  Broadcasting in wireless networks is vulnerable to adversarial jamming. To
thwart such behavior, \emph{resource competitive analysis} is proposed. In this
framework, sending, listening, or jamming on one channel for one time slot
costs one unit of energy. The adversary can employ arbitrary strategy to
disrupt communication, but has a limited energy budget $T$. The honest nodes,
on the other hand, aim to accomplish broadcast while spending only $o(T)$.
Previous work has shown, in a $C$-channels network containing $n$ nodes, for
large $T$ values, each node can receive the message in $\tilde{O}(T/C)$ time,
while spending only $\tilde{O}(\sqrt{T/n})$ energy. However, these
multi-channel algorithms only work for certain values of $n$ and $C$, and can
only tolerate an oblivious adversary.
  In this work, we provide new upper and lower bounds for broadcasting in
multi-channel radio networks, from the perspective of resource competitiveness.
Our algorithms work for arbitrary $n,C$ values, require minimal prior
knowledge, and can tolerate a powerful adaptive adversary. More specifically,
in our algorithms, for large $T$ values, each node's runtime is $O(T/C)$, and
each node's energy cost is $\tilde{O}(\sqrt{T/n})$. We also complement
algorithmic results with lower bounds, proving both the time complexity and the
energy complexity of our algorithms are optimal or near-optimal (within a
poly-log factor). Our technical contributions lie in using "epidemic broadcast"
to achieve time efficiency and resource competitiveness, and employing coupling
techniques in the analysis to handle the adaptivity of the adversary. At the
lower bound side, we first derive a new energy complexity lower bound for
1-to-1 communication in the multi-channel setting, and then apply simulation
and reduction arguments to obtain the desired result.
