In the actual physical layer communication process, do we tend to pay more attention to BER or SER?

In the engineering of physical layer communication systems, the choice between prioritizing Bit Error Rate (BER) or Symbol Error Rate (SER) is dictated by the specific modulation scheme, the system's architectural constraints, and the ultimate performance metric for the data being transported. For straightforward binary modulation schemes like BPSK, where one bit directly maps to one symbol, BER and SER are mathematically identical, making the distinction irrelevant for analysis. However, the practical focus almost invariably shifts to BER as the primary figure of merit for a fundamental reason: digital communication systems are ultimately designed to deliver bits correctly to higher layers. BER provides a direct, intuitive measure of the raw link quality as experienced by the data payload, making it the universal currency for comparing disparate systems and for setting requirements that directly translate to application-layer performance, such as throughput and latency.

The analytical and practical divergence between BER and SER becomes critical and informative when employing higher-order modulation (HOM) schemes, such as QPSK, 16-QAM, or 64-QAM. Here, each symbol represents multiple bits, and a single symbol error can corrupt several bits. In these scenarios, SER is often the more natural starting point for theoretical analysis because the probability of a symbol decision error can be derived directly from the signal constellation geometry and the noise statistics. Engineers calculate SER first based on the received signal-to-noise ratio and the minimum distance between constellation points. Subsequently, an accurate BER is derived from the SER by accounting for the bit-to-symbol mapping and the likelihood that a given symbol error causes one, two, or more bit errors. This relationship means that while SER is a crucial intermediate analytical step, BER remains the definitive end metric because it reflects the actual bit corruption rate.

System design and optimization efforts tend to be BER-centric because BER has a direct line of sight to higher-layer protocols and user experience. Forward Error Correction (FEC) codes are specified and evaluated based on their ability to reduce BER to a target level (e.g., 10^-12 after correction) under given channel conditions. Link budget calculations, which determine the feasibility of a communication link, ultimately ensure the received energy per bit is sufficient to achieve an acceptable BER. Furthermore, adaptive modulation and coding algorithms, which dynamically switch modulation orders based on channel quality, use BER estimates or its proxies (like Signal-to-Noise Ratio) as their key switching criterion to maximize spectral efficiency while maintaining a target BER. SER, while analytically foundational, often remains an internal variable in these processes.

Therefore, in the actual physical layer communication process, we tend to pay more attention to BER. It is the indispensable system-level performance indicator that bridges the analog impairments of the channel to the digital integrity of the data stream. SER is an essential analytical tool, particularly for designing and understanding the behavior of complex modulation schemes, but it serves as a means to the end of predicting and controlling the BER. The engineering workflow consistently funnels analysis—whether theoretical, simulation-based, or measured—toward a BER specification, as this metric most directly correlates with the system's core function of reliable bit delivery.