spectrum usage by wireless communications has grown even further with
the advent of fifth-generation (5G) wireless technology, even heavier
demands are being placed on radar systems to proactively share spectrum
with communications in real time. Adaptive matching of the transmitter
power amplifier is important to maintain high range and power efficiency
through changes in operating frequency and array scan angle . High-power tuning technologies have been recently developed using mechanical tuning of resonant-cavity discs .
While reconfiguring mechanically tuned devices can require time frames
much longer than a typical radar pulse repetition interval (PRI) or
coherent processing interval (CPI), new high-power electrical switch
technologies are under development .
These switches can be used to create switched-stub impedance tuners,
permitting tuning of the amplifier around the Smith Chart by changing
the tuning stubs that are exposed to the main feedline through
switching. The fast optimization of a switched-stub tuner in less than
In the present paper, we introduce dynamic learning to reduce the number of measurement iterations for reoptimization of power-amplifier load impedance as the search progresses.
order to demonstrate the flexibility of the switched-stub tuner in
changing operating conditions, experiments were performed in a scenario
where both system frequency and reflection coefficient presented to the
tuner vary. The change in frequency alters the maximum load impedance,
which, when presented to the amplifier, leads to maximum power output.
The frequencies used in this experiment, like in ,
are 2, 2.5,3,3.5, and 4GHz, covering an octave. The change in
reflection coefficient presented by the antenna to the tuner represents a
system where an antenna array is electrically scanned to another angle,
resulting in changes in mutual coupling and apparent antenna reflection
coefficient. The “antenna” reflection coefficients used in this
Previously in , switched-stub tuner optimization searches were performed at various operating conditions, and the system had no knowledge of these operating conditions to aid the search. In that case, each search began at the starting point with all switches open (no stubs exposed in the matching network). The searches would complete in the order of tens of microseconds, but there is room for improvement: dynamic online learning for starting point optimization. Five frequencies and five reflection coefficients provide 25 unique operating conditions for the system, and all switches being left open to start is not optimal at many of these conditions.
In this experiment, the search demonstrated in  was used for real-time optimization of the switched-stub impedance tuner, also described and demonstrated in . Searches were performed in a random order, dynamically populating the system memory with the switched-stub tuner state at which each search converged. To decrease search time, this state is used as the starting point the next time that the same operating frequency and antenna reflection coefficient (corresponding to array scan angle) are used. The hope is that, unlike in  where a continuous, mechanical impedance tuner is used, the previous optimal switched-stub state is also very likely to be the optimum for the next search at the same operating conditions, as the discrete impedance tuner demonstrated in  can only tune to 64 different states. Variations in the optimum would likely be due to different mutual coupling scenarios potentially arising from different operating environments if the platform is moving. If a system like this were to be deployed in a real-life scenario, the antenna reflection coefficients would likely not be known. However, storing the scan angle can provide an equivalent capability to storing the antenna reflection coefficient.
To test this approach experimentally, optimization searches were conducted using an Ettus X310 software-defined radio (SDR) in conjunction with a host computer. The SDR acts as the (calibrated) transmitter, power meter, and tuner switch controller in the system. Additionally, the search algorithm is stored on the SDR's FPGA for speed purposes. The host computer is used to initiate the searches, as well as to dynamically learn and recall the optimal starting points for each system operating condition. Fig. 2 shows the measurement bench.
As seen in Fig. 2, the switched-stub impedance tuner (D) is between the MWT-173 field-effect transistor (FET) (C, which serves as the device under testing (DUT)) and the Maury Microwave mechanical impedance tuner (A, which simulates the antenna reflection coefficients). The DC power supplies are used both to bias the FET and provide power to the six switches on the tuner.
The results for one of the 25 optimization searches using the search of  without system memory is shown in Table I, below. The searched was performed with a 3.5 GHz operating frequency and an emulated antenna reflection coefficient of
Highlighted in Table I is state 60, where the search converged. With dynamic learning, the system remembers this state, and the point is used as the starting point when the same system operating frequency and antenna reflection coefficient are used again.
In contrast with the Table I standard search of , the results for a search that utilizes dynamic learning for the same operating conditions are shown in Table II. The search converged to state 60 once again. This time, however, state 60 was the starting point, so the search only performed 7 measurements instead of 14, based on the search procedure of . While the improvement from utilizing dynamic learning varies across operating frequency and antenna reflection coefficient, this example demonstrates the impact of utilizing results from previous searches with the stub tuner.
To compare broader results of using dynamic learning, 25
searches without dynamic learning and 25 searches with dynamic learning
were performed, and the search results were compared. A summary of
notable results from the experiment are shown in Table III.
Comparing the two sets of data, the most noteworthy difference is the
decrease in measurement number and time taken for the searches with
dynamic learning. By using dynamic learning, the average search time
Plans for Future Development
As noted previously, the test setup used for these experiments uses the host computer to store the system memory. In a deployed system, the system memory would need to be stored either on the FPGA itself or on a more easily deployable small-board computer, and the communication time required to retrieve the starting tuner state will need to be considered. The dynamic learning approach should be integrated into the deployable system and re-evaluated for time savings, based on the communication overhead.
Experiments have demonstrated a time savings from using dynamic learning to select an impedance tuner starting state for real-time tuning when changing operating frequency or antenna impedance, which is related to array scan angle. This has significant applications to real-time radar transmitter frequency agility, allowing maximum transmission range to be maintained in a spectrum-sharing scenario. Measurement results show that using the previous search optimum as the starting location for optimizing the switched-stub tuner can decrease the search time by up to 25 percent. As transmitters change frequency and scan angle strive to avoid interference and maintain compatibility, improved speed in optimizing the impedance tuner for optimal range can be achieved. For example, an impedance tuning optimization search may, in many cases, be performed within the timeframe of one PRI or CPI. This allows radar transmitters to quickly optimize range at new operating conditions while avoiding spectral or spatial interference with or from other wireless devices.
This material is based upon work supported by the Office of Naval Research under Award No. N00014-19-1-2549. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Office of Naval Research.