Resource Allocation Suite#

The suite also includes a separate joint ISAC OFDM allocation demonstration. That synthetic protocol exposes communication and sensing gains together, enforces one exact sum-power budget, and compares equal power, communication-only water filling, a scalarized per-scene optimizer, and a portable learned policy. It does not reuse the communication-only pack or present the scalarized objective as a waveform-level sensing metric.

This experimental suite compares subcarrier power-allocation policies by configuring the canonical communication pipeline rather than constructing a separate resource-allocation graph. Both recipes use the same seeded random-bit source, canonical payload and TX bit boundaries, channel-code stage, QPSK modulator, Sionna 3GPP TDL-A OFDM realization, wireless.channel, demodulator, channel decoder, BER/ BLER accounting, and resource-allocation metrics. Only the policy on model.symbol_power_allocator changes.

Benchmark Pack#

  • benchmarks/resource_allocation/power_allocation_v1.yaml

The pack compares:

  • recipes/resource_equal_power_baseline.yaml with policy: fixed;

  • recipes/resource_water_filling_baseline.yaml with policy: water_filling.

wireless.ofdm_channel_state is the explicit CSI boundary added to the main pipeline. It generates a reproducible Sionna TDL frequency response sized to the actual modulated bit payload. Both the allocator and wireless.channel have a visible channel_state edge from that artifact, so the oracle label, applied fading, receiver metrics, and training capture refer to the same channel sample. This is the perfect instantaneous CSIT/CSIR assumption used by the theoretical oracle.

Each allocatable channel is one OFDM subcarrier for one OFDM-symbol channel state, not an antenna and not a user-data symbol. Noise variance is fixed at 0.2. The template matrix evaluates normalized average transmit-power budgets 0.5, 1, and 2; within every matrix point all policies receive the same fixed budget. Thus the allocator redistributes power across subcarriers without changing a point’s mean TX power or sweeping the noise floor. An SNR parameter is inactive in this fixed-variance mode and cannot be bound to a recipe or capture sweep.

The oracle uses p_k = max(mu - noise_variance / gain_k, 0), choosing mu so the powers sum to fft_size * average_tx_power. The noise_variance / gain_k term is the inverse-unit-SNR floor. Higher-gain subcarriers generally receive more power, while sufficiently weak subcarriers receive zero; water-filling is not allocation proportional to inverse SNR.

Training Boundary#

The training capture contains only channel_state.state, with the realized Sionna frequency-response gains. Noise variance and the instantaneous average-power budget are runtime conditions. Oracle power is deliberately not a training tap: the OFDM demonstration trainer minimizes negative Shannon spectral efficiency and satisfies nonnegativity and the exact sum-power constraint through a differentiable simplex projection. A researcher may replace the demonstration’s model, objective, and trainer while keeping the same data, constraint, and artifact-return contracts.

Water filling is introduced only after checkpoint selection, on held-out channel seeds. A learned allocator replaces the policy at tx_power while the source, CSI generator, noise variance, channel, receiver, metrics, and seeds stay fixed. This makes learned-versus-oracle comparisons sample-aligned without turning training into imitation of the oracle.

Metrics and Dashboard#

Core evidence includes the theoretical parallel-Gaussian-channel sum objective per OFDM symbol, theoretical full-band Shannon spectral efficiency, maximum power-budget error, active resource-element fraction, TX power, coded and post-decoder BER, and image reconstruction metrics. The theoretical spectral efficiency is mean_k log2(1 + |h[k]|² p[k] / noise_variance); it is not achieved QPSK payload throughput. In the Communication tab, canonical resource-allocation runs show both the ordinary link figures and a selectable per-state plot with unit-power subcarrier SNR and allocated power. Water filling is the Shannon sum-rate oracle; without adaptive bit loading, it is not expected to minimize fixed-QPSK BER or image-decoder outages.

Delayed-CSI finite-blocklength demonstration#

The separate Reliability-aware OFDM allocation with delayed CSI template keeps the same resource-allocation purpose but changes the research question. It gives the allocator four causal complex CSI snapshots from a non-normalized wideband Sionna TDL-C trajectory and withholds the channel state five OFDM symbols later. Training and evaluation score finite-blocklength expected goodput on that later state. In this setting, water filling on the newest delayed snapshot is a mismatched baseline rather than an oracle. See the step-by-step demonstration for the training workflow, interactive paired-seed result figures, and immutable benchmark provenance.

Agentic supervisory demonstration#

The agentic supervisory allocation tutorial wraps the delayed-CSI system at a slower, between-run boundary. A provider selects one of the existing causal allocation policies for the next complete run; it does not generate symbol-level power values. The tutorial defines timestamped allowlisted observations, bounded actions, a deterministic scripted backend, model-provider alternatives, a rule-based supervisor, latency and failure accounting, and deterministic fallback behavior. It is an MX-AI-inspired tutorial specification, not a reproduction of that paper or a validated agent-performance benchmark.