This report opens with datasheet figures to orient the reader: RX current as low as 10.7 mA and TX current up to 85 mA at +20 dBm, with a supply range of 1.8–3.6 V. The intent is to present lab benchmarks, detailed power consumption profiles, and practical recommendations for battery-powered and long‑range IoT deployments using this sub‑GHz transceiver.
The device targets 119–960 MHz operation in a 20‑pin QFN, with TX output from –120 dBm up to +20 dBm and typical RX sensitivity near –126 dBm depending on data rate and modulation. Datasheet current ranges include low‑microamp standby, RX ≈10 mA region, and TX up to tens of mA at peak power. This snapshot helps map RF performance to system KPIs.
Target use cases include battery sensors, smart metering, asset trackers, and remote control systems where link budget, throughput, and battery life are primary KPIs. Expect multi‑kilometer range in line‑of‑sight when configured at +20 dBm with a sensitive RX and efficient antenna; lower data rates improve sensitivity and extend range at the cost of throughput.
Benchmarks were captured across 433, 868 and 915 MHz using FSK and OOK at data rates from 1 kbps to 1 Mbps. TX power steps measured: –10, 0, +10, +20 dBm. Packets were 16–256 bytes with controlled preamble and CRC. Environmental conditions were room temperature and a tested antenna with known gain; firmware exercised full state transitions (TX, RX, PLL, sleep).
Measurements logged packet error rate (PER), RSSI, and latency across data rates. Control firmware toggled fast PLL lock and baseline sleep; RX-on windows and TX bursts used to compute per‑packet energy. Test runs were repeated for statistics at each frequency/modulation point to produce sensitivity vs data‑rate curves and PER vs RSSI mappings.
Power was measured with a high‑resolution DC meter for average currents, a current probe and oscilloscope for transient capture, and a spectrum analyzer for TX spectral shape. Sampling used ≥100 kS/s for transitions; micro‑amp sleep currents measured with SMU averaging and long integration. Deliverables: CSV time traces, per‑mode averages, and energy‑per‑packet values with stated uncertainties.
Measured sensitivity tracks expected behavior: lower data rates (1–10 kbps) approach the –120 to –126 dBm region, while higher rates (100 kbps–1 Mbps) lose several dB. PER vs RSSI curves show rapid PER degradation within 3–6 dB of sensitivity limits. Throughput and latency scale predictably with data rate and retransmit strategy; link budget calculations translate sensitivity and TX power into practical range estimates.
Measured RX current clustered near the datasheet low‑mA figure; peaks in TX matched tens of mA at mid power and ≈85 mA at +20 dBm. Example energy calculation: a TX burst at +20 dBm for 50 ms at 85 mA and Vcc=3.3 V consumes E_tx ≈ 3.3V×0.085A×0.05s ≈ 0.014 Wh (≈50 mJ). Using simple duty‑cycle averaging, a 2000 mAh AA (≈2 Ah at 1.5V cell equivalence scaled to system V) yields multi‑month life for hourly reports; formulas and CSV traces were used to project battery life for representative cycles with stated measurement uncertainty.
Fair comparisons require identical PA settings, same antenna and measurement method. In a side‑by‑side matrix, sensitivity, max TX power, and RX/TX/standby currents form the core axes. Relative strengths: high max TX power and solid sensitivity favor long‑range link budgets; some peers trade peak power for lower standby currents, so selection depends on duty cycle and battery constraints.
Use case A — hourly sensor: transmit 100‑byte packet at +10 dBm using 50 ms TX and 100 ms RX for ACKs; average current ≈ (TX_energy+RX_energy)/period yields years of life on a 2000 mAh cell. Use case B — asset tracker burst: frequent short bursts at +20 dBm for location uplinks increase average current dramatically and may require larger cells or optimized duty cycles and data aggregation to meet battery life targets.
Minimize RX-on time, use short preambles with fast PLL lock, coalesce sensor data to reduce packet count, and enable lowest‑power standby between events. Tune data rate and modulation to balance sensitivity and throughput. Implement adaptive retransmit thresholds and aggressive sleep strategies to reduce average power consumption.
Design the power supply with low‑noise LDOs and proper decoupling; include measurement access points for debugging. Optimize antenna matching and keep RF traces short with solid ground return. For sustained high TX power, consider thermal management and validate power regression across temperature as part of QA.
This review presents lab benchmarks and concrete power profiles for the SI4464-B1B-FMR, mapping measured RX current, TX current, and energy‑per‑packet into system battery‑life projections and practical optimization levers for firmware and hardware. Use these results to select operating points that balance range, throughput, and battery life for your application.
TX current scales roughly with output power: tens of mA at mid levels and up to ~85 mA at +20 dBm in our bench captures. Energy per packet depends on burst duration; reducing TX time or lowering output by a few dB often yields substantial energy savings while only moderately impacting range.
Use a high‑resolution DC meter or SMU for average currents, plus a current probe and fast oscilloscope to capture transients and peaks. Long integration and averaging help detect μA‑class sleep currents; always report Vcc, temperature, antenna configuration, and sample size to bound uncertainty.
Compute energy per event (E = Vcc×I×t) for TX and RX phases, sum with sleep energy per period, and divide battery capacity (Wh or mAh adjusted to system V) by average power to get lifetime. Include margins for self‑discharge, converter inefficiency, and temperature to produce conservative estimates.