# CF-mMIMO-Based Computational Offloading for UAV Swarms: System Design and Experimental Results
## 2 System Design
### Edge Tier  
The edge server centrally manages task scheduling for UAV swarms through real-time data acquisition. By leveraging the real-time status, positional data, and task priorities of all UAVs, the system optimizes task allocation to ensure efficient resource utilization. Furthermore, edge servers continuously monitor system resources, swarm task status, data traffic, and network performance metrics to dynamically optimize communication efficiency among UAV and APs. This centralized management and intelligent scheduling approach not only enhances real-time responsiveness to swarm operations but also improves the overall efficiency of task execution. Edge servers establish reliable connectivity with UAV swarms via the CF-mMIMO architecture. The architecture ensures robust coverage in dynamic environments through large-scale deployment of APs, playing a critical role in enhancing communication reliability and reducing latency. As illustrated in Figure 1, the proposed architecture comprises \(N\) APs, \(M\) edge distributed unit (EDU), \(M\) user-centric distributed unit (UCDU), and a CPU. The APs handle radio frequency (RF) signal reception/transmission across Sub-6 GHz frequency bands, while the EDU executes distributed precoding and reception processing. The UCDU manages data distribution and combining operations. In the considered system, it is assumed that no inter-UAV communication exists, thereby resulting in independent tasks. Furthermore, each UAV is served by \(N\) APs, where each AP is equipped with \(N_{\text{t}}\) antennas. The implementation details of uplink and downlink transmissions are presented separately. During the uplink phase, the received signal at the \(n\)-th AP is expressed as  
\[\mathbf{y}_{n}=\sum_{k=1}^{K}\mathbf{h}_{k,n}x_{k}+\mathbf{z}_{n}, \tag{1}\]where \(\mathbf{h}_{k,n}\in\mathbb{C}^{N_{k}\times 1}\) denotes the channel coefficient between the \(k\)-th UAV and \(n\)-th AP, \(x_{k}\) represents the transmitted signal from the \(k\)-th UAV, and \(z_{n}\) denotes additive white Gaussian noise (AWGN) with power \(\sigma^{2}\) at \(n\)-th AP [41]. By employing detector \(\mathbf{v}_{k,n}\) at the \(n\)-th AP, the transmitted signal of the \(k\)-th UAV can be collaboratively detected as  
\[\hat{x}_{k}=\sum_{n=1}^{N}\mathbb{I}_{k,n}\mathbf{v}_{k,n}^{H}\mathbf{y}_{n}, \tag{2}\]  
where \(\mathbb{I}_{k,n}\in\{0,1\}\) is the indicator function that satisfies \(\mathbb{I}_{k,n}=1\) if, and only if, the \(k\)-th UAV is served by the \(n\)-th AP. In a fully distributed architecture, each AP is limited to local CSI, i.e., \(\mathbf{h}_{k,n},\forall k\), and thus performs non-cooperative signal detection (e.g., conventional maximum ratio combining (MRC)). This decentralized strategy inevitably induces significant performance degradation, while full collaboration imposes prohibitive fronthaul overhead. Following the CF-mMIMO architecture in [42], multiple APs are interconnected with a dedicated EDU for signal detection. Specifically, each user may associate with multiple EDUs but only one UCDU. After demultiplexing multi-user data streams, EDUs forward them to the associated UCDU, which performs signal aggregation from distributed EDUs. The composite partial minimum mean squared error (P-MMSE) detector at the \(m\)-th EDU is formulated as [43]  
\[\mathbf{v}_{m,k}=p_{k}\left[\sum_{i\in\mathcal{A}_{m}}p_{i}\overline{\mathbf{ h}}_{i,m}\overline{\mathbf{h}}_{i,m}^{H}+\sigma^{2}\mathbf{I}\right]^{-1} \overline{\mathbf{h}}_{k,m}, \tag{3}\]  
where \(p_{k}\) denotes the transmit power of the \(k\)-th UAV, \(\mathcal{A}_{m}\) represents the set of UAVs associated with the \(m\)-th EDU, and \(\overline{\mathbf{h}}_{i,m}\) denotes the equivalent channel between the \(i\)-th UAV and the \(m\)-th EDU. Substituting (3) into (2) yields the recovered uplink signal from the UAVs.