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Standard cell-free (CF) massive multiple-input-multiple-output (mMIMO) systems is a promising technology to cover the demands for higher data rates in fifth-generation (5G) networks and beyond. These systems assume a large number of distributed access points (APs) using joint coherent transmission to communicate with the users. However, CF mMIMO systems present an increasing computational complexity as the number of users increases. Scalable cell-free CF (SCF) systems have been proposed to face this challenge. Given that the cost-efficient deployment of such large networks requires low-cost transceivers, which are prone to unavoidable hardware imperfections, realistic evaluations of SCF mMIMO systems should take them into account before implementation. Hence, in this work, we focus on the impact of hardware impairments (HWIs) on the SCF mMIMO systems through a general model accounting for both additive and multiplicative impairments. Notably, there is no other work in the literature studying the impact of phase noise (PN) in the local oscillators (LOs) of CF mMIMO systems or in general the impact of any HWIs in SCF mMIMO systems. In particular, we derive upper and lower bounds on the uplink capacity accounting for HWIs. Moreover, we obtain the optimal hardware-aware (HA) partial minimum mean-squared error (PMMSE) combiner. Especially, the lower bound is derived in closed-form using the theory of deterministic equivalents (DEs). Among the interesting findings, we observe that separate LOs (SLOs) outperform a common LO (CLO), and the additive transmit distortion degrades more the performance than the additive receive distortion. [ABSTRACT FROM AUTHOR]

One of the main goals of the future wireless networks is improving the users’ quality of experience (QoE). In this paper, we consider the problem of the QoE-based resource allocation in the downlink of a massive multiple-input multiple-output heterogeneous network. The network consists of a macrocell with a number of small cells embedded in it. The small cells’ base stations (BSs) are equipped with a few antennas, while the macro BS is equipped with a massive number of antennas. We consider the two services Video and Web Browsing and design the beamforming vectors at the BSs. The objective is to maximize the aggregated mean opinion score (MOS) of the users under constraints on the BSs’ powers and the required quality of service of the users. We also consider extra constraints on the QoE of users to more strongly enforce the QoE in the beamforming design. To reduce the complexity of the optimization problem, we suggest suboptimal and computationally efficient solutions. Our results illustrate that increasing the number of antennas at the BSs and also increasing the number of small cells’ antennas in the network leads to a higher user satisfaction. [ABSTRACT FROM AUTHOR]

Massive MIMO (MM) is one of the leading technologies that can cater for very high capacity demand. However, energy consumption of MM systems needs to be load adaptive in order to cope with the significant temporal load variations (TLV) over a day. In this paper, we propose a game-theoretic model for studying load adaptive multicell massive MIMO system where each base station (BS) adapts the number of antennas to the TLV in order to maximize the downlink energy efficiency (EE). The utility function considered here is defined as the number of bits transferred per Joule of energy. In order to incorporate the TLV, the load at each BS is modeled as an $M/G/m/m$ state dependent queue under the assumption that the network is dimensioned to serve a maximum number of users at the peak load. The EE maximization problem is formulated in a game theoretic framework where the number of antennas to be used by a BS is determined through the best response iteration. This load adaptive system achieves around 24% higher EE and saves around 40% energy compared to a baseline system where the BSs always run with the fixed number of antennas that is most energy efficient at the peak load and that can be switched off when there is no traffic. [ABSTRACT FROM AUTHOR]

This paper proposes a flexible pilot assignment method to jointly optimize the uplink and downlink data transmission in multi-cell Massive multiple input multiple output (MIMO) systems with correlated Rayleigh fading channels. By utilizing a closed-form expression of the ergodic spectral efficiency (SE) achieved with maximum ratio processing, we formulate an optimization problem for maximizing the minimum weighted sum of the uplink and downlink SEs subject to the transmit powers and pilot assignment sets. This combinatiorial optimization problem is solved by two sequential algorithms: a heuristic pilot assignment is first proposed to obtain a good pilot reuse set and the data power control is then implemented. Numerical results manifest that the proposed algorithm converges fast to a better minimum sum SE per user than the algorithms in previous works. [ABSTRACT FROM AUTHOR]

We consider a two-way half-duplex relaying system where multiple pairs of single-antenna users exchange information assisted by a multiple-antenna relay. Taking into account the practical constraint of imperfect channel knowledge, we study the achievable sum spectral efficiency (SE) of the amplify-and-forward protocol, assuming that the relay employs maximum ratio processing. We derive a closed-form expression for the sum SE for arbitrary system parameters and a large-scale approximation for the sum SE when the number of relay antennas $M$ becomes sufficiently large. In addition, we study how the transmit power reduces with $M$ to maintain a desired SE. Our results show that by using a large number of relay antennas, the transmit powers of the user, relay, and pilot symbol can be scaled down proportionally to $1/M^\alpha$ , $1/M^\beta$ , and $1/M^\gamma$ for certain combinations of $\alpha$ , $\beta$ , and $\gamma$ , respectively. This elegant power scaling law reveals a fundamental tradeoff between the transmit powers of the user/relay and pilot symbol. Finally, capitalizing on the new expressions for the sum SE, novel power allocation schemes are designed to further improve the sum SE. [ABSTRACT FROM AUTHOR]

Transceiver hardware impairments (e.g., phase noise, in-phase/quadrature-phase imbalance, amplifier nonlinearities, and quantization errors) have obvious degradation effects on the performance of wireless communications. While prior works have improved our knowledge of the influence of hardware impairments of single-user multiple-input multiple-output (MIMO) systems over Rayleigh fading channels, an analysis encompassing the Rician fading channel is not yet available. In this paper, we pursue a detailed analysis of regular and large-scale (LS) MIMO systems over Rician fading channels by deriving new closed-form expressions for the achievable rate to provide several important insights for practical system design. More specifically, for regular MIMO systems with hardware impairments, there is always a finite achievable rate ceiling, which is irrespective of the transmit power and fading conditions. For LS-MIMO systems, it is interesting to find that the achievable rate loss depends on the Rician $K$- factor, which reveals that the favorable propagation in LS-MIMO systems can remove the influence of hardware impairments. However, we show that the nonideal LS-MIMO system can still achieve high spectral efficiency due to its huge degrees of freedom. [ABSTRACT FROM PUBLISHER]