One of the main limitations of a centralized detection technique is that it requires a large bandwidth of the communication link between the system nodes.
In particular, in [6], a centralized DCA technique considering a heterogeneous network that consists of small cells and macro cells is investigated based on the graph approach.
It is worth noting that for the case of N=61, the centralized technique was replaced with the alternating-coordinate technique.
Alternatively, the centralized technique may be replaced with the standard alternating-coordinate optimization technique for dense networks.
According to Figure 6, the greedy technique with any of the scheduling modes reaches the sum rate value of 48 bits per channel use or less with 100% probability, while both the proposed and the centralized technique provide about 49 bits per channel use.
Still in Figure 10, the greedy technique with any of the scheduling modes reaches the sum rate value of 45 bits per channel use or less with 100% probability, while both the proposed and the centralized technique provide about 51 bits per channel use as aforementioned.
Furthermore, it is well known that the use of centralized techniques entails extra implementation costs and an increase in system's complexity [1,20,21].
Despite optimality of centralized techniques in resource allocation, finding the analytic formulation and in turn the optimum point is prohibitively difficult due to the non-convexity of the associated optimization problem [16, 17].
Was it a centralized, or centralizing, authority?
On this basis, a centralized Principal Component Analysis (PCA) technique is utilized to perform the compression and recovery for the predicted data on the CHs and the sink, separately in order to save the communication cost and to eliminate the spatial redundancy of the sensed data about environment.
