A Novel Solution to Improve Network Coverage and Capacity with EasyMacro

Complementing macro-only cellular networks with low-powered base stations is a promising deployment solution to improve both network coverage and capacity, and cope with exploding data traffic in the coming years. In Beyond 3G Networks, such as LTE-Advanced, Relay Nodes and micro base stations can transmit on the same spectrum as the overlaying macro layer, and guarantee higher spatial reuse through cell splitting. Differently from previous research studies, this paper specifically aims at evaluating and comparing the potential of LTE relay and micro deployment in a realistic metropolitan scenario. A heuristic deployment algorithm which combines network coverage and realistic spatial user density information is also proposed. The results show that for the downlink, in-band relays can be deployed to improve network coverage, but not substantially the network capacity due to the limitation of the wireless backhaul link. In-band micro deployment, on the other hand, is the best solution to boost downlink network capacity (up to 5 times), while also providing full network coverage.

This paper investigates the capacity of the cooperative cellular networks and proposes to use the 2-D nested deployment on the base stations. Further analysis of sum-rate capacity and spectrum efficiency shows that this approach provides significant improvement. The study leverages the traditional model of regarding the coverage of base stations as the hexagonal blocks and views each hexagon as a macro-cell. One of the theoretical contributions of this paper is to use the invariance in the difference co-array to cover the derive the entire set of the channel fading coefficients. Furthermore, this paper proves the validity of using the average sum-rate capacity to calculate the capacity of the networks in both Rayleigh fading and non-fading channels. We also use simulations to confirm the correctness of the proposed theories from another perspective. As a result, the proposed approach remarkably improves the capacity of the network with the same amount of base stations.

Ultra-dense Heterogeneous networks (UDHN) are emerging as the inevitable approach to cope with the imminent cellular network capacity crunch. However, load imbalance and widely disproportionate SINR distribution between macro and small cells, remains the key hurdle in harnessing the full potential of UDHN. In this paper we address this problem by proposing and analysing a novel load-aware user association methodology that offers a mechanism to simultaneously optimize network capacity, load distribution and coverage. The solution concurrently leverages the three key optimization parameters for Coverage and Capacity Optimization (CCO) and Load Balancing (LB) SON functions i.e. antenna tilts, transmit powers and cell individual offsets (CIOs). The method incorporates exponential-weighting based prioritization of CCO and LB SON functions within the user association process. The results suggest that the proposed approach offers a distribution of load between macro and small cells that yields more gain in terms of both network capacity and user quality of service than conventional max signal strength or max SINR based association methods.

Wireless cellular networks have many parameters that are normally tuned upon deployment and re-tuned as the network changes. Many operational parameters affect reference signal received power (RSRP), reference signal received quality (RSRQ), signal-to-interference-plus-noise-ratio (SINR), and, ultimately, throughput. In this paper, we develop and compare two approaches for maximizing coverage and minimizing interference by jointly optimizing the transmit power and downtilt (elevation tilt) settings across sectors. To evaluate different parameter configurations offline, we construct a realistic simulation model that captures geographic correlations. Using this model, we evaluate two optimization methods: deep deterministic policy gradient (DDPG), a reinforcement learning (RL) algorithm, and multi-objective Bayesian optimization (BO). Our simulations show that both approaches significantly outperform random search and converge to comparable Pareto frontiers, but that BO converges with two orders of magnitude fewer evaluations than DDPG. Our results suggest that data-driven techniques can effectively self-optimize coverage and capacity in cellular networks.

This paper presents verifications for UMTS coverage and capacity in a live network using planning and performance tools applied to a real-world operational UMTS network. Case study results are presented and followed by comparisons between coverage predictions, capacity simulations, field measurements and system statistics. Tuned propagation models based on area clutter, population profiles, digital height maps and base station (BS) engineering elements are taken into account to match the outputs of the planning tool to the practical drive-test and UMTS Terrestrial Radio Access Network (UTRAN) system Key Performance Indicators (KPIs) measurements. This work outcome addresses the importance of using planning tools for dimensioning coverage and capacity aspects in UMTS networks and draw some attention to network future optimization, load balancing and congestions relief in advance before system traffic statistics are being generated or afterwards as supplementary grounds for network optimization and maintenance that support statistics.

Wireless cellular networks are large-scale networks in which asymptotic capacity investigation is no longer a cliche. A substantial body of work has been carried out to improve the capacity of cellular networks by introducing ad hoc communications, resulting in the so-called multihop cellular networks. Most of the previous research allows ad hoc transmissions between certain source and destination pairs to alleviate base stations' relay burden. However, since reports show that Internet data traffic is becoming more and more dominant in cellular networks, we explore in this paper the capacity of multihop cellular networks with all traffic going through base stations and ad hoc transmissions only acting as relay. We first investigate the capacity of regular multihop cellular networks where both nodes and base stations are regularly placed. By fully exploiting the link rate variability, we find that multihop cellular networks can have higher per-node throughput than traditional cellular networks by a scaling factor of log 2 n. Then, for the first time we extend our study to the capacity of heterogeneous multihop cellular networks where nodes are distributed according to a general Inhomogeneous Poisson Process and base stations are randomly placed. We show that under certain conditions multihop cellular networks can also outperform traditional cellular networks by a scaling factor of log 2 n. Moreover, both throughput-fairness and bandwidth-fairness are considered as fairness constraints for both kinds of networks.

A Mining Model for the Worst Plot in the Mobile Communication Networks

Mobile networks are now growing quickly due to major advancements in wireless technology especially with the introduction of the Fifth Generation New Radio (5G NR). A greater risk of exposure to electromagnetic field radiation (EMF) is being raised by the widespread deployment of base stations (BSs). Standard guidelines are set to control the amount of EMF radiation. This paper proposed a design model to de-concentrate and reduce the total exposure of the multi-technology BS with no drawbacks on network coverage level and key performance indicators (KPIs). The proposed solution applies the concept of weighted antenna’s azimuth to spread the total exposure by separating the antennas in the same sector. A set of simulations is carried out to calculate the reduction in total exposure ratio (TER) and the Compliance Distance (CD). Also, A field measurement test was done in a life network to validate and evaluate the proposed model under real conditions. Furthermore, the network operation support system (OSS) records were analyzed to evaluate the impact on the network coverage and capacity behavior. The pre- and post-results demonstrate that using the proposed model enhanced the CD and TER., the results show using two azimuths reduces the CD by 23% and by 43.4% when using six antennas. Also, the field test result demonstrated a 19.23% reduction in the Total Exposure Ratio. Overall, the system records show no significant impacts were registered on network coverage level and capacity performance for the sites involved in the test.

In satellite networks, information can be transmitted either directly by inter-satellite links or the movement of satellites carrying. Consequently, how to quantify network capacity, considering both the carrying and transmission capability of satellites is crucial to the deployment of satellite networks. In this paper, we define the capacity of satellite networks consisting of both, and propose a strategy to exploit the mobile carrying of satellites under the constraint of service requirements. Then, we reveal the theoretical relationship between satellite carrying and the network capacity. The theoretical analysis and simulated results show that 1) satellite carrying can improve the network capacity when the service delay constraints could be released; 2) The capacity gain from satellite carrying is influenced by network parameters, such as orbital altitude, number of satellites, and storage capacity.

One of the promising solutions to meet the demands for ubiquitous coverage and high capacity in heterogeneous cellular networks (HCNets) is the co-deployment of small base stations (sBSs) inside the coverage region of a macro base station BS. Due to its high transmit power, most of the users get associated with BS. This results in imbalanced load distribution across the HCNets. To balance the load, users from BS are offloaded to low power sBSs to decrease the BS load and increase network capacity. However, BS now acts as a strong interferer for offloaded users. Therefore, a proactive interference mitigation technique together with load balancing is required to mitigate the interference and balance the network load. In this paper, we use the soft frequency reuse (SFR) scheme to abate such interference. Furthermore, we use cell range expansion (CRE) based cell association where coverage regions of sBSs are increased by using cell association bias to include more users. Moreover, we derive coverage probability expressions for the proposed model. Numerical results demonstrate that the SFR scheme together with association bias reduce interference and balance network load distribution.

The volume of data in broadband cellular network is growing exponentially. However, studies have indicated the traffic load on the cellular base stations varies significantly over time. This gives an opportunity to accommodate additional traffic with the same network capacity if some of the traffic (e.g., p2p, cloud sync) can be amenable to `delayed scheduling' without hurting the user experience any significantly. In this paper, we study various algorithmic problems that can arise in this context. Using a model where all flows can have certain flexibility in scheduling (via use of a `deadline'), we develop optimal or near-optimal algorithms to determine the minimum network capacity for two different models. We also develop various semi-online and online algorithms for online scheduling of flows, and analyze their performance. In particular, even though the online scheduling problem is shown to be intractable, our proposed semi-online algorithm can schedule flows optimally if aided by historical data and slightly additional network capacity over the optimal. Finally, using flow level traffic traces collected at the core of a commercially operated cellular network, we evaluate the effectiveness of our techniques. Evaluations show that delayed scheduling, when done efficiently (using an offline optimal algorithm), can accommodate the same traffic with much lower network capacity (up to 50% less) with only modest delays. While such an optimal solution needs an offline approach, we demonstrate that online scheduling can be almost equally effective when historical traffic data can be exploited for estimation purposes.

Virtual radio access networks (RANs) is the candidate solution for 5G access networks, the concept of virtualised radio resources completing the virtual RAN paradigm. This paper proposes a new analytical model for the management of virtual radio resources in full heterogeneous networks. The estimation of network capacity and data rate allocation are the model’s two main components. Based on the probability distribution of the signal-to-interference-plus-noise-ratio observed at the user terminal, the model leads to the probability distribution for the total network data rate. It considers different approaches for the estimation of the total network data rate, based on different channel qualities, i.e., optimistic, realistic and pessimistic. The second component uses the outcome of the first one in order to maximise the weighted data rate subject to the total network capacity, the SLAs (service level agreements) of Virtual Network Operators (VNOs), and fairness. The weights for services in the objective function of the resource allocation component enable the model to have prioritisation among services. The performance of the proposed model is evaluated in a practical heterogeneous access network. Results show an increase of 2.5 times in network capacity by implementing an access point at the centre of each cell of a cellular network. It is shown that the cellular network capacity itself can vary from 0.9 Gbps in the pessimistic approach up to 5.5 Gbps in the optimistic one. Finally, the isolation of service classes and VNOs by means of virtualisation of radio resources is clearly demonstrated.

Relaying is one of the key techniques adopted by third-generation partnership project long-term evolution (LTE) advanced as part of 4G cellular technologies, aiming to increase coverage and capacity of networks especially for the edge nodes. The authors have considered the uplink scheduling of LTE networks with the help of positioned relay nodes which have fixed routing configuration. The entire problem is projected as a constrained convex optimisation formulation and for the solution purpose, subgradient method is adopted. Revealing the guiding principles of optimal solution, a few suboptimal scheduling algorithms are proposed to allocate resource blocks across all nodes with the help of existing work. Deploying a large number of relays may not be useful to basic user nodes, and hence, the proposed schemes are adaptive and have ability to distinguish useful relays from not-useful ones. In addition to system throughput maximisation, for ensuring fairness across user nodes, the authors have proposed scheduling techniques which are the outcome of Nash bargaining solution. Numerical calculations and results have been shown to justify that relay nodes can potentially improve system's performance at low load, whereas at high load they remain inactive because of their inability to contribute.

Cooperative communication has been shown to provide significant increase of transmission reliability and network capacity while expanding coverage in cellular networks. The present work is devoted to the investigation of the end-to-end performance and power allocation of a maximum-ratio-combining based regenerative multi-relay cooperative network over non-homogeneous scattering environment, which is the realistic case in many practical wireless communication scenarios. Novel analytic expressions are derived for the end-to-end symbol-error-rate of both M -ary phase-shift keying and M -ary quadrature amplitude modulation over independent and non-identically distributed generalized fading channels are given by exact analytic expressions that involve the Lauricella function and can be readily evaluated with the aid of a proposed computing algorithm. Simple analytic expressions are also derived for the corresponding symbol-error-rate at asymptotically high signal-to-noise ratios. The derived expressions are corroborated with respective results from computer simulations and are subsequently employed in formulating a sum-power optimization problem that enhances the system performance under total sum-power constraint within the multi-relay cooperative system. It is also shown that asymptotically optimum power allocation provides substantial performance gains over the corresponding equal power allocation, particularly, when the source-relay and relay-destination paths are highly unbalanced.

Two main concerns for designing a wireless system are more network capacity and less energy consumption. Recently, distributed antenna system (DAS) has received considerable attention due to its potential to provide higher spectral efficiency (SE) and uniform coverage for cellular networks. In this regard, this paper compares the performance of DAS with centralized antenna system (CAS) in LTE-A system in terms of energy efficiency (EE), where practical restrictions such as out-of-cell interference, path loss, and small scale fading are taken into account. Furthermore, the EE and system power consumption are investigated under three different cell-load scenarios (high, moderate, and low load) where different numbers of antennas are activated and remaining of antennas are under sleep mode. Finally, based on the tradeoff between power-saving and EE, two optimal DAS antenna deployments are proposed for low and moderate cell-load scenarios. The results reveal that DAS considerably outperforms CAS in terms of EE and by optimal deploying antennas of DAS significant power-saving and EE are achievable. The proposed methodology achieved savings of up to 27.63% in terms of energy savings in a macrocell with guarantee of a high capacity of data.