## 1 Introduction  
In LTE-Advanced Pro related systems, due to the spectrum resource limitations, the system capacity cannot increase without limitation, so cooperative transmission has been adopted to enhance transmission speed, especially for cell-edge users with poor performance. However, the link to transmit cooperative information is also capacity constrained when the amount of cooperative data is huge, so it will also face a bottleneck. Such conditions will get worse with the quick development of the Internet of Things (IoT), as the number of terminals working in cooperative mode will deteriorate the wireless environment. To tackle this problem, various inter-cell interference mitigation techniques have been adopted. One typical method is Coordinated Multipoint Transmission/Reception (CoMP) [1]. Thisoperation avoids or exploits interference through coordination among different base stations, which is implemented by information sharing via backhaul among coordinated BSs. Generally, there are two ways to implement coordinated transmission based on different types of cooperation: (1) coordinated beamforming or coordinated scheduling (CB/CS), where only neighbor channel state information (CSI) is exchanged for coordinated scheduling and beam forming [2; 3]; (2) Joint Processing (JP): Both data and CSI are shared using cooperative links for joint decoding or transmission [4; 5], which inevitably brings increased pressure on the system backhaul. The problem of backhaul resource constraints in realistic systems is more serious, particularly when full cooperation based on data sharing is employed, especially in multi user scenario [6]. In uplink transmission, a multi-user decoding scheme is proposed to increase system performance. Joint decoding means the signals received at different base stations (BSs) are jointly decoded through exchanging quantized information among cooperative ones [7; 8]. This scheme can exploit diversity and cooperative gain to a large extent, similarly to virtual MIMO (Multiple-Input Multiple-Output) [9], which provides extra overhead to the capacity-limited backhaul. Therefore, it is necessary to reduce the redundancy in shared information transmitted through backhaul links and improve the efficiency of backhaul utilization. In this case, the problems could be solved using distributed source coding with side information, which is commonly used by researchers [10; 11]. Based on this idea, a new backhaul efficient approach method called distributed compression is proposed in [12; 13]. In this method, multiple cooperative BSs compress and forward their received signals through backhaul to the central BS for joint decoding. As these compressed versions carry only necessary information for the central BS, backhaul overhead is greatly reduced and joint processing gain is obtained at the same time. It was shown in [14] that this compress-and-forward scheme achieves good performance compared with the methods without such operations. However, this work is based on a single carrier network. The optimal backhaul resource allocation in more practical multicarrier systems has not been considered yet. In multicarrier systems, the problems become more complex due to the increased dimensions, so researchers are trying to solve such problems by adding more constraints or weakening the scenario. In [15; 16; 17; 18], a capacity constrained backhaul transmission environment has been adopted in wireless networks, and methods using Markov Control are given to adopt the optimal policy, but the constraint is strict and the optimal solution could only be obtained offline. In [19; 20; 21], the authors try to rewrite the problem as a continuous optimal power allocation policy under the system capacity constraint. In this way, the discrete optimization problem has been relaxed to a continuous scenario and the bandwidth resource allocation in backhaul links is not mentioned.