Satellite communication is an indispensable part of sixth generation of mobile communication systems (6G) given its global coverage and long-distance propagation. Energy consumption and channel acquisition are two critical issues in satellite communication systems. On the one hand, with the rapidly increasing energy wastage in wireless systems, green communication technology has attracted extensive attentions and energy efficiency (EE) becomes the key performance indicator in the transmission scheme design of satellite communications. On the other hand, channel state information (CSI) is well required in designing the beamforming, but it is infeasible for gateway (GW) to acquire accurate CSI at the transmitter (CSIT) due to the long-distance delay. Most of the existing work focused on maximizing the total EE (TEE) or minimum EE (MEE) while tradeoff between TEE and MEE has not been investigated in the satellite communication systems. However, it is of both great theoretical and practical significance for satellite systems to investigate the tradeoff between the TEE and MEE so as to balance the total system performance and the fairness in terms of EE. In a research paper recently published in Space: Science & Technology, a team from National Mobile Communications Research Laboratory, Southeast University investigated the EE optimization for robust multigroup multicast satellite communication systems given imperfect CSI, aiming at the beamforming design that balances the TEE-MEE tradeoff.
First of all, authors present the model of multigroup multicast satellite communication systems and the optimization problem formulation. A downlink multigroup multicast satellite communication system is depicted in Fig. 1, where Nu single antenna users are simultaneously served by Nt beams. The information data is sent by a GW to the users within the coverage areas of the satellite beams. It is assumed that the feeder link between the GW and satellite is ideal and no intercluster interference is considered. In addition, each beam contains a multicast group, and there are a total of M = Nt multicast groups. The users are grouped according to the method by You et al [1]. Every user can only belong to one of the multicast groups. The signal received by the ith user in group m is represented as (1), where hi ∊ ℂNt×1 and wi ∊ ℂNt×1 are the forward link beam domain channel vector from all Nt beams to the ith user and the beamforming vector for group m, respectively. sm is denoted as the transmit signal for all users in group m and ni denotes the additive white Gaussian noise.
Moreover, authors also pay attention to modeling uncertainty ei of the channel phase θi in hi, which also implies the accurate satellite channel vector hi is troublesome to acquire. To this end, the robust beamformer design based on expectation of accurate channel vector is introduced next. According to the signal model in (1), we can obtain the signal-to-interference-plus-noise ratio (SINR) of the ith user in group m, i.e. SINRi,m. The data rate of the ith user in group m is the average value related to the satellite channel phase uncertainty qi, i.e. Ri,m ≜ ????{log2(1 + SINRi,m)}, in order to ensure robustness. The multicast rate of the users in group m can be denoted by Rm ≜ min Ri,m. The sum rate of all multicast groups is expressed as Rtot ≜ Σ Rm (m = 1, …, M). The power consumption of the mth group is modeled as Pm ≜ ξm‖wi‖22 + P0,m, where ξm>1 is the inefficiency of the power amplifier and P0,m is the basic power consumption for group m. The total power consumption of all groups is Ptot ≜ Σ Pm (m = 1, …, M). With the multigroup multicast rate and power consumption, the EE of group m is EEm ≜ B∙Rm/Pm and the system TEE is EEtot ≜ B∙Rtot/Ptot, where B is the bandwidth. Specifically, a new metric, namely, the weighted product (WP) of TEE and MEE is introduced as FWP(β). Therefore, the robust beamforming design can be formulated as (2).
Then, authors transform the optimization problem and present the TEE-MEE trade-off algorithm. To make the problem easier to tackle, the original problem ????1 undergoes several transformations. Introducing auxiliary variables {αm}, ????1 can be equivalently converted into ????2 where B is also omitted with loss of generality. Adopting a variable transformation by introducing auxiliary variables 2u and 2v, ????2 is converted to ????3. Moreover, for Ri,m, an explicit tight approximate function of the average rate is also used. An efficient approximation method called as semidefinite relaxation (SDR) is invoked and the optimization variables are transformed into {Wm}, thus relaxing ????3 as ????4. To make the notations more concise, {W1, …, Wm} is denoted by W, and then problem ????4 is rewritten as ????5 which is a differential convex (DC) programming. The concave-convex procedure (CCCP) is utilized to handle this DC problem and the constraints in ????5 can be re-expressed, thus transforming ????5 into a series of optimization subproblems which is expressed as ????6. Now, problem ????6 is convex and can be handled utilizing classical convex optimization methods. If the ranks of Wm*, ∀ m, to problem ????6 are all equal to one, the corresponding Wm*, ∀ m, will be a feasible solution to problem ????2. Then, the robust multicast beamformers can be obtained. When the ranks of Wm*, ∀ m, to problem ????6 are not all one, the Gaussian randomization method is adopted to handle the rank issue. In summary, the whole approach for the considered problem is described in Algorithm 1.
Finally, authors discuss the performance of the proposed robust algorithm in multigroup multicast satellite communication systems through numerical simulations and give a brief conclusion. In simulation, the number of users in each group is set to be 5, and the number of beams is set to be 7. The simulation results are based on 106 channel realizations. Figure 2 depicts the convergence curves of Algorithm 1 for some typical system settings. It can be observed that the objective function has a fast convergence speed and usually converges within about 5 iterations for the given simulation parameters. Simulation results also indicate that using more antennas can improve the EE performance. Figure 3 demonstrates the TEE-MEE tradeoff curves attained by Algorithm 1 under different priority weights. It can be observed that all the TEE-MEE points of the proposed approach lie on or above the line where TEE = MEE. In addition, the conventional baseline approach in which the obsolete CSI is directly utilized is compared. It can be observed that the proposed robust beamforming design approach outperforms the conventional one. In conclusion, those numerical results illustrated that the proposed robust beamforming design approach could effectively balance the tradeoff between TEE and MEE. Meanwhile, the proposed robust algorithm outperforms the conventional baselines in terms of the EE performance.
References
[1] You L, Liu A, Wang W, Gao X. Outage constrained robust multigroup multicast beamforming for multi-beam satellite communication systems. IEEE Wireless Commun Lett. 2019;8(2):352–355.
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