This　paper　presents　the　empirical　likelihood　inferences　for　a　class　of　varying-coefficient　models　with　error-prone　covariates.　We　focus　on　the　case　that　the　covariance　matrix　of　the　measurement　errors　is　unknown　and　neither　repeated　measurements　nor　validation　data　are　available.　We　propose　an　instrumental　variable-based　empirical　likelihood　inference　method　and　show　that　the　proposed　empirical　log-likelihood　ratio　is　asymptotically　chi-squared.　Then,　the　confidence　intervals　for　the　varying-coefficient　functions　are　constructed.　Some　simulation　studies　and　a　real　data　application　are　used　to　assess　the　finite　sample　performance　of　the　proposed　empirical　likelihood　procedure.