Bidirectional dynamic network for asymmetry traffic with massive MIMO

In this paper, we find the optimum lengths of T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">UL</sub> , T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DL</sub> and B i.e. the uplink (UL) and downlink (DL) pilots and feedback bits respectively maximizing the sum of the lower bounds of the UL and the DL rates. With the aid of T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">UL</sub> symbols, the Base Station (BS) estimates the UL channel of each user. T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DL</sub> symbols are means of estimating the downlink channel, which after being normalized and quantized with B bits, is made available at the BS. Estimated Channel State Information (CSI) and quantized Channel Direction Information (CDI), which may be received erroneously, are thus available at the BS for computing the receive and the transmit Zero-forcing (ZF) filters, respectively. The use of ZF enables the derivation of analytical DL and UL mean squared error (MSE) expressions that give a lower bound on the DL and UL rates, respectively, needed for our optimization.

In this paper, we consider a dynamic time division duplex system based on a cloud radio access network (RAN) to support the ultimate goal of efficient resource utilization. To optimize the performance of both remote radio heads (RRHs) and user equipments (UEs) which have multiple antennas, we propose an iterative scheme consisting of four types of beamforming, namely downlink (DL) transmit beamforming, DL receive beamforming, uplink (UL) transmit beamforming, and UL receive beamforming, in an attempt to minimize mean squared error (MSE). The proposed beamforming scheme makes it possible to allow DL and UL transmission be supported simultaneously by managing interference between RRHs and UEs. Our simulation results show that the MSE and the bit error rate (BER) are greatly improved under the proposed beamforming algorithm compared to conventional schemes. From intensive system level simulations, the proposed algorithm points out more than 3 dB performance improvement in terms of the required signal-to-noise-ratio (SNR) to achieve the BER less than $10^{-3}$ compared to conventional schemes.

Full duplex (FD) communication has emerged as an attractive solution for increasing the network throughput, by allowing downlink (DL) and uplink (UL) transmissions in the same spectrum. However, only employing FD base stations in heterogeneous cellular networks (HCNs) cause coverage reduction, due to the DL and UL interferences as well as the residual loop interference. We, therefore, propose HCNs with half duplex massive multiuser multiple-input multiple-output macrocell base stations (MBSs) to relax the coverage reduction, and FD small cell base stations (SBSs) to improve spectrum efficiency. A tractable framework of the proposed system is presented, which allows to derive exact and asymptotic expressions for the DL and the UL rate coverage probabilities, and the DL and the UL area spectral efficiencies (ASEs). Monte Carlo simulations confirm the accuracy of the analytical results, and it is revealed that the equipping massive number of antennas at MBSs enhances the DL rate coverage probability, whereas increasing FD SBSs increases the DL and the UL ASEs. The results also demonstrate that by tuning the UL fractional power control, a desirable performance in both UL and DL can be achieved.

In this paper, we focus on the downlink (DL) and uplink (UL) power control issue in a wireless full-duplex system consisting of a full-duplex base station with single antenna and mobile stations working in time-division duplex mode. First, we investigate the signal-to-interference ratios (SIRs) of DL and UL and obtain a formula reflecting their relationship. Then, by employing the Lagrange multiplier method, we derive an optimal joint power control solution to maximize the sum rate of DL and UL channels. Consequently, we propose an efficient switching scheme between the full-duplex mode and the opportunistic half-duplex mode that can be considered as a compensation mode in sum rate maximization. The proposed switching scheme is verified by numerical simulations.

Massive multiple input multiple output (MIMO) and full duplex (FD) communication are being considered as potential candidates for the spectrum efficient 5G wireless networks. In this paper, we develop a tractable model for downlink (DL) and uplink (UL) transmission in K-tier heterogeneous cellular networks (HCNs) with massive MIMO macrocells and full duplex (FD) small cells for spectrum efficiency. In the considered HCNs, the performance of the mobile user (MU) is limited by several sources of interference, specifically due to FD nature of small cell base stations (SBSs). A stochastic geometry based model of the proposed HCNs is provided which allows to derive the DL and UL rate coverage probabilities of such a system. Monte Carlo simulations confirm the accuracy of the analytical results, while numerical results reveal that equipping large number of MIMO antennas at macro base stations (MBSs) enhances the DL rate coverage probability of a random MU in HCNs. The results show that to achieve the maximum joint DL and UL performance gain in HCNs with FD small cells, both SBSs' density and SBSs' transmit power should be optimized. Moreover, the UL performance can be improved by decreasing the SBSs receivers sensitivity and increasing the UL power control factor.

Downlink power control in C-RAN enabled full duplex cellular networks

In traditional half-duplex (HD) communication systems, the HD base station (BS) can transmit artificial noise (AN) to jam the eavesdroppers for securing the downlink (DL) communication. However, guaranteeing uplink (UL) transmission is not possible with an HD BS because HD BSs cannot jam the eavesdroppers during UL transmission. In this chapter, we investigate the resource allocation algorithm design for secure multiuser systems employing a full-duplex (FD) BS for serving multiple HD DL and UL users simultaneously. In particular, the FD BS transmits AN to guarantee the concurrent DL and UL communication security. We propose a multi-objective optimization framework to study two conflicting yet desirable design objectives, namely total DL transmit power minimization and total UL transmit power minimization. To this end, the weighted Tchebycheff method is adopted to formulate the resource allocation algorithm design as a multi-objective optimization problem (MOOP). The considered MOOP takes into account the quality-of-service (QoS) requirements of all legitimate users for guaranteeing secure DL and UL transmission in the presence of potential eavesdroppers. Thereby, secure UL transmission is enabled by the FD BS, which would not be possible with an HD BS. Although the considered MOOP is non-convex, we solve it optimally by semidefinite programming (SDP) relaxation. Simulation results not only unveil the trade-off between the total DL transmit power and the total UL transmit power, but also confirm that the proposed secure FD system can guarantee concurrent secure DL and UL transmission and provide substantial power savings over a baseline system.

Concurrent self-organization of coverage and capacity in both downlink (DL) and uplink (UL) is an ambitious task since 1) Coverage and capacity are conflicting key performance indicators (KPIs), and 2) an adequate DL performance does not necessarily entail an adequate UL performance because the DL and UL interference scenarios are fundamentally different. However, considering the UL when self-organizing the network is crucial because the UL transmission becomes more important due to the emergence of new applications and services, such as social networking, video calls, or sensor networks, which require either parity between DL and UL traffic or even more UL than DL traffic. In this paper, we propose a general concept for the self-organization of multiple KPIs while having only very sparse knowledge about the network. Using this concept, we propose an effective tilt-based algorithm that manages to jointly optimize coverage and capacity in DL and UL. In an urban scenario with real Long-Term Evolution (LTE) site locations, we were able to increase the cell-edge user throughput by 70% (DL) and 24% (UL) while concurrently decreasing the number of uncovered users by 21% (DL) and 25% (UL), compared with a well-chosen reference tilt setting.

This paper aims at studying user association for uplink (UL) transmission in cooperative heterogeneous cellular networks, where small cell sum rate is limited by the backhaul (BH) capacity. The investigated architecture can be modeled as a multiple-access relay channel (MARC) channel where multiple users communicate with a final destination (the macro base station) with the help of a relay (the small cell base station). A network-coding-based MARC, which exploits both the direct and relayed transmissions, is introduced to deal with low-capacity BH. We assess the UL average system capacity for 1) the baseline association based on the strongest reference signal received power (RSRP) in downlink (DL) and 2) the optimized user association chosen to improve the overall network UL sum-rate. Our results show that the network-coding-based MARC scheme achieves significant gains with respect to the pure relaying strategy. We further show that the optimized user association provides significant gains in network UL sum-rate with respect to the RSRP association, for the same sum power constraint and without degrading DL performance, especially when the BH capacity is sufficiently large.

In this paper, we propose a novel method for efficient implementation of a massive Multiple-Input Multiple- Output (massive MIMO) system with Frequency Division Duplexing (FDD) operation. Our main objective is to reduce the large overhead incurred by Downlink (DL) common training and Uplink (UL) feedback needed to obtain channel state information (CSI) at the base station. Our proposed scheme relies on the fact that the underlying angular distribution of a channel vector, also known as the angular scattering function, is a frequency-invariant entity yielding a ULDL reciprocity and has a limited angular support. We estimate this support from UL CSI and interpolate it to obtain the corresponding angular support of the DL channel. Finally we exploit the estimated support of the DL channel of all the users to design an efficient channel probing and feedback scheme that maximizes the total spectral efficiency of the system. Our method is different from the existing compressed-sensing (CS) based techniques in the literature. Using support information helps reduce the feedback overhead from O(s logM) in CS techniques to O(s) in our proposed method, with s andM being sparsity order of the channel vectors and the number of base station antennas, respectively. Furthermore, in order to control the channel sparsity and therefore the DL common training and UL feedback overhead, we introduce the novel concept of active channel sparsification. In brief, when the fixed pilot dimension is less than the required amount for reliable channel estimation, we introduce a pre-beamforming matrix that artificially reduces the effective channel dimension of each user to be not larger than the DL pilot dimension, while maximizing both the number of served users and the number of probed angles. We provide numerical experiments to assess the performance of our method and compare it with the state-of-the-art CS technique.

This paper proposes a joint beamforming and resource allocation scheme to maximize the minimum capacity for wireless powered communication network (WPCN) designed for multi-user distributed massive multi-input multi-output (DM-MIMO) system with full-duplex radio remote heads (RRHs), which enable simultaneous uplink (UL) and downlink (DL) communications. The minimum capacity is maximized by a proposed algorithm based on the non-negative matrix theory. The simulation results have demonstrated that the achievable max-min capacity of the DM-MIMO is considerably higher compared to that of the CMMIMO system. It has also showed that the convergence speed of the proposed algorithm is so fast.

Downlink (DL) and uplink (UL) decoupling (DUDe)<br> is a new architectural paradigm where DL and UL are not<br> constrained to be associated to the same base station (BS). Thus,<br> a user having access to multiple BSs within a dense cellular<br> network can receive the DL traffic from one BS and send its<br> UL traffic through another. Building upon this architectural<br> paradigm, the present paper provides tight analytical bounds<br> in closed form for the UL ergodic capacity that depend solely<br> on the density of the infrastructure. The devised bounds account<br> for the backbone network congestion and the synchronization of<br> the acknowledgments of the decoupled channels. The proposed<br> bounds are compared against extensive numerical simulations<br> demonstrating the tractability and accuracy of the expressions.<br> Index Terms—Downlink uplink decoupling; ergodic capacity;<br> bound; network density; acknowledgment synchronization.

In this paper, we analyze the performance of a cloud radio access network (CRAN), consisting of multiple randomly distributed remote radio heads (RRHs) and a macro base station (MBS), each equipped with multiple antennas. To model the spatial distribution of RRHs and analyze its performance, we use stochastic geometry tools. In contrast to previous works on CRAN that consider Poisson Point Process (PPP) model for the spatial distribution of RRHs, we consider a more realistic Matern hard-core point process (MHCPP) model that imposes a certain minimal distance (referred to as hard-core distance) between the two RRHs so that the RRHs are not too close to each other. To compare system performance of CRAN when different transmission strategies are used, three RRH selection schemes are adopted including 1) the best RRH selection (BRS); 2) all RRHs participation (ARP); and 3) nearest RRH selection (NRS). Considering downlink transmission, the ergodic capacity, outage probability, and system throughput of CRAN are analytically characterized for different RRH selection schemes. The presented results demonstrate that compared to PPP model, the increase in hard-core distance will result in a higher outage probability and cause a negative impact on ergodic capacity. Furthermore, when the same total transmit power is consumed, BRS scheme provides the best outage performance while ARP scheme is the best RRH selection scheme when the same transmit SNR at each RRH is assumed. Moreover, it is shown that the hard-core distance has a more significant impact on systems with higher intensity of PPP distributed candidate points and in large hard-core distance regime increasing the intensity of candidate points can only provide a small improvement in outage performance. We extend our work to multiuser case with zero-forcing (ZF) precoding where it is proven that the results in multiuser case reduce to the derived results in this work by substituting K=1 for single-user.

Different from public 4G/5G networks that are dominated by downlink (DL) traffic, emerging 5G non-public networks (NPNs) need to support significant uplink (UL) traffic to enable emerging applications such as industrial Internet of things (IIoT). The UL-DL spectrum sharing is becoming a viable solution to enhance the UL throughput of NPNs, which allows NPNs to perform the UL transmission over the time-frequency resources configured for DL transmission in coexisting public networks. To deal with the severe interference from the DL public base station (BS) transmitter to the coexisting UL non-public BS receiver, we propose an adaptive asymmetric successive interference cancellation (SIC) approach, in which the non-public BS is enabled to have the capability of decoding the DL signals transmitted from the public BS and cancelling them for interference mitigation. In particular, this paper studies a basic UL-DL spectrum sharing scenario when a UL non-public BS and a DL public BS coexist in the same area, each communicating with multiple users via orthogonal frequency-division multiple access (OFDMA). Under this setup, we aim to maximize the common UL throughput of all non-public users, under the condition that the DL throughput of each public user is above a certain threshold. The decision variables include the subcarrier allocation and user scheduling for both non-public and public BSs, the receiver mode of the non-public BS over subcarriers, as well as the rate and power control. Numerical results show that the proposed design significantly improves the common UL throughput as compared to benchmark schemes without such consideration.

SummaryHeterogeneous networks (HetNets) are a practical solution for traffic offloading from a high powered base station (HBS) to a low powered base station (LBS). In the HetNets, uplink (UL)–downlink (DL) decoupled access (UDDa) strategy is an optimal solution that ensures cell association, independently, in the UL and DL. This strategy offloads HBS cell edge mobile device (MD) to the nearby LBS in the UL. However, energy‐efficient cell association for traffic offloading from HBS to LBS, relay, or device to device (D2D) in the UL employing UDDa strategy has not been explored in the past work. We formulate mathematical models which ensure energy‐efficient cell association, power allocation, and traffic offloading employing traditional UL‐DL coupled access (UDCa) and UDDa strategies in the HetNets. The formulated problems are concave fractional programming (CFP) problems. The CFP is changed to a concave optimization problem using Charnes–Cooper transformation (CCT). The transformed problems are solved using an outer approximation algorithm to obtain optimal solution. Simulation results show the effectiveness of UDDa strategy over UDCa strategy in terms of MDs association, traffic offloading in the UL and DL, interference mitigation in the UL, data rate, and energy efficiency in the HetNets.