The diffusion-based MVDR beamformer of [97] makes use of the diffusion adaptation algorithm for networks to iteratively optimize weight values across the sensors. The beamformer is partially MVDR since the algorithm assumes covariances only among at most two-hop neighbours leading to a sparse covariance matrix for network sensor observations where distant nodes are considered independent. The beamformer is therefore divided into two components: the weight vector optimization process; and the weighted averaging that is required to produce a beamformed output sig- nal.
To determine the weight vector, local cost functions are first defined at each node i that include a prescaling term to account for multiple occu- rances of the same node to node covariances within the network. These are given by
Jk(w) =C†2◦E[|uVi(t)
∗ w|2],
(2.75) where uVi(t) is the vector of observations in the neighbourhood of node
i, ◦ is the Hadamard or element-wise product, and C†2 is the protected element-wise inverse ofC2, the square of the adjacency matrix (also known as the binary connection matrix). This results in a global cost function given by the sum of all local costs
Jglob(w), V X i=1 C†2◦E[|uVi(t) ∗ w|2] =w∗˜ Ruw, (2.76)
where R˜u is the partial covariance matrix of the network observations. The local cost function (2.75) is expanded and differentiated giving [89]
Ji(w) =w∗(C†2◦R˜u)w,
−[∇Ji(w)]∗ =−(C†2◦R˜u)w,
(2.77) where an instantaneous approximation of this moment will be used
˜ Ru =E[uVi(t)u ∗ Vi(t)]≈uVi(t)u ∗ Vi(t). (2.78)
With no linear constraint the trivial solution to equation (2.75) will be the zero vector, resulting in a beamformer output of zero. We will therefore orthogonally project the outputs of each diffusion iteration onto the sub- space satisfying this linear constraint. This yields the following iterative diffusion process φki+1 =wki −µi X l∈Ni cji[C†2◦uVj(t)u ∗ Vj(t)]w k i ψik+1 =P⊥dφik+1+d(d∗d)−1 wki+1 =X l∈Vi ajiψkj+1 (2.79)
where φki+1 is a stochastic gradient descent step, ψki+1 is an intermediate step that projects the estimate φki+1 onto the linear constraint subspace [16], and wki+1 is a summation of neighbourhood intermediate variables to diffuse information.
The diffusion process and projection requires knowledge of the net- work topology, a reasonable assumption due to recent developments [45], and also requires a transfer of receive vectors at the start of every iteration to form the vectoruVi(t). Once the weighting values are optimized a sim-
ple averaging process may be used to form the final beamformer output, such as RGA.
The diffusion-based MVDR beamformer is adaptive to varying inter- ference statistics, only requires a single update per iteration, and approx- imates a full MVDR centralized beamformer with two-hop covariances.
2.4. EXISTING DISTRIBUTED NEAR-FIELD BEAMFORMERS 45 However, each node will ultimately end up with a lengthN vector of ev- ery node in the network’s weight value as well as require the projection of this vector onto the linear constraint subspace. This limits the algorithm’s true distributed nature, particularly in very large networks. Additionally, convergence can be quite slow due to the gradient optimization steps and synchronous vector updates are required for each update iteration.
Chapter 3
A Distributed Beamformer using
PDMM
In this chapter will describe the development of a distributed MVDR beam- former using both the alternating direction method of multipliers (ADMM) and the primal-dual method of multipliers (PDMM). We will first for- mulate our beamformer as a separable linearly constrained convex opti- mization problem in the primal domain and then derive the dual problem form. This will allow us to phrase the problem in a form acceptable by the ADMM and PDMM algorithm. We will derive update equations for a distributed ADMM MVDR beamformer and finally discuss the difficulty encountered in the PDMM case.
3.0.1
Derivation of a PDMM Beamformer
Recall that the traditional centralized MVDR beamformer minimizes out- put energy while maintaining a distortionless response in the direction of the source. This may be expressed as an optimization problem of the form
minimize w E[|u ∗ w|2] =w∗Rw, subject to d∗w = 1, (3.1) 47
where u ∈CN is the complex observed signal vector,w ∈
CV is the com-
plex weight vector over which the optimization is performed, d ∈ CV is the complex acoustic transfer function vector, and R ∈ CV×V
+ is the Her-
metian positive semi-definite covariance matrix of the observed signals. A problem that arises in optimization problems over complex variables is the difference in differentiability by real variables when compared with complex variables [76]. A common approach for dealing with this issue is to convert the optimization of real functions in complex variables into an optimization of real functions in real variables by considering the real and imaginary components of our complex variables as two separate real vari- ables [18, 125]. This essentially doubles the dimension of our optimization problem as we are now optimizing over variables inR2V. However, these
higher dimension expressions become cumbersome and only add to the confusion of our derivations. Therefore, throughout the rest of this report we will assume this transformation has already been performed and deal with real valued functions of dimensionV, i.e.u,w,d∈RV.
We now make the following three assumptions:
• Assumption 1- The underlying network is sparsely connected.
• Assumption 2- Observations of nodes not sharing a neighbourhood
are assumed to be independent, i.e.
E|uTi uj|= [R]i,j = 0,∀i, j /∈ Vk, (3.2) where[R]i,j is element(i, j)of the covariance matrixR.
• Assumption 3 - Each nodek stores local estimates of neighbouring
weight components and the estimated covariances of their neigh- bourhoods[R]i,j∀(i, j)∈ Vi.
49 tion as 1 2w ∗ Rw= 1 2w ∗ (C†2◦R)w = 1 2 X i∈V w∗iRiwi (3.3)
where wi ∈ RV is the vector of weights in the neighbourhood of node i,
C†2 is a mask that applies the network sparsity pattern and accounts for
multiple neighbours carrying the same entry of the global weight vector
w, and Ri ∈ SV+ is the resulting prescaled sparse covariance matrix of the
neighbourhood Vi. This has allowed us to separate our centralized cost function into a sum ofN local cost functions present at each nodeiacross the network.
A problem still remains, however, for application of PDMM. The global distortionless response constraint d∗w = 1is not immediately able to be encoded in the edge-wise constraints present in the PDMM problem form (2.32). To deal with this problem we first recall the central analytic solution
w = R
−1
d
dHR−1d. (3.4)
Rather than attempting to find the weight vector w across all nodes we instead restrict our attention to finding a scaled version of the weight vec- torx =R−1dsince the beamformer output may then be calculated using gossip algorithms as z = x Tu xTd = 1 V P i∈V[x]Ti [u]i 1 V P i∈V[x]Ti [d]i . (3.5)
This is simply the ratio of two global averages, which is required to pro- duce a beamformer output signal regardless of the method used to op- timize our weight vector. To obtain x we construct the unconstrained quadratic program
minimize f(x) = 1 2x
which we may then decompose as 1 2x TRx−dT x=X i∈V 1 2x T(C†2◦R)x−dT ixi =X i∈V 1 2x T i Rixi −dTi xi . (3.7)
where xi ∈ RVi is the local vector of neighbourhood elements from the global vector x, di ∈ RVi is a vector containing all zeros apart from the
kth entry which is equal to node i’s scalar ATFdi, and Ri ∈ RVi×Vi is the covariance matrix for the neighbourhood Vi. Provided we enforce pair- wise consensus constraints between the primary scalar element of nodei
(i.e. [x]i) and the copy held by nodej for allj ∈ Vi, we obtain the optimal vector entries ofxacross neighbourhoods ofkas solutions of
minimize X i∈V fi(xk) = X i∈V 1 2x T i Rixi−dTi xi , subject to Ai|jxi =Aj|ixj ∀(i, j)∈ E, (3.8)
where the matrices Ai|j ∈ R2×Vi andAj|i ∈ R2×Vj contain entries of1or0 to enforce consistency (with one row each for the consensus of nodeiand
j’s primary elements and their copies), and the local vectorxiis nodeik’s idea of the elements of x belonging to its neighbourhood Vi. Using the PDMM update equations of section 2.2.5 we arrive at the iterations∀i∈ V
xki+1 = X j∈Vi ρATi|jAi|j +Ri −1 di+ X j∈Vi ATi|j(ρAj|ixkj −λ k j|i) (3.9) λki|+1j =ρ(Aj|ixkj −Ai|jxki+1)−λ k j|i ∀j ∈ Vi. (3.10)
In the next section we will test the proposed PDMM beamformer in a simulated environment to confirm that it converges to the central solution obtained by (3.4). Recall that this approach does not include any form of weight regularization, such as attempting to reduce the energy of weights or induce sparsity in our weight set.