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Satellite communications plays an important role in future Internet of Things (IoT) networks, and mega satellite constellations can further provide global coverage and high-quality services for IoT communications. In the mega constellation, large-scale satellites are launched to enhance the capacity. However, the dense distribution of satellites brings intra-constellation interference, limiting the performance. In order to evaluate the restriction of interference caused by system parameters such as the scale of constellation or the frequency reuse factor, we investigate uplink intra-constellation interference and performance of the mega satellite constellation. First, a multi-beam polar constellation with uplink spatial frequency reuse is assumed. Then, the interference model is constructed considering the antenna gain of interfering user terminals and multi-beam satellites, where the details of satellite-fixed frequency reuse scheme and coordinates of co-frequency cells are provided. To evaluate the performance, expressions of outage probability, ergodic capacity and sum ergodic capacity are driven. The analytical results disclose the impact of system design on the performance, and the accuracy of analysis results is obtained through extensive simulation evaluation. Results show that sum ergodic capacity achieves highest in the case of full frequency reuse for frequency limited constellation system, and it gets a linear growth at first but then keeps flat with a trend of fluctuating downward as scale increases, therefore, the impact of the scale should be considered when constructing mega constellations.

In helicopter satellite communication systems, the received signal is blocked by the helicopter rotor blades during propagation, incurring the unstableness of traditional methods of channel estimation due to unknown channel state. To this end, a joint occlusion detection and phase estimation algorithm is proposed by factor graph in this paper. For occlusion detection to acquire channel state, the occlusion channel model is established relying on a Markov chain with a priori blocking ratio, in which the probability distribution of the channel state is calculated by the proposed message-passing algorithm. To obtain the occlusion position and update the blocking ratio, a cross-correlation method is designed. Furthermore, a joint block detection and phase estimation algorithm is developed based on the proposed occlusion detection algorithm to realize phase estimation by iterative receiver, where occlusion detection, phase estimation, and decoding are performed in turn for one turbo iteration and the result of decoding is then sent back to the occlusion detection step for the next iteration. Simulation results show that the proposed algorithm is adaptive to the dynamical change in the blocking ratio and can achieve bit error rate performance that is close to the performance bounds based on perfect channel state.

Random access is a key technology in satellite communication, as a large number of machine- type communication (MTC) terminals accessing the satellite makes it difficult to guarantee the access quality. Irregular repetition slotted ALOHA (IRSA) is one random access protocol relying on transmitting irregular number of replicas in multiple time slots, achieving a peak throughput at 0.8 in practical implementations. However, the probability of sending a certain number of replicas stays the same when given degree distribution, without considering the effects of different loads, which means there are extra useless packets sent and brings power waste in IRSA. Therefore, enhanced irregular repetition slotted ALOHA (EIRSA) based on tracking degree distribution control (TDDC) algorithm is proposed in this paper with adaptive degree distribution adjustment scheme to reduce the number of replicas while maintaining the same access performance with adaptation. Simulation results show that proposed protocol can achieve higher performance at the same power level and it is adaptive to load change.

Grant-free non-orthogonal multiple access (NOMA) is highly expected to support massive connectivity and reduce the transmission latency for future wireless communications. In this paper, we present a joint active user and data detection with no priori knowledge of the active users relying on expectation propagation (EP) and Gaussian approximation (GA) algorithm. To detect the user activity, a structured spike and slab prior is introduced to present the sparsity of transmission signal. Further, the parameters unknown are learned via expectation maximization (EM), which improves the performance of active user detection. Specifically, the active user detection problem in NOMA is firstly formulated under EM framework by parameter learning, and then the transmission data can be detected accurately by message-passing algorithms (MPA). Simulation experiments demonstrate the superiority of our proposed EP-GA-EM algorithm both in the performance of reconstruction and the bit error rate (BER).