Understanding Society User Support

I am conducting a (longitudinal) diff-in-diff analysis for a policy evaluation where the date of policy introduction is important. I have a few questions below:

1) As my date of interest falls in the middle of a single wave, I could split up wave X into two parts indicating the before and after. Is this enough so that I can only use a single wave for the analysis, OR would you say it is preferable that I also use more waves to more accurately represent the year for the before and after treatment? ( the reason i am asking is because i read the following on your website: "As some samples are fielded in the first 12 months (BHPS and General Population-Northern Ireland samples), some in months 13-24 (IEMB sample) and some across all 24 months (General Population-Great Britain and EMB samples), just using data from the same wave to compare the two consecutive years will result in comparing different samples. Similarly, just using data from year 1 or year 2 of a wave to conduct cross-sectional analyses of that year will result in analysing samples that are not-representative. So, to correctly do these types of analyses, data from two waves need to be combined. For example, for 2019, use data from year 2 of Wave 10 and year 1 of Wave 11."

2) To split up any given wave into two separate waves etc, which variable would you recommend? I have seen many variables in the dataset indicating the month of interview, year, etc but there are also others relating to the sample, but I am unsure which variable would be the most accurate? Moreover, I am confused as some waves suggest they may extend across three calendar years but when I look at the year of interview variable, it only reflects year 1 and year 2, there is no mention of year 3.

3) Which weights would you recommend using in this case?

Many thanks in advance for any help you can provide.

Best,
Marina