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Opportunistic cooperative spectrum sharing and optimal receive combiner for cognitive MU-MIMO systems

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Abstract

Cognitive multiple-input/multiple-output (MIMO) has been studied as a solution to improve spectrum utilization via dynamic spectrum sharing technology. In cognitive MIMO systems, it is most important to design a transceiver for minimizing interference from the cognitive base station (CBS) to primary users, and for maximizing the sum rate of cognitive users (CUs). In this paper, we first propose opportunistic cooperative spectrum sharing to improve the sum rate of the CR system through an increase of the achievable maximum number of serving CUs, while guaranteeing the quality of service of the primary system. Secondly, an optimal receive combiner (ORC) for the CR system is proposed to maximize the signal to interference plus noise ratio (SINR) of CUs. Utilizing the geometric analysis for a given MIMO channel, we propose a criterion for the beam-forming vector selection and then design the ORC scheme based on major factors that affect the SINR, i.e., multi-user interference among CUs, interference from the primary base station to CUs and the desired channel gain. Consequently, it is demonstrated that the ORC maximizes the sum rate of the cognitive MU-MIMO system.

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Notes

  1. In the multi-user MIMO system with limited feedback, the quantization error in codebook-based zero-forcing beamforming generates the MUI. Similarly, in random unitary beamforming, the mismatch between the selected beam and the actual user channel leads to generation of the MUI.

  2. According to the user selection in the OCSS algorithm, the variation of interference from the CBS to PUs is reduced. Thus, the lower-bound can be approximated as \({\mathbb{E}}[{\mathrm{P}}{\mathrm{-}}{\mathrm{SINR}}_k]\).

  3. It is assumed that the target SINRs of PUs are identical in the primary system. Although each PU has a different target SINR in practice, \(\gamma _p\) can be set to the minimum or maximum target SINR among the PUs. The analysis based on \(\gamma _p\) is then interpreted as the upper or lower bounds of the performance.

  4. Basically, we assume that \(N_R-N_p > 0\) in this paper. However, when \(N_R-N_p \le 0\), \({\mathbf{H}}_k\) can be projected on the right singular vectors corresponding to the relatively small singular values of the interference channel space. In this case, it can be presumed that the receive combiner is designed on the left singular vectors corresponding to the relatively small singular values of the interference channel space in order to minimize the IPI.

  5. There exist infinite base vector sets to span B. Thus, we can make the different beam vector sets orthogonal to A. The regenerated beam vectors in RUB have been studied in [24] and [25].

  6. \(\gamma _\xi\) is similarly used as \(\epsilon\) for the semi orthogonal user selection in [27]. It can be determined to maximize the performance of PUs through the simulation, as in Fig. 2 in [27]. However, we set \(\gamma _{\xi }\)=0.5 in Table 1 because \(\gamma _{\xi }\)=0.5 is the minimum value to guarantee \(||\overline{{\mathbf{h}}}_k^I{\mathbf{B}}||^2 \le ||\overline{{\mathbf{h}}}_k^I{\mathbf{A}}||^2\) where \(||\overline{{\mathbf{h}}}_k^I{\mathbf{A}}||^2+||\overline{{\mathbf{h}}}_k^I{\mathbf{B}}||^2=1\) for \(k\in {\mathcal{S}}\). This means that \(\overline{{\mathbf{h}}}_k^I\) is close to space \({\mathbf{A}}\) rather than to space \({\mathbf{B}}\).

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Acknowledgements

This paper was supported by Wonkwang University in 2018.

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  1. Multidimensional Communication and Signal Processing Lab., Department of Electronic Engineering, University of Wonkwang, 460 Iksandae-ro, Iksan, Jeonbuk, Republic of Korea

    Hyukmin Son

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Son, H. Opportunistic cooperative spectrum sharing and optimal receive combiner for cognitive MU-MIMO systems. Wireless Netw 26, 2271–2285 (2020). https://doi.org/10.1007/s11276-019-02145-w

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