Ubiquitous Cell-Free Massive MIMO Communications

Since the first cellular networks were trialled in the 1970s, we have witnessed an incredible wireless revolution. From 1G to 4G, the massive traffic growth has been managed by a combination of wider bandwidths, refined radio interfaces, and network densification, namely increasing the number of antennas per site. Due its cost-efficiency, the latter has contributed the most. Massive MIMO (multiple-input multiple-output) is a key 5G technology that uses massive antenna arrays to provide a very high beamforming gain and spatially multiplexing of users, and hence, increases the spectral and energy efficiency. It constitutes a centralized solution to densify a network, and its performance is limited by the inter-cell interference inherent in its cell-centric design. Conversely, ubiquitous cell-free Massive MIMO refers to a distributed Massive MIMO system implementing coherent user-centric transmission to overcome the inter-cell interference limitation in cellular networks and provide additional macro-diversity. These features, combined with the system scalability inherent in the Massive MIMO design, distinguishes ubiquitous cell-free Massive MIMO from prior coordinated distributed wireless systems. In this article, we investigate the enormous potential of this promising technology while addressing practical deployment issues to deal with the increased back/front-hauling overhead deriving from the signal co-processing.Comment: Published in EURASIP Journal on Wireless Communications and Networking on August 5, 201

Comparison of Co-located and Distributed MIMO for Indoor Wireless Communication

This paper compares the communication performance for co-located and emerging distributed MIMO in a typical indoor scenario. The simulations, which are verified against experimental measurement data, show that distributed MIMO offers a significantly more uniform capacity for the users. The results also show that the same user capacity can be achieved with half the number of antennas in the distributed MIMO case

Dynamic Federations for 6G Cell-Free Networking: Concepts and Terminology

Cell-Free networking is one of the prime candidates for 6G networks. Despite being capable of providing the 6G needs, practical limitations and considerations are often neglected in current research. In this work, we introduce the concept of federations to dynamically scale and select the best set of resources, e.g., antennas, computing and data resources, to serve a given application. Next to communication, 6G systems are expected to provide also wireless powering, positioning and sensing, further increasing the complexity of such systems. Therefore, each federation is self-managing and is distributed over the area in a cell-free manner. Next to the dynamic federations, new accompanying terminology is proposed to design cell-free systems taking into account practical limitations such as time synchronization and distributed processing. We conclude with an illustration with four federations, serving distinct applications, and introduce two new testbeds to study these architectures and concepts

A Low Complexity Space-Time Block Codes Detection for Cell-Free Massive MIMO Systems

The new generation of telecommunication systems must provide acceptable data rates and spectral efficiency for new applications. Recently massive MIMO has been introduced as a key technique for the new generation of telecommunication systems. Cell-free massive MIMO system is not segmented into cells. Each BS antennas are distributed throughout the environment and each user is served by all BSs, simultaneously. In this paper, the performance of the multiuser cell-free massive MIMO-system exploying space-time block codes in the uplink, and with linear decoders is studied. An Inverse matrix approximation using Neumann series is proposed to reduce the computational and hardware complexity of the decoding in the receiver. For this purpose, each user has two antennas, and also for improving the diversity gain performance, space-time block codes are used in the uplink. Then, Neumann series is used to approximate the inverse matrix in ZF and MMSE decoders, and its performance is evaluated in terms of BER and spectral efficiency. In addition, we derive lower bound for throughput of ZF decoder. The simulation results show that performance of the system , in terms of BER and spectral efficiency, is better than the single-antenna users at the same system. Also, the BER performance in a given system with the proposed method will be close to the exact method.Comment: 5 pages, 4 figures, Accepted for ICEE202

Dynamic Federations for 6G Cell-Free Networking: Concepts and Terminology

Cell-Free networking is one of the prime candidatesfor 6G networks. Despite being capable of providing the 6Gneeds, practical limitations and considerations are often neglectedin current research. In this work, we introduce the conceptof federations to dynamically scale and select the best set ofresources, e.g., antennas, computing and data resources, to servea given application. Next to communication, 6G systems are expected to provide also wireless powering, positioning and sensing,further increasing the complexity of such systems. Therefore,each federation is self-managing and is distributed over thearea in a cell-free manner. Next to the dynamic federations,new accompanying terminology is proposed to design cell-freesystems taking into account practical limitations such as timesynchronization and distributed processing. We conclude withan illustration with four federations, serving distinct applications,and introduce two new testbeds to study these architectures andconcepts

Centralized and Distributed Power Allocation for Max-Min Fairness in Cell-Free Massive MIMO

Cell-free Massive MIMO systems consist of a large number of geographically distributed access points (APs) that serve users by coherent joint transmission. Downlink power allocation is important in these systems, to determine which APs should transmit to which users and with what power. If the system is implemented correctly, it can deliver a more uniform user performance than conventional cellular networks. To this end, previous works have shown how to perform system-wide max-min fairness power allocation when using maximum ratio precoding. In this paper, we first generalize this method to arbitrary precoding, and then train a neural network to perform approximately the same power allocation but with reduced computational complexity. Finally, we train one neural network per AP to mimic system-wide max-min fairness power allocation, but using only local information. By learning the structure of the local propagation environment, this method outperforms the state-of-the-art distributed power allocation method from the Cell-free Massive MIMO literature

Trade-Off Between Beamforming and Macro-Diversity Gains in Distributed mMIMO

Industry and academia have been working towards the evolution from Centralized massive Multiple-Input Multiple-Output (CmMIMO) to Distributed mMIMO (DmMIMO) architectures. Instead of splitting a coverage area into many cells, each served by a single Base Station equipped with several antennas, the whole coverage area is jointly covered by several Access Points (AP) equipped with few or single antennas. Nevertheless, when choosing between deploying more APs with few or single antennas or fewer APs equipped with many antennas, one observes an inherent trade-off between the beamforming and macro-diversity gains that has not been investigated in the literature. Given a total number of antenna elements and total downlink power, under a channel model that takes into account a probability of Line-of-Sight (LoS) as a function of the distance between the User Equipments (UEs) and APs, our numerical results show that there exists a ``sweet spot" on the optimal number of APs and of antenna elements per AP which is a function of the physical dimensions of the coverage area.Comment: 6 pages, 3 figures. Manuscript submitted to the IEEE Wireless Communications and Networking Conference (WCNC) 2024, Dubai, United Arab Emirate

Self-Learning Detector for the Cell-Free Massive MIMO Uplink: The Line-of-Sight Case

The precoding in cell-free massive multiple-input multiple-output (MIMO) technology relies on accurate knowledge of channel responses between users (UEs) and access points (APs). Obtaining high-quality channel estimates in turn requires the path losses between pairs of UEs and APs to be known. These path losses may change rapidly especially in line-of-sight environments with moving blocking objects. A difficulty in the estimation of path losses is pilot contamination, that is, simultaneously transmitted pilots from different UEs that may add up destructively or constructively by chance, seriously affecting the estimation quality (and hence the eventual performance). A method for estimation of path losses, along with an accompanying pilot transmission scheme, is proposed that works for both Rayleigh fading and line-of-sight channels and that significantly improves performance over baseline state-of-the-art. The salient feature of the pilot transmission scheme is that pilots are structurally phase-rotated over different coherence blocks (according to a pre-determined function known to all parties), in order to create an effective statistical distribution of the received pilot signal that can be efficiently exploited by the proposed estimation algorithm.Comment: Paper accepted for presentation in IEEE SPAWC 2020 - 21st IEEE International Workshop on Signal Processing Advances in Wireless Communications. {\copyright} 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other use