▁seen ▁to ▁be ▁an ▁upper ▁bound ▁on ▁the ▁channel ▁capacity . ▁ ▁Suppose ▁there ▁is ▁an ▁om nis cient ▁" gen ie " ▁that ▁tells ▁the ▁source ▁whenever ▁a ▁transm itted ▁bit ▁gets ▁er ased . ▁There ▁is ▁nothing ▁the ▁source ▁can ▁do ▁to ▁avoid ▁er asure , ▁but ▁it ▁can ▁fix ▁them ▁when ▁they ▁happen . ▁For ▁example , ▁the ▁source ▁could ▁repeatedly ▁transmit ▁a ▁bit ▁until ▁it ▁gets ▁through . ▁There ▁is ▁no ▁need ▁for ▁ ▁to ▁code , ▁as ▁ ▁will ▁simply ▁ignore ▁er asures , ▁knowing ▁that ▁the ▁next ▁successfully ▁received ▁bit ▁is ▁the ▁one ▁that ▁ ▁intended ▁to ▁send . ▁Therefore , ▁having ▁a ▁gen ie ▁allows ▁us ▁to ▁achieve ▁a ▁rate ▁of ▁ ▁on ▁average . ▁ ▁This ▁additional ▁information ▁is ▁not ▁available ▁normally ▁and ▁hence ▁ ▁is ▁an ▁upper ▁bound . ▁ ▁De let ion ▁channel ▁▁ ▁The ▁binary ▁er asure ▁channel ▁should ▁not ▁be ▁confused ▁with ▁a ▁delet ion ▁channel ▁where ▁bits ▁from ▁the ▁transmit ter ▁are ▁either ▁transm itted ▁to ▁the ▁receiver ▁( with ▁probability ▁) ▁or ▁dropped ▁without ▁not ifying ▁the ▁receiver ▁( with ▁probability ▁). ▁Det erm ining ▁the ▁entropy ▁of ▁the ▁delet ion ▁channel ▁is ▁an ▁open ▁problem . ▁ ▁See ▁also ▁▁ ▁Er asure ▁code ▁ ▁References ▁▁ ▁David ▁J . ▁C . ▁Mac K ay . ▁Information ▁Theory , ▁In ference , ▁and ▁Learning ▁Alg orithms ▁Cambridge : ▁Cambridge ▁University ▁Press , ▁ 2 0 0 3 . ▁▁ ▁Thomas ▁M . ▁Cover , ▁Joy ▁A . ▁Thomas .