Performance Analysis of IRS-Assisted Cell-Free Communication

In this paper, the feasibility of adopting an intelligent reflective surface (IRS) in a cell-free wireless communication system is studied. The received signal-to-noise ratio (SNR) for this IRS-enabled cell-free set-up is optimized by adjusting phaseshifts of the passive reflective elements. Then, tight approximations for the probability density function and the cumulative distribution function for this optimal SNR are derived for Rayleigh fading. To investigate the performance of this system model, tight bounds/approximations for the achievable rate and outage probability are derived in closed form. The impact of discrete phase-shifts is modeled, and the corresponding detrimental effects are investigated by deriving an upper bound for the achievable rate in the presence of phase-shift quantization errors. MonteCarlo simulations are used to validate our statistical characterization of the optimal SNR, and the corresponding analysis is used to investigate the performance gains of the proposed system model. We reveal that IRS-assisted communications can boost the performance of cell-free wireless architectures.

[1]  Emil Björnson,et al.  Intelligent Reflecting Surfaces: Physics, Propagation, and Pathloss Modeling , 2019, IEEE Wireless Communications Letters.

[2]  Qingqing Wu,et al.  Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming , 2018, IEEE Transactions on Wireless Communications.

[3]  Mohamed-Slim Alouini,et al.  Smart radio environments empowered by reconfigurable AI meta-surfaces: an idea whose time has come , 2019, EURASIP Journal on Wireless Communications and Networking.

[4]  Erik G. Larsson,et al.  Cell-Free Massive MIMO: Uniformly great service for everyone , 2015, 2015 IEEE 16th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[5]  Zhiguo Ding,et al.  A Simple Design of IRS-NOMA Transmission , 2019, IEEE Communications Letters.

[6]  Shi Jin,et al.  Large Intelligent Surface-Assisted Wireless Communication Exploiting Statistical CSI , 2018, IEEE Transactions on Vehicular Technology.

[7]  Bhaskar D. Rao,et al.  Precoding and Power Optimization in Cell-Free Massive MIMO Systems , 2017, IEEE Transactions on Wireless Communications.

[8]  Rui Zhang,et al.  Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network , 2019, IEEE Communications Magazine.

[9]  Gayan Amarasuriya,et al.  Performance Analysis of Distributed Intelligent Reflective Surface Aided Communications , 2020, GLOBECOM 2020 - 2020 IEEE Global Communications Conference.

[10]  Erik G. Larsson,et al.  Fundamentals of massive MIMO , 2016, SPAWC.

[11]  Gayan Amarasuriya,et al.  Cell-Free Massive MIMO with Underlay Spectrum-Sharing , 2019, ICC 2019 - 2019 IEEE International Conference on Communications (ICC).

[12]  Chau Yuen,et al.  Intelligent Reflecting Surface: Practical Phase Shift Model and Beamforming Optimization , 2019, ICC 2020 - 2020 IEEE International Conference on Communications (ICC).

[13]  Athanasios Papoulis,et al.  Probability, Random Variables and Stochastic Processes , 1965 .

[14]  Michail Matthaiou,et al.  Power Scaling of Uplink Massive MIMO Systems With Arbitrary-Rank Channel Means , 2014, IEEE Journal of Selected Topics in Signal Processing.

[15]  Erik G. Larsson,et al.  Cell-Free Massive MIMO Versus Small Cells , 2016, IEEE Transactions on Wireless Communications.

[16]  Erik G. Larsson,et al.  On the Total Energy Efficiency of Cell-Free Massive MIMO , 2017, IEEE Transactions on Green Communications and Networking.

[17]  Jie Chen,et al.  Intelligent Reflecting Surface: A Programmable Wireless Environment for Physical Layer Security , 2019, IEEE Access.

[18]  Ian F. Akyildiz,et al.  A New Wireless Communication Paradigm through Software-Controlled Metasurfaces , 2018, IEEE Communications Magazine.