Joint Communication and Parameter Estimation in MIMO Channels
This work addresses the challenge of integrating communication and sensing tasks in multi-antenna systems, which is incremental as it builds on existing frameworks to derive specific trade-offs and optimal schemes.
The paper tackles the problem of joint communication and parameter estimation in MIMO channels, establishing a fundamental trade-off between communication capacity and sensing mean squared error (MSE) and identifying optimal coding schemes for this setting.
We study a joint communication and sensing setting comprising a transmitter, a receiver, and a sensor, all equipped with multiple antennas. The transmitter sends an encoded signal over the channel with the dual purpose of communicating an information message to the receiver, and enabling the sensor to estimate a target parameter vector by generating back-scattered signals. We assume that the transmitter and sensor are co-located, or fully connected, giving the latter access to the transmitted signal. The target parameter vector is randomly drawn from a continuous distribution, yet remains fixed throughout the transmission block. We establish the fundamental performance trade-off between the communication and sensing tasks, captured in terms of a capacity-MSE function. In doing so, we identify optimal coding schemes for this multi-antenna joint communication and sensing setting. Moreover, we particularize our result to two practically-inspired scenarios where we showcase optimal schemes and trade-offs.