## 2 Reliable, Low-Latency Fronthaul Model  
In 5G, it is recommended to avoid having a single point of failure that could affect a high number of radio sites. Where appropriate, it is important to enforce redundancy schemes. This is because, in a macro site where a single transport facility may aggregate multiple radios, the failure or repair of such transport equipment may have an impact on a large operator serving area. The key requirement of the 5G RAN transport network is to allow meshed connectivity to enable reliability and resilience [15]. In this context, a scenario in which the 5G transport network offers multiple connectivity is considered in this paper. In [13], we presented a solution based on multi-path transmission with erasure coding (MPC) on the Ethernet-based fronthaul network with an MAC-PHY split. The aim is to achieve higher reliability without a significant impact on latency in a Cloud-RAN system. Erasure codes, specifically fountain codes, are rateless in the sense that they do not exhibit a fixed code rate but are adapted to the channel conditions. The original data are split into \(k\) equal blocks, and then encoded independently and randomly, according to a degree distribution which determines the number of blocks to be exclusive-or-ed into \(n\) encoded blocks using an \((n,k)\) maximum distance separable code [16]; this is referred to as the \((n,k)\) fountain code (see Figure 1). One of the properties of fountain coding is that the receiver can retrieve the original transport block (TB) from any subset \((k+\epsilon)\) of encoded blocks; where \(0\leq\epsilon\leq n-k\). Thus, the decoder starts decoding the message once the first \(k\) encoded blocks are received using the sum-product algorithm [16]. If these blocks do not have enough information to retrieve the original message then the encoder has to wait for more encoded blocks. In [13], we analytically derived the average latency using queuing theory by making the following assumptions:  
* Purging scenario, that is, after \(k\) out of \(n\) encoded blocks are received, the other \(n-k\) remaining encoded blocks are removed from the queues;
* The switch on the fronthaul has a negligible delay and hence the switch delay is not included in the fronthaul latency budget. In this paper, however, we implement the same model in a software defined radio-based testbed, while fixing the value of \(n\) to 4, given the hardware limitations, to experimentally evaluate the framework and provide numerical proof on its performance. Furthermore, in this paper, we attempt to overcome the constraints raised by assumptions taken in [13] as follows:  
* Assess the impact of the proposed solution on end-to-end system performance: by executing the solution on a hardware testbed, we can ensure that the system is functioning properly by ensuring that user equipment remains synchronized with the network and maintains network connection, as well as that network resources, such as memory, storage space and queue buffers, are sufficiently available for executing the proposed solution;
* Packets are not removed from the fronthaul network; that is, after receiving \(k\) out of \(n\) encoded blocks, the remaining \(n\)-\(k\) encoded blocks are not removed from the fronthaul network and they will continue to be processed, which is more realistic;
* Consider internal process delay in the (CU + DU) entity: processing time including scheduling, queueing, encoding and packetizing data which might affect the per  
Figure 1: Schematic diagram of an \((n,k)\) fountain code. The blocks are encoded into \(n\) encoded blocks using a given degree distribution. formance of the system. The time taken depends, essentially, on capabilities of the hardware used and how functions are processed.