This document provides updates to IEEE Std 802.16's MIB for the MAC, PHY and asso- ciated management procedures in order to accommodate recent extensions to the standard.

IEEE Standard for Local and Metropolitan Area Networks Part 16: Air Interface for Fixed Broadband Wireless Access Systems Draft Amendment: Management Information Base Extensions

A Cell-Free Massive MIMO (multiple-input multiple-output) system comprises a very large number of distributed access points (APs), which simultaneously serve a much smaller number of users over the same time/frequency resources based on directly measured channel characteristics. The APs and users have only one antenna each. The APs acquire channel state information through time-division duplex operation and the reception of uplink pilot signals transmitted by the users. The APs perform multiplexing/de-multiplexing through conjugate beamforming on the downlink and matched filtering on the uplink. Closed-form expressions for individual user uplink and downlink throughputs lead to max–min power control algorithms. Max–min power control ensures uniformly good service throughout the area of coverage. A pilot assignment algorithm helps to mitigate the effects of pilot contamination, but power control is far more important in that regard. Cell-Free Massive MIMO has considerably improved performance with respect to a conventional small-cell scheme, whereby each user is served by a dedicated AP, in terms of both 95%-likely per-user throughput and immunity to shadow fading spatial correlation. Under uncorrelated shadow fading conditions, the cell-free scheme provides nearly fivefold improvement in 95%-likely per-user throughput over the small-cell scheme, and tenfold improvement when shadow fading is correlated.

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Cell-Free Massive MIMO Versus Small Cells

Cyclostationarity: half a century of research

A Cell-Free Massive MIMO (multiple-input multiple-output) system comprises a very large number of distributed access points (APs)which simultaneously serve a much smaller number of users over the same time/frequency resources based on directly measured channel characteristics. The APs and users have only one antenna each. The APs acquire channel state information through time-division duplex operation and the reception of uplink pilot signals transmitted by the users. The APs perform multiplexing/de-multiplexing through conjugate beamforming on the downlink and matched filtering on the uplink. Closed-form expressions for individual user uplink and downlink throughputs lead to max-min power control algorithms. Max-min power control ensures uniformly good service throughout the area of coverage. A pilot assignment algorithm helps to mitigate the effects of pilot contamination, but power control is far more important in that regard. 
Cell-Free Massive MIMO has considerably improved performance with respect to a conventional small-cell scheme, whereby each user is served by a dedicated AP, in terms of both 95%-likely per-user throughput and immunity to shadow fading spatial correlation. Under uncorrelated shadow fading conditions, the cell-free scheme provides nearly 5-fold improvement in 95%-likely per-user throughput over the small-cell scheme, and 10-fold improvement when shadow fading is correlated.

Cell-Free Massive MIMO versus Small Cells

We examine and compare the different types of linear transmit processing for multiple input, multiple output systems, where we assume that the receive filter is independent of the transmit filter contrary to the joint optimization of transmit and receive filters. We can identify three filter types similar to receive processing: the transmit matched filter, the transmit zero-forcing filter, and the transmit Wiener filter. We show that the transmit filters are based on similar optimizations as the respective receive filters with an additional constraint for the transmit power. Moreover, the transmit Wiener filter has similar convergence properties as the receive Wiener filter, i.e., it converges to the matched filter and the zero-forcing filter for low and high signal-to-noise ratio, respectively. We give closed-form solutions for all transmit filters and present the fundamental result that their mean-square errors are equal to the errors of the respective receive filters, if the information symbols and the additive noise are uncorrelated. However, our simulations reveal that the bit-error ratio results of the transmit filters differ from the results for the respective receive filters.

Linear transmit processing in MIMO communications systems

Remarkable research activities and major advances have been occurred over the past decade in multiuser multiple-input multiple-output (MU-MIMO) systems. Several transmission technologies and precoding techniques have been developed in order to exploit the spatial dimension so that simultaneous transmission of independent data streams reuse the same radio resources. The achievable performance of such techniques heavily depends on the channel characteristics of the selected users, the amount of channel knowledge, and how efficiently interference is mitigated. In systems where the total number of receivers is larger than the number of total transmit antennas, user selection becomes a key approach to benefit from multiuser diversity and achieve full multiplexing gain. The overall performance of MU-MIMO systems is a complex joint multi-objective optimization problem since many variables and parameters have to be optimized, including the number of users, the number of antennas, spatial signaling, rate and power allocation, and transmission technique. The objective of this literature survey is to provide a comprehensive overview of the various methodologies used to approach the aforementioned joint optimization task in the downlink of MU-MIMO communication systems.

An Overview on Resource Allocation Techniques for Multi-User MIMO Systems

Multicell cooperation is a promising solution for cellular wireless systems to mitigate intercell interference, improve system fairness, and increase capacity. In this article, we propose power allocation techniques for the downlink of distributed, precoded, multicell cellular-based systems. The precoder is designed in two phases: first the intercell interference is removed by applying a set of distributed precoding vectors; then the system is further optimized through power allocation. Three centralized power allocation algorithms with per-BS power constraint and different complexity trade-offs are proposed: one optimal in terms of minimization of the instantaneous average bit error rate (BER), and two suboptimal. In this latter approach, the powers are computed in two phases. First, the powers are derived under total power constraint (TPC) and two criterions are considered, namely, minimization of the instantaneous average BER and minimization of the sum of inverse of signal-to-noise ratio. Then, the final powers are computed to satisfy the individual per-BS power constraint. The performance of the proposed schemes is evaluated, considering typical pedestrian scenarios based on LTE specifications. The numerical results show that the proposed suboptimal schemes achieve a performance very close to the optimal but with lower computational complexity. Moreover, the performance of the proposed per-BS precoding schemes is close to the one obtained considering TPC over a supercell.

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Power allocation strategies for distributed precoded multicell based systems

The distributed antenna system concept promises to enhance the capacity and diversity of next-generation wireless communication networks, due to the inherently added micro and macro diversity. In this article we first give an overview of the main benefits of a DAS in relation to a collocated antenna system. Next we study the sum-capacity scaling of a multi-user DAS with the number of jointly processed transmit antennas in the downlink. In a practical system this scaling will have implications on the number of antennas worth jointly processing, since the costs of processing an additional antenna can be higher than the additional benefits obtained. Results show that the most important system property to attain the highest capacity gains is symmetry, and the users that attain the maximum gain are those at cell borders. They also confirm that the main DAS feature that makes possible its gains over the CAS architecture are the additional degrees of freedom/diversity provided by such an architecture, which increase the probability of finding a system state with high symmetry and of each user being near one of the transmit antennas.

/pdf/distributed-antenna-system-capacity-scaling-coordinated-and-1is3zpi276.pdf

Distributed antenna system capacity scaling [Coordinated and Distributed MIMO]

The increasing demand of wireless services associated with the scarcity of the radio spectrum and the trend to provide end-to-end QoS in emerging and future applications calls for the design of spectrally efficient systems with QoS support. To fulfill these two requirements of spectral efficiency and QoS provision in the highly dynamic environment of mobile radio requires the collaboration of several layers in the system as well as the use of multiple transmit and receive antennas. In a packet network, one important component to achieve the aforementioned efficiency goals is a properly designed scheduling algorithm. Using an information-theoretic framework, we present an overview of the issues associated with the design of packet scheduling algorithms and review several proposed solutions. The article focuses on the advantages of a cross-layer approach to the resource allocation problem and identifies the trade-offs associated with the increased signaling needs. Also, we thoroughly discuss the additional degrees of freedom that multiple transmit and receive antennas can provide

Atilio Gameiro

Papers

An Overview on Resource Allocation Techniques for Multi-User MIMO Systems

Power allocation strategies for distributed precoded multicell based systems

Distributed antenna system capacity scaling [Coordinated and Distributed MIMO]

Cross-layer scheduling for multi-user MIMO systems

Long-term behavior of lime-metakaolin pastes at ambient temperature and humid curing condition