Your objective is to solve $Ax=b$ subject to the constraint $||x||=1$. One way to do this is to recognize that the solution to $Ax=b$ is an affine space (translated subspace). This means that out of all the solutions to $Ax=b$, there is a unique "smallest" one, i.e. where $||x||$ is as small as possible. To find this solution, you can solve the minimium-norm optimization problem, whose solution is:
$$
x_0 = \lim_{\lambda \to 0^+} (A^TA+\lambda I)^{-1}A^Tb
$$
In the case where $A$ is skinny and full-rank, this simplifies to $(A^TA)^{-1}A^Tb$. In general, the solution can be found by taking the SVD of $A$.
Once you have verified that $x_0$ is indeed a solution, the next thing to do is check that $||x_0|| \leq 1$, for if that were not the case, then you could confidently assert that there is no solution to $Ax=b$ with norm 1.
The next step is to find a basis for the nullspace of $A$, i.e. the set of vectors $x$ such that $Ax=0$. This can be done using the SVD again. If $N$ is a matrix whose columns form a basis for the nullspace of $A$, then the set of all solutions to $Ax=b$ is:
$$
x = x_0 + Ny
$$
where $y$ is an arbitrary vector of appropriate dimension. The key point here is that $x_0$ is orthogonal to all vectors in the nullspace of $A$ (because $x_0$ is the solution point closest to the origin). Thus, you may compute the norm of $x$ using the Pythagorean theorem:
$$
||x||^2 = ||x_0||^2 + ||Ny||^2
$$
Now we see that the if we want $||x||=1$, we simply need to choose $y$ so that
$$
||Ny|| = \sqrt{1 - ||x_0||^2}
$$
We may always choose the columns of $N$ to be orthonormal, so let's assume we have done that. Then the set of desired $y$ is precisely the sphere with radius $\sqrt{1 - ||x_0||^2}$. By choosing any $y$ on this sphere, we parametrize all solutions to your problem. If you only care about a particular solution, then you can choose $y$ of the form $[\alpha,0,...,0]$, where $\alpha = \sqrt{1 - ||x_0||^2}$.f