Per-user Throughput Research Articles

Impact of Multi-Connectivity on Channel Capacity and Outage Probability in Wireless Networks

Multi-connectivity facilitates higher throughput, shorter delay, and lower outage probability for a user in a wireless network. Considering these promises, a rationale policy for a network operator would be to implement multi-connectivity for all of its users. In this paper, we investigate whether the promises of multi-connectivity also hold in such a setting where all users of a network are connected through multiple links. In particular, we consider a network where every user connects to its k closest base stations. Using a framework of stochastic geometry and probability theory, we obtain analytic expressions for per-user throughput and outage probability of $k$-connectivity networks under several failure models. In contrast to the conclusions of previous research, our analysis shows that per-user throughput decreases with increasing k. However, multi-connected networks are more resilient against failures than single connected networks as reflected with lower outage probability and lead to higher fairness among the users. Consequently, we conclude that rather than implementing multi-connectivity for all users, a network operator should consider it for its users who would benefit from additional links the most, e.g., cell edge users.

Resource Allocation for Reconfigurable Intelligent Surface Assisted Dual Connectivity.

The next generation 6G wireless systems are envisioned to have higher reliability and capacity than the existing cellular systems. The reconfigurable intelligent surfaces (RISs)-assisted wireless networks are one of the promising solutions to control the wireless channel by altering the electromagnetic properties of the signal. The dual connectivity (DC) increases the per-user throughput by utilizing radio resources from two different base stations. In this work, we propose the RIS-assisted DC system to improve the per-user throughput of the users by utilizing resources from two base stations (BSs) in proximity via different RISs. Given an -fair utility function, the joint resource allocation and the user scheduling of a RIS-assisted DC system is formulated as an optimization problem and the optimal user scheduling time fraction is derived. A heuristic is proposed to solve the formulated optimization problem with the derived optimal user scheduling time fractions. Exhaustive simulation results for coverage and throughput of the RIS-assisted DC system are presented with varying user, BS, blockage, and RIS densities for different fairness values. Further, we show that the proposed RIS-assisted DC system provides significant throughput gain of 52% and 48% in certain scenarios when compared to the existing benchmark and DC systems.

Efficient User-Serving Scheme in the User-Centric Cell-Free Massive MIMO System.

A cell-free massive multiple input multiple output (MIMO) system is an attractive network model that is in the spotlight in 5G and future communication systems. Despite numerous advantages, the cell-free massive MIMO system has a problem in that it is difficult to operate in reality due to its vast amount of calculation. The user-centric cell-free massive MIMO model has a more feasible and scalable benefit than the cell-free massive MIMO model. However, this model has the disadvantage that as the number of users in the area increases, there are users who do not receive the service. In this paper, the proposed scheme creates connections for unserved users under a user-centric scheme without additional access point (AP) installation and disconnection for existing users. A downlink user-centric cell-free massive MIMO system model in which the APs are connected to the central processing unit (CPU) and the APs and users are geographically distributed is considered. First, the downlink spectral efficiency formula is derived and applied to the user-centric cell-free massive MIMO system. Then, the proposed scheme and power control algorithm are applied to the derived formula. The simulation results show that the unserved users within the area disappear by using the proposed scheme, while the bit error rate (BER) performance and sum rate improve compared to the existing scheme. In addition, it is shown that the proposed scheme works well even with a very large number of users in the area, and a significant service performance improvement for the worst 10% of users and the overall improvement of per-user throughput for the bottom 70% of users are ensured.

Multi-Point Coordination in Massive MIMO Systems With Sectorized Antennas

Non-cooperative cellular massive MIMO, combined with power control, is known to lead to significant improvements in per-user throughput compared with conventional LTE technology. In this paper, we investigate further refinements to massive MIMO, first, in the form of three-fold sectorization, and second, coordinated multi-point operation (with and without sectorization), in which the three base stations cooperate in the joint service of their users. For these scenarios, we analyze the downlink performance for both maximum-ratio and zero-forcing precoding and derive closed-form lower-bound expressions on the achievable rate of the users. These expressions are then used to formulate power optimization problems with two throughput fairness criteria: ${i}$ ) network-wide max-min fairness, and ii ) per-cell max-min fairness. Furthermore, we provide centralized and decentralized power control strategies to optimize the transmit powers in the network. We demonstrate that employing sectorized antenna elements mitigates the detrimental effects of pilot contamination by rejecting a portion of interfering pilots in the spatial domain during channel estimation phase. Simulation results with practical sectorized antennas reveal that sectorization and multi-point coordination combined with sectorization lead to more than $1.7\times $ and $2.6\times $ improvements in the 95%-likely per-user throughput, respectively.

Delay Optimal Concurrent Transmissions With Raptor Codes in Dual Connectivity Networks

Dual connectivity (DC) has been emphasized in both LTE and 5G networks to utilize a secondary evolved NodeB (SeNB) connected to the master evolved NodeB (MeNB) that can simultaneously serve the user equipment (UE) to improve the per-user throughput as well as the mobility support. However, the non-ideal X2 link between the MeNB and SeNB and the dynamic channel condition can cause severe out-of-order packet arrival problem when transmitting data to the UE via MeNB and SeNB concurrently, which leads to excessive delay and requires non-trivial effort to do the de-jitter queueing and retransmission. In this paper, we propose a Raptor codes based dual connectivity (RCDC) scheme to solve the out-of-order packet arrival problem with the reduced delivery delay. The source packets at the MeNB are coded and separately transmitted to the UE through the MeNB and SeNB. Due to the unique recover capability of Raptor codes, the UE can decode the original data if enough encoded packets are received from either the MeNB or SeNB, and thus the out-of-order problem can be effectively eliminated without a dedicated de-jitter process. Mathematical models are developed to analyze the delay performance and simulation results are provided to demonstrate that the proposed scheme can solve the out-of-order packet arrival problem with significantly reduced delay comparing with the conventional DC scheme.

Channel Modeling and Performance Analysis of Airplane-Satellite Terahertz Band Communications

Wireless connectivity in airplanes is becoming more important, demanded, and common. One of the largest bottlenecks with the in-flight Internet is that the airplane is far away from both the satellites and the ground base stations during most of the flight time. Maintaining a reliable and high-rate wireless connection with the airplane over such a long-range link thus becomes a challenge. Microwave frequencies allow for long link distances but lack the data rate to serve up to several hundreds of potential onboard customers. Higher bands in the millimeter-wave spectrum (30 GHz–300 GHz) have, therefore, been utilized to overcome the bandwidth limitations. Still, the per-user throughput with state-of-the-art millimeter-wave systems is an order of magnitude lower than the one available with terrestrial wireless networks. In this paper, we take a step further and study the channel characteristics for the terahertz band (THz, 0.3 THz–10 THz) in order to map the feasibility of this band for aviation. We first propose a detailed channel model for aerial THz communications taking into account both the non-flat Earth geometry and the main features of the frequency-selective THz channel. We then apply this model to estimate the characteristics of aerial THz links in different conditions. We finally determine the altitudes where the use of airplane-to-satellite THz connection becomes preferable over the airplane-to-ground THz link. Our results reveal that the capacity of the airborne THz link may reach speeds ranging from 50–150 Gbps, thus enabling cellular-equivalent data rates to the passengers and staff during the entire flight.

Max-Min Power Control in Downlink Massive MIMO With Distributed Antenna Arrays

In this paper, we investigate optimal downlink power allocation in massive multiple-input multiple-output (MIMO) networks with distributed antenna arrays (DAAs) under correlated and uncorrelated channel fading. In DAA massive MIMO, the base station (BS) consists of multiple antenna sub-arrays. Notably, the antenna sub-arrays are deployed in arbitrary locations within a DAA massive MIMO cell. Consequently, the distance-dependent large-scale propagation coefficients are different from a user to these different antenna sub-arrays, which makes power control a challenging problem. We assume that the network operates in time-division duplex mode, where each BS obtains the channel estimates via uplink pilots. Based on the channel estimates, the BSs perform maximum-ratio transmission in the downlink. We then derive a closed-form signal-to-interference-plus-noise ratio (SINR) expression, where the channels are subject to correlated fading. Based on the SINR expression, we propose a networkwide max-min power control algorithm to ensure that each user in the network receives a uniform quality of service. Numerical results demonstrate the performance advantages offered by DAA massive MIMO. For some specific scenarios, DAA massive MIMO can improve the average per-user throughput up to 55%. Furthermore, we demonstrate that channel fading covariance is an important factor in determining the performance of DAA massive MIMO.

Towards Finite File Packetizations in Wireless Device-to-Device Caching Networks

We consider wireless device-to-device (D2D) caching networks with single-hop transmissions. Previous work has demonstrated that caching and coded multicasting can significantly increase per user throughput. However, the state-of-the-art coded caching schemes for D2D networks are generally impractical because content files are partitioned into an exponential number of packets with respect to the number of users if both library and memory sizes are fixed. In this paper, we present two combinatorial approaches of D2D coded caching network design with reduced packetizations and desired throughput gain compared to the conventional uncoded unicasting. The first approach uses a “hypercube” design, where each user caches a “hyperplane” in this hypercube and the intersections of “hyperplanes” represent coded multicasting codewords. In addition, we extend the hypercube approach to a decentralized design. The second approach uses the Ruzsa-Szemeredi graph to define the cache placement. Disjoint matchings on this graph represent coded multicasting codewords. Both approaches yield an exponential reduction of packetizations while providing a per-user throughput that is comparable to the state-of-the-art designs in the literature. Furthermore, we apply spatial reuse to the new D2D network designs to further reduce the required packetizations and significantly improve per user throughput for some parameter regimes.

BiLSTM Based Reinforcement Learning for Resource Allocation and User Association in LTE-U Networks

LTE-unlicensed (LTE-U) technology is a promising innovation to extend the capacity of cellular networks. The primary challenge for LTE-U is the fair coexistence between LTE systems and the incumbent WiFi systems. In this paper, we aim to maximize the long-term average per-user LTE throughput with long-term fairness guarantee by jointly considering resource allocation and user association on the unlicensed spectrum within a prediction window. We first formulate the problem as an NP-hard combinatorial optimization problem, then reformulate it as a non-cooperative game by applying the penalty function method. To solve the game, a novel reinforcement learning approach based on Bi-directional LSTM neural network is proposed, which enables small base stations (SBSs) to predict a sequence of future actions over the next prediction window based on the historical network information. It is shown that the proposed approach can converge to a mixed-strategy Nash equilibrium of the studied game and ensure the long-term fair coexistence between different access technologies. Finally, the effectiveness of the proposed algorithm is demonstrated by numerical simulation.

Elastic multi-connectivity in 5G networks

We study a self-organizing wireless network in which the number of active connections to the typical user equipment (UE) is dynamic, in order to sustain temporally varying network traffic. Using tools of stochastic geometry, we show that even though the signal coverage improves with a higher degree of connectivity, the per-user throughput may degrade. Consequently, the operator needs to reconfigure the UE association throughout the network as the UE density and the network requirements change. To optimize this trade-off in an online manner, we propose a reinforcement-learning (RL) based framework in which a given number of base station (BS) connections to the UE is modeled as an arm of the multi-armed bandit (MAB) problem. We propose a refresh-based Thompson sampling (TS) approach for the MAB problem and show that it is able to track the temporally optimal static association rule, in terms of signal coverage and throughput. Our study highlights that in the future fifth-generation (5G) and beyond wireless networks, elastic multi-connectivity must be employed in order to sustain agile quality-of-service (QoS) requirements and dynamic UE traffic.

Graph Coloring Based Pilot Assignment for Cell-Free Massive MIMO Systems

An efficient pilot assignment scheme based on graph coloring is proposed to mitigate the severe pilot contamination effect in cell-free massive multiple-input multiple-output systems. By exploiting the large-scale fading coefficients between the access points (APs) and the user equipments, we first utilize an AP selection algorithm to construct an interference graph. Then, the optimal pilot assignment can be achieved by updating the interference graph. In addition, we consider a fair policy for pilot reuse to improve the system performance. Numerical results verify that, compared with conventional pilot assignment schemes, the proposed graph coloring based pilot assignment scheme can significantly increase the 95 $\%$ -likely per-user throughput and reduce the complexity of assigning pilot sequences.

Machine Learning-Based Channel Prediction in Massive MIMO With Channel Aging

To support the ever increasing number of devices in massive multiple-input multiple-output (mMIMO) systems, an excessive amount of overhead is required for conventional orthogonal pilot-based channel estimation schemes. To circumvent this fundamental constraint, we design a machine learning (ML)-based time-division duplex scheme in which channel state information (CSI) can be obtained by leveraging the temporal channel correlation. The presence of the temporal channel correlation is due to the stationarity of the propagation environment across time. The proposed ML-based predictors involve a pattern extraction implemented via a convolutional neural network, and a CSI predictor realized by an autoregressive (AR) predictor or an autoregressive network with exogenous inputs recurrent neural network. Closed-form expressions for the user uplink and downlink achievable spectral efficiency and average per-user throughput are provided for the ML-based time division duplex schemes. Our numerical results demonstrate that the proposed ML-based predictors can remarkably improve the prediction quality for both low and high mobility scenarios, and offer great performance gains on the per-user achievable throughput.

Tabu-Search-Based Pilot Assignment for Cell-Free Massive MIMO Systems

In this correspondence, an efficient pilot assignment scheme based on tabu search (TS) is proposed to mitigate the severe pilot contamination effect caused by pilot reuse in cell-free massive multiple-input multiple-output systems. By exploiting the local neighborhood search, we first define the neighborhood and search objective function. Then, the tabu list is introduced to record the prohibited solutions and to avoid being trapped in the local optimum. In addition, we consider a fair policy for the pilot reused times to further improve the system performance. We propose a TS-based pilot assignment iterative algorithm to search the suboptimal pilot assignment result with low complexity. Numerical results verify that the proposed TS-based pilot assignment scheme can significantly improve the 95%-likely per-user throughput with similar complexity compared to the conventional random and greedy schemes.

KRAFT: Kalman Filter based Energy-aware LTE and Wi-Fi Joint Throughput Optimization

Resource block allocation in LTE is a complex metric consisting of available bandwidth, required data rate and channel state conditions. A Wi-Fi access point, co-operating with LTE eNB can service a single User Equipment (UE) through multiple interfaces (multihoming) for improving throughput. The challenge is to inter-operate the greedy LTE scheduler with deferral-based Wi-Fi band allocation. A joint scheduling therefore is a complex problem requiring a robust estimation method which not only optimize per-user throughput but also can support a minimum average system throughput condition. Kalman filter provides resource prediction while minimizing the mean square error. In this paper, we propose KRAFT, a novel LTE and Wi-Fi joint scheduler using Kalman filter. We periodically measure the discrete SINR values, available bandwidth and resource requirements to predict the system throughput optimized scheduling among Wi-Fi and LTE networks. Also, an energy-aware scheduling algorithm is proposed as a tie breaker between all the other approaches.

Dynamic Carrier-Sense Threshold Selection for Improving Spatial Reuse in Dense Wireless LANs

As density of a wireless LAN grows, per-user throughput degrades severely, deteriorating user experience. To improve service quality, it is important to increase system spectral efficiency. Controlling carrier-sense threshold is one of the key techniques to achieve the goal, because frequently transmissions are unnecessarily blocked by carrier sensing, even though these transmissions can take place without causing packet losses. Using high carrier-sense threshold and allowing nodes to transmit aggressively may increase the system throughput, but this approach can lead to unfair channel share and cause starvation for the edge nodes. In this paper, we propose a medium access control protocol where transmitters include the carrier-sense threshold required to protect its packet in the preamble. Nodes receiving the preamble only transmit concurrently, when they are confident that their own transmission as well as the on-going transmission will both be successfully received at the respective receivers. The simulation results show that this dual-threshold approach can achieve higher system throughput compared to using a single carrier-sense threshold, without penalizing edge nodes.

Location-Aware Self-Optimization for Interference Management in Ultra-Dense Small Cell Networks

To disperse traffic explosion in hotspot areas, the operators have been overlaying their formerly deployed legacy networks with small cells in a denser manner. In this letter, a location-aware self-optimization scheme is proposed, which improves per-user throughput by adjusting downlink transmission power offset based on the effective provision of positioning. The simulation results confirm that, without relying on discovery signals, the proposed scheme provides higher average SINR per user equipment than the small cell on/off control scheme with the discovery signal in 3GPP-LTE Standards, and the localized capacity is also improved.

Anticipatory Association for Indoor Visible Light Communications: Light, Follow Me!

In this paper, a radically new anticipatory perspective is taken into account when designing the user-to-access point (AP) associations for indoor visible light communications (VLC) networks, in the presence of users’ mobility and wireless-traffic dynamics. In its simplest guise, by considering the users’ future locations and their predicted traffic dynamics, the novel anticipatory association prepares the APs for users in advance, resulting in an enhanced location- and delay-awareness. This is technically realized by our contrived design of an efficient approximate dynamic programming algorithm. More importantly, this paper is in contrast to most of the current research in the area of indoor VLC networks, where a static network environment was mainly considered. Hence, this paper is able to draw insights on the performance trade-off between delay and throughput in dynamic indoor VLC networks. It is shown that the novel anticipatory design is capable of significantly outperforming the conventional benchmarking designs, striking an attractive performance trade-off between delay and throughput. Quantitatively, the average system queue backlog is reduced from 15 to 8 [ms], when comparing the design advocated to the conventional benchmark at the per-user throughput of 100 [Mbps], in a $15\times 15\times 5$ [ $\text{m}^{3}$ ] indoor environment associated with $ 8\times 8$ APs and 20 users walking at 1 [m/s].

Hierarchical cache-aided transmission cooperation in 5G user-centric network: Performance analysis and design insights

In user-centric network (UCN), potential serving small base stations (SBSs) of user equipment (UE) form a dynamic SBS group (SBSG). Cache space and transmission resource of potential serving SBSs can be considered as an entity, but joint cache-level and transmission-level cooperation of UCN has not been analyzed in literature. This paper proposes dynamic hierarchical cache-aided transmission cooperation (CATC) in UCN, and derives semi-closed expressions of caching coverage, per-user throughput, and delay using stochastic geometry. Specifically, hierarchical CATC is dynamic since SBSG changes constantly, where UE utilizes increased aggregate cache space of potential serving SBSs by searching the contents in SBSG, and interference is coordinated among SBSs within SBSG. If not accessible in SBSG, the contents are searched in serving macro base station (MBS) and other SBSs managed by serving MBS to overcome limited cache size at SBSG. Moreover, SBSs cooperatively increase content diversity in the new content eviction policy (processed-least recently used (P-LRU)). The analysis reveals the tradeoff between coverage and throughput, for although hierarchical CATC increases hit ratio and caching coverage, per-user MBS throughput is constrained due to increased load. The analytical results are verified through simulations, and show that total and SBS caching coverage are improved by 53% and 81% respectively (when cache size of SBSs equals 10), compared to state-of-art user-centric transmission scheme.

Dual Connectivity for LTE-NR Cellular Networks: Challenges and Open Issues

The Dual Connectivity (DC) technology has gained a lot of momentum in the LTE Release 12 as a means to enhance the per-user throughput and provide mobility robustness. Some studies in the literature have discussed the interworking between the LTE and the air interface of the upcoming Fifth Generation (5G) in a DC scenario. That integration may provide some benefits to meet the high throughput, reliability and availability requirements of the 5G networks. This work firstly presents a brief overview of the DC technology considering the integration between 4G e 5G. Then, we highlight some open issues and challenges for future investigation involving Radio Resource Management (RRM) in such a scenario, which includes: (i) user-cell association, (ii) interaction between base stations and (iii) resource allocation. After that, we propose an extension of a utility-based resource allocation algorithm for DC scenarios. Finally, a performance evaluation is conducted in multi-Radio Access Technology (RAT) LTE-NR scenarios considering the bearer split configuration of the DC technology, where the gains provided by the proposed algorithm are demonstrated.

Precoding and Power Optimization in Cell-Free Massive MIMO Systems

Cell-free Massive multiple-input multiple-output (MIMO) comprises a large number of distributed low-cost low-power single antenna access points (APs) connected to a network controller. The number of AP antennas is significantly larger than the number of users. The system is not partitioned into cells and each user is served by all APs simultaneously. The simplest linear precoding schemes are conjugate beamforming and zero-forcing. Max-min power control provides equal throughput to all users and is considered in this paper. Surprisingly, under max-min power control, most APs are found to transmit at less than full power. The zero-forcing precoder significantly outperforms conjugate beamforming. For zero-forcing, a near-optimal power control algorithm is developed that is considerably simpler than exact max-min power control. An alternative to cell-free systems is small-cell operation in which each user is served by only one AP for which power optimization algorithms are also developed. Cell-free Massive MIMO is shown to provide five- to ten-fold improvement in 95%-likely per-user throughput over small-cell operation.