniques: Per-branch phase/amplitude offset correction, cluster-specific memory depth tuning
• Break-even analysis: Sharing becomes advantageous when FPGA DSP slice savings exceed the cost of residual distortion compensation

Coefficient sharing must account for beamforming-dependent nonlinearity changes. As beamforming weights alter the active impedance seen by each PA, cluster membership may need reconfiguration.

• Beam-aware clustering: Clusters defined per beam index or steering angle range
• Look-up table approach: Pre-computed shared coefficient sets for discrete beam configurations
• Interpolation between clusters: Smooth transition of shared coefficients as beams steer continuously
• Compatible with hybrid beamforming architectures where digital and analog domains have separate sharing strategies

A complexity-reduction method where a single DPD engine linearizes a cluster of antenna elements sharing similar nonlinear characteristics. This is the architectural foundation that enables coefficient sharing.

• Groups 4-16 adjacent elements into a sub-array
• Reduces DPD engines from 64+ to just 4-8 per panel
• Assumes high spatial correlation within each cluster
• Works best with tightly spaced patch arrays

The variation in impedance seen by an individual power amplifier due to beam steering, causing the amplifier's nonlinear behavior to change dynamically. Coefficient sharing relies on grouping PAs experiencing similar impedance conditions.

• Impedance varies with beam angle and frequency
• Causes load-dependent AM/AM and AM/PM distortion
• Adjacent elements often see correlated impedance shifts
• Critical factor in determining sharing group boundaries

A signal processing method to mitigate unintended electromagnetic interaction between adjacent antenna elements. When coefficient sharing is applied, residual crosstalk between branches must be addressed separately.

• Models S-parameter coupling network between elements
• Crosstalk creates spatially correlated distortion products
• Can be integrated with shared DPD as a joint optimization
• Often implemented as a pre-distortion coupling matrix

A dimensionality reduction technique that identifies and compensates for the dominant spatial modes of nonlinear distortion across the array. This is a mathematical formalization of the coefficient sharing concept.

• Applies PCA to the array's distortion covariance matrix
• Retains only the top K principal components
• Each component corresponds to a shared DPD basis
• Provides theoretical bounds on sharing performance

A predistortion technique that accounts for dynamic changes in PA nonlinearity caused by varying beamforming weights. Coefficient sharing groups must be reconfigured or validated across the beamforming codebook.

• Nonlinear behavior changes with precoding vector
• Sharing groups may need beam-dependent adaptation
• Combines spatial signal processing with linearization
• Essential for 5G NR hybrid beamforming systems

A complexity-reduction technique that selects only the most significant basis functions from a large candidate set. Complements coefficient sharing by further reducing the per-branch computational load.

• Uses LASSO or OMP for basis selection
• Typically retains 20-30% of full Volterra terms
• Combined with sharing for compounded complexity savings
• Enables real-time implementation on mid-range FPGAs