This paper studies a cell-free massive multiple-input multiple-output (MIMO) system where its access points (APs) and users are equipped with multiple antennas. Two transmission protocols are considered. In the first transmission protocol, there are no downlink pilots, while in the second transmission protocol, downlink pilots are proposed in order to improve the system performance. In both transmission protocols, the users use the minimum mean-squared error-based successive interference cancellation (MMSE-SIC) scheme to detect the desired signals. For the analysis, we first derive a general spectral efficiency formula with arbitrary side information at the users. Then analytical expressions for the spectral efficiency of different transmission protocols are derived. To improve the spectral efficiency (SE) of the system, max-min fairness power control (PC) is applied for the first protocol by using the closed-form expression of its SE. Due to the computation complexity of deriving the closed-form performance expression of SE for the second protocol, we apply the optimal power coefficients of the first protocol to the second protocol. Numerical results show that two protocols combining with multi-antenna users are prerequisites to achieve the suboptimal SE regardless of the number of user in the system. Index terms-Cell-free massive MIMO, massive MIMO, spectral efficiency, MMSE-SIC, power control. I. INTRODUCTION Cellular massive multiple-input multiple-output (MIMO) is currently considered as a key wireless access technology for 5G because it can provide high spectral efficiency (SE) and high energy efficiency (EE) with simple signal processing [2], [3]. In cellular massive MIMO, the BS with massive antenna arrays simultaneously serves all users in its cell on the same time-frequency resource [4]-[7]. Since cellular massive MIMO is based on cellular topology, its inherent limitation is inter-cell interference. To overcome this limitation, cell-free massive MIMO is introduced [8]. Cell-free massive MIMO can be considered as a useful and scalable version of network MIMO [9], [10] (much in the same way as cellular Massive MIMO is scalable version of multiuser MIMO). In cell-free massive MIMO, a large number of access points (APs), which are geographically distributed over a large area, coherently serve all users on same timefrequency resource [8], [11]. Cell-free massive MIMO can
Abstract-A key problem in nanomachine networks is how information from sensors is to be transmitted to a fusion center. In this paper, we propose a molecular communicationbased event detection network. In particular, we develop a detection framework that can cope with scenarios where the molecules propagate according to anomalous diffusion instead of the conventional Brownian motion. We propose an algorithm for optimizing the network throughput by exploiting tools from reinforcement learning. Our algorithms are evaluated with the aid of numerical simulations, which demonstrate the tradeoffs between performance and complexity. I. INTRODUCTIONNetworks consisting of a large number of nanomachines, which are able to sense, communicate, and actuate at the nanoscale, have been proposed for applications ranging from intrabody health monitoring to pollution control [1]. A key component of these networks is the detection of events such as the presence of undesirable chemicals in the atmosphere or the malfunction of cells in biological systems. In a network of distributed nanomachines, this information must then be sent to a fusion nanomachine (FN), which can take action to mitigate the effect of the event.Molecular communication forms one approach to support communication among sensing nanomachines (SNs) and a FN, where unlike conventional electromagnetic-based communication, each SN encodes information in the release time, number, or type of molecules emitted by each SN [1]- [3]. Molecular communication raises new challenges, due to the low energy and limited computational resources available at each nanomachine. Moreover, the achievable throughput of molecular communication systems is limited by the noise introduced from the random diffusion time of each molecule.In existing works, the diffusion is typically modeled according to Brownian motion. In this case, it is possible to derive closed-form expressions for the first passage time distribution and the impulse response for reasonable boundary conditions. Under the assumption of Brownian motion, capacity characterizations [4], [5], practical receiver designs [6], [7] and intersymbol interference mitigation strategies [8] have been proposed.
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