The next-generation of wireless communication is anticipated to integrate sensing and communication using a unified yet versatile waveform, capable of providing not only reliable data communication, but also localization and sensing functionality. Given the chirp-based structure of the recently proposed orthogonal chirp division multiplexing (OCDM), it has shown robustness against frequency selectiveness for communication but also suitability for sensing because of the inherent pulse compression property of the chirps. Although OCDM-based spectral resolution algorithms have been explored, polynomial root-based multiple signal classification (MUSIC) poses the issues of high-dimensionality and correct estimation assignment, especially for highly correlated multiple targets. In this paper, a computationally efficient two-dimensional high-resolution algorithm is proposed for joint range-velocity estimation for the OCDM-based Radar-Communication (RadCom) system. The proposed frequency domain radar processing for OCDM utilizes the eigen-decomposition property of the Fresnel transform through a reduced polynomial-based root MUSIC algorithm, in conjunction with Gaussian pulses, to jointly localize synchronized targets. Polynomial dimensionality has been reduced by transforming the nonlinear polynomial into a linear eigenvalue problem. The eigen structure of the signal subspace is utilized to resolve the issue of estimation assignment for highly correlated multiple targets by minimizing the residual projection error. The numerical results for the proposed chirp-based pilot sensing demonstrate a significant improvement in sidelobes reduction over the typical 2D-FFT and 2D-MUSIC approach, for uncorrelated and highly correlated targets. Moreover, the algorithm avoids 2D exhaustive search as in conventional 2D-MUSIC, meanwhile, reducing the computational overhead of traditional OCDM radar processing.

In this paper, a novel Deep Q-Network (DQN) based scheduling method to optimize delay time and fairness among entanglement requests in quantum repeater networks is proposed. The scheduling of requests determines which pairs of end nodes should be entangled during the current time slot, while other pairs are placed in a queue for future slots. However, existing research on quantum networking often relies on simple statistical models to capture the behavior of quantum hardware, such as the failure rate of establishing entanglement. Moreover, current quantum simulators do not support network behaviors, including handling, pending, and dropping requests. To bridge the gap between quantum deployments and network behaviors, in this paper a dynamic network model is presented, encompassing quantum simulations, random topologies, and user modeling. The DQN based scheduling scheme allows us to balance the conflicting objectives of minimizing delay time and maximizing fairness among these entanglement requests. The proposed technique was evaluated using simulations, with results showing that the proposed DQN achieves higher performance compared to Greedy, Proportional fair and FIFO scheduling schemes.

In this paper, a joint optimal allocation of transmit power at the source and jamming power at the destination is proposed to maximize the average secrecy energy efficiency (SEE) of a wireless network within a finite time duration. The destination transmits the jamming signal to improve secrecy by utilizing full-duplex capability. The source and destination both have energy harvesting (EH) capability with limited battery capacity. Due to the Markov nature of the system, the problem is formulated as a finite-horizon reinforcement learning (RL) problem. We propose the finite-horizon joint power allocation (FHJPA) algorithm for the finite-horizon RL problem and compare it with a low-complexity greedy algorithm (GA). An infinite-horizon joint power allocation (IHJPA) algorithm is also proposed for the corresponding infinite-horizon problem. A comparative analysis of these algorithms is carried out in terms of SEE, expected total transmitted secure bits, and computational complexity. The results show that the FHJPA algorithm outperforms the GA and IHJPA algorithms due to its appropriate modelling in finite horizon transmission. When the source node battery has sufficient energy, the GA can yield performance close to the FHJPA algorithm despite its low-complexity. When the transmission time horizon increases, the accuracy of the infinite-horizon model improves, resulting in a reduced performance gap between FHJPA and IHJPA algorithms. The computational time comparison shows that the FHJPA algorithm takes 16.6 percent less time than the IHJPA algorithm.

Unmanned Aerial Vehicles (UAVs) are becoming an indispensable tool in civilian and military data-gathering and reconnaissance tasks, due to their ability to freely fly over a mission area and collect data. In this paper, we experimentally demonstrate how off-the-shelf Software-Defined Radio receivers can be used as part of a UAV payload, to create a cost-effective system to locate the origin of a radio transmitter. A uniform linear array of antennas is used to measure the angle-of-arrival of the signal, which is then combined with the GPS coordinates of the UAV and processed with an Unscented Kalman Filter algorithm to estimate the location of the transmitter. We demonstrate this system in field conditions, and show that the system can accurately estimate the angle-of-arrival. We demonstrate that a low-power handset can be located to within 100m from over 1.5km away within several minutes of flight. While our experiment is based around a search and rescue scenario, this localisation approach has wider applicability for wireless communications applications.

Wireless connectivity will soon enable people to consume augmented and virtual reality content anywhere and cloud-connected mobile robots to perform complex tasks collaboratively. This connectivity will come to enterprises, factory floors and digital homes first, powered by indoor networks that offer much higher data rates, greater reliability, and lower latency than today’s networks. In addition to providing traditional communication capabilities, the future indoor network will have a “sixth sense” that enables it to provide sensory information and insights to meet physiological needs such as lighting, heating, health and safety. It will serve as the core infrastructure for smart buildings and help enterprises operate more efficiently by further enabling capabilities such as immersive virtual workplaces, indoor navigation and asset tracking. This paper reviews current technologies, examines the enabling technologies of the future indoor network and presents our latest research results and our vision for implementing them in commercial and residential environments.

Unmanned aerial vehicles (UAVs) are now beginning to be deployed for enhancing the network performance and coverage in wireless communication. However, due to the limitation of their on- board power and flight time, it is challenging to obtain an optimal resource allocation scheme for the UAV-assisted Internet of Things (IoT). In this paper, we design a new UAV-assisted IoT system relying on the shortest flight path of the UAVs while maximising the amount of data collected from IoT devices. Then, a deep reinforcement learning-based technique is conceived for finding the optimal trajectory and throughput in a specific coverage area. After training, the UAV has the ability to autonomously collect all the data from user nodes at a significant total sum-rate improvement while minimising the associated resources used. Numerical results are provided to highlight how our techniques strike a balance between the throughput attained, trajectory, and the time spent. More explicitly, we characterise the attainable performance in terms of the UAV trajectory, the expected reward and the total sum-rate.

Wireless backhaul communication and power provision to fifth-generation small cells (SCs) is expected to decrease their installation cost significantly. In this paper, hybrid solar/laser-based energy harvesting and storage are investigated for the self-sufficient year-round operation of outdoor SCs. The required electrical power for a SC is assumed to be 10 W according to state-of-the-art millimeter-wave phased-array transceivers. Here we show through simulations that hybrid solar/laser-based energy harvesting with storage and exclusive laser-based energy harvesting enable the SC operation not only in sunnier parts of the world but also in ‘darker’ places. Since the highest monthly power required from the laser is shown to be 10.1 W, a highly directive optical wireless link is designed using the simulation software Zemax. The operation wavelength of 1310 nm is selected because of the highest maximum permissible exposure of 1 W/cm2. The designed system is shown to be capable of harvesting 10.4 W at the distance of 100 m with transmitter-receiver dimensions of the order of cm3and meeting eye-safety regulations for Class 1M.

MPTCP is used in Multi-WAN Routers to aggregate multiple WAN/Internet connections using two architectural variants: proxying and tunneling. The proxy variant creates one MPTCP connection for each TCP connection, resulting in a large number of parallel uncoordinated MPTCP connections, which leads to underutilizating the available capacity, suboptimal scheduling, and increased loss rate. The tunnel variant encapsulates TCP over MPTCP, stacking two reliability layers, which leads to large number of spurious retransmissions, an issue known as TCP meltdown. We propose a new solution, BOOST, that eliminates the problems with both variants by multiplexing TCP connections over a single persistent multi-path connection. BOOST also takes a hybrid approach to multi-path scheduling combining load balancing and scheduling by transmitting short flows across a single path, avoiding HoL blocking, and opportunistically transmitting long flows across multiple path, utilizing left-over capacity. Evaluations show that BOOST provides better throughput, lower losses, and retransmissions compared to MPTCP

In addition to high coverage and capacity, localization capabilities are becoming increasingly important for WLAN access points as enabler for additional services. In this paper the joint optimization of coverage and localization is investigated using a switched directional antenna array configuration. It is shown that switched directional antenna arrays can significantly outperform omni-directional antenna arrays in both coverage and angle estimation. In addition, it is shown that optimizing the antenna array for coverage also provides near-optimal localization performance. This is of great practical importance, as coverage optimization does not require manual calibration based on ground truth, and a good joint solution can be autoconfigured easily.

In this paper we consider the problem of computing the capacity of multi-user Gaussian MIMO systems under multiple linear transmit covariance constraints (LTCCs). These LTCCs are general enough to include many transmit power constraints such as sum power constraint (SPC) or per-antenna power constraint (PAPC) as special cases. For the considered MIMO systems with multiple LTCCs, existing solutions are based on subgradient or gradient descent methods, which are known to have slow convergence in general and are therefore not applicable to massive MIMO systems. In contrast, we propose a low-complexity semi-closed-form approach to computing the MIMO capacity for the system of interest. To this end, the considered problem in the broadcast channel is transformed into an equivalent minimax problem in the multiple access channel. The special structure of the minimax problem allows us to derive water-filling-like algorithms based on a novel combination of alternating optimization and concave-convex procedure. For the important case of joint SPC and PAPC, we also propose analytical expressions to find the optimal covariance matrix. Extensive analytical and numerical results are provided to demonstrate the effectiveness of our approach under various massive MIMO system settings.

In this work, we consider a joint optimisation of real-time deployment and resource allocation scheme for UAV-aided relay systems in emergency scenarios such as disaster relief and public safety missions. In particular, to recover the network within a disaster area, we propose a fast K-means-based user clustering model and jointly optimal power and time transferring allocation which can be applied in the real system by using UAVs as flying base stations for real-time recovering and maintaining network connectivity during and after disasters. Under the stringent QoS constraints, we then provide centralised and distributed models to maximise the energy efficiency of the considered network. Numerical results are provided to illustrate the effectiveness of the proposed computational approaches in terms of network energy efficiency and execution time for solving the resource allocation problem in real-time scenarios. We demonstrate that our proposed algorithm outperforms other benchmark schemes.

Next-generation wireless networks are expected to enable new applications with strict latency constraints. However, existing transport layer protocols are unable to meet the stringent Quality of Service (QoS) requirements on throughput and maximum latency: excessive queuing due to capacity-oriented congestion control inflates end-to-end latency well beyond interactivity deadlines. In this work, we propose a novel framework that evolves best-effort communications into reliability- and latency-aware communications for QoS-sensitive applications. The new protocol, named High-reliability latency-bounded Overlay Protocol (HOP), provides a novel combination of packet-level Forward Error Correction (FEC) and multipath scheduling to compensate for capacity drops and meet pre-defined QoS requirements. More specifically, the sender splits the data and the associated redundancy between the paths by using a stochastic forecast of their future capacity and decides the amount of redundancy necessary to meet the application’s requirements without clogging the connections. We compare HOP’s performance with state-of-the-art multipath protocols in ns-3 simulations using both synthetic and live network traces, and confirm that our scheme can reliably deliver high-throughput data, reducing the number of late blocks by 2 to 5 times with respect to optimized Multipath TCP (MPTCP).

Grammar-Guided Genetic Programming is already outperforming humans at creating efficient transmission schedulers for large heterogeneous communications networks. We have previously proposed a multi-level grammar approach which achieved significantly better results than the canonical Grammar-Guided Genetic Programming approach. Initially, a restricted `small' grammar is utilised in order to discover suitable structures. A full grammar is then adopted after this initial phase. Hence, evolution can focus on maximising performance, by fine-tuning the well-structured models. In this work, we propose to use a hierarchical approach by employing multiple small grammars instead of a unique small grammar at the lower level, in conjunction with the full grammar at the upper level. To use multiple small grammars while maintaining the same computational budget, we have to use either (i) reduce the number of generations, or (ii) reduce the size of the population for the evolution with each of the small grammars. In this work, we confirm that the hierarchical grammar approach using the division of number of generations strategy achieves significantly better results than the multi-level approach, but requires defining an ideal number of small grammars to achieve the best performance. We also show that the hierarchical grammar approach using the division of population size strategy achieves significantly better results than the multi-level approach. However the division of population size strategy is less sensitive to the number of small grammars.

Delivering massive video content while consuming low energy at the user devices is an issue of increasing importance, both for the users and operators. In this paper we consider the problem of minimum-energy video delivery when the users are served by LTE multicast transmissions which are assisted by Wi-Fi networks. We formulate the optimization problem where we jointly decide the user association and spectrum allocation to multicast groups, and the Wi-Fi access point (AP) association and airtime allocation to users, in order to deliver a requested video quality. The problem is NP-hard and we propose a cutting planes algorithm, based on Benders decomposition, to expedite its exact solution. We evaluate our proposal using a wealth of simulation experiments based on 3GPP parameters and measurement studies. Our findings show that coordinated decisions on both networks reduce the devices power consumption by up to approximately 50%.

360° video streaming is rapidly progressing towards the multimedia industry, offering an interactive user experience but also challenging telco operators due to the huge volume of the video data and the user channel dynamics. We propose a software defined transport layer proxy overlay architecture, that builds on top of existing standard technologies, to facilitate hybrid multicast/unicast 360° video delivery as a service-particularly combining LTE multicast and WiFi unicast transmissions. In addition we holistically define the emergent optimization problem, where we jointly consider network association, multicast/unicast scheduling, and spectrum/bandwidth management in both networks, taking also into consideration user specific preferences, e.g., the popularity of the video tiles. The proposed framework is evaluated through a series of simulation based experiments following 3GPP standard parameters and real 360° video traces. Our findings reveal significantly increase in the user utility, particularly when the wireless spectrum is limited.

In this paper we consider the task of scheduling packet transmissions amongst multiple paths with uncertain, time-varying delay. We make the observation that the requirement is usually to transmit application layer objects (web pages, images, video frames etc) with low latency, and so it is the object delay rather than the per packet delay which is important. This has fundamental implications for multipath scheduler design. We introduce SOS (Stochastic Object-aware Scheduler), the first multipath scheduler that considers application layer object sizes and their relationship to link uncertainty. We show how to interface SOS with cwnd-based/ack-clocked congestion control (as usually used in TCP) and show up to 150% improvement in the 95% percentile and mean delay vs MPTCP/minRTT.

Indoor areas, such as offices and shopping malls, are a natural environment for initial millimeter-wave (mmWave) deployments. Although we already have the technology that enables us to realize indoor mmWave deployments, there are many remaining challenges associated with system-level design and planning for such. The objective of this article is to bring together multiple strands of research to provide a comprehensive and integrated framework for the design and performance evaluation of indoor mmWave systems. This article introduces the framework with a status update on mmWave technology, including ongoing fifth generation (5G) wireless standardization efforts and then moves on to experimentally validated channel models that inform performance evaluation and deployment planning. Together these yield insights on indoor mmWave deployment strategies and system configurations, from feasible deployment densities to beam management strategies and necessary capacity extensions.

Mobile network operators today need to deliver new types of multimedia content, such as 360° video, to an ever-growing population of users. This creates a pressing need for network mechanisms that can support such demanding services in an economically-efficient fashion. This work focuses on applications that deliver a video file concurrently to multiple users which can utilize different radio technologies, namely cellular and Wi-Fi networks. We propose a mechanism for the orchestration of LTE multicast and Wi-Fi unicast transmissions by optimizing jointly: the design of multicast groups, the spectrum and bandwidth management in both networks, and the video encoding quality for each user (or, group). We consider two key performance criteria: minimizing the utilized LTE spectrum resources, and maximizing the delivered video quality. We formulate the respective optimization problems, prove they are NP-hard, and design an exact algorithm for solving them in near-real time. We employ a wealth of simulation experiments, using 3GPP-compliant parameters, that compare our approach with state-of-the-art benchmarks. Our findings suggest that, in representative scenarios, this multi-RAT orchestration can save up 55% LTE radio resources and increase up to 46% the delivered video quality.

Despite the large literature on localization, there is no solution yet to localize a commercial off-the-shelf smartphone device using radio measurements from a single WiFi AP. We present SPRING, Smartphone Positioning with Radio measurements from a sINGle wifi access point. SPRING exploits Fine Time Measurements (FTM) and Angle of Arrival (AOA) extracted from commercial chipsets exploiting the specifications of the recent 802.11-2016 and the 802.11ac amendment to combine distance and direction from the AP to the client for positioning. Our system has the potential to bring indoor positioning to homes and small businesses which typically have a single access point. We exploit physical layer (PHY) information to detect the number of paths and their directions. We use this information to derive a new method for filtering ranging measurements obtained with the FTM protocol. We achieve sub-meter distance estimation accuracy eliminating the adverse effect of multipath in FTM using calibrated inputs from Channel State Information (CSI). Our evaluation in indoor scenarios in multipath rich environments demonstrates that the combination of AOA estimation and the proposed FTM refinement approach can locate a Google Pixel 3 smartphone with a median positioning error of 0.9-2.15 m through an area comparable to typical flat sizes.

Wireless communications networks are operating at breaking point during an era of relentless traffic growth. Network operators must utilize scarce and expensive wireless spectrum efficiently in order to satisfy demand. Spectrum on the links between cells and user equipments ('users': smartphones, tablets, etc.) frequently becomes congested. Capacity can be increased by transmitting data packets via multiple links. Packets can be routed through multiple Long Term Evolution (LTE) links in existing fourth generation (4G) networks. In future 5G deployments, users will be equipped to receive packets over LTE, WiFi, and millimetre wave links simultaneously.

Interactive cloud-based applications such as virtual reality telepresence require flawless data delivery well within interactivity deadlines. These can range from sub-ms intervals for machines-type communications in industrial settings (e.g., multi-view obstacle detection for mobile vehicles) to 100 ms for basic human-human interactivity (e.g., virtual office, remote cockpit). Yet in wireless networks serving mobile users, the support of any quality-of-service (QoS) constraints is challenging due to their inherent instability and best-effort nature. In this context, we propose a novel multi-connectivity framework that allows federating multiple wireless links such as 4G LTE, 5G New Radio, Wi-Fi and WiGig in a simple and backward-compatible manner, and use them for multi-path data delivery with controlled end-to-end throughput, latency and reliability. The proposal is based on software-defined transport-layer proxies that can be deployed instantaneously and upgraded on-demand with no need for any handset or network modifications. As a proof of concept, we evaluate several innovative protocols for user-centric bandwidth and latency control in real-life LTE and Wi-Fi networks.

Evolutionary computation is used to automatically evolve small cell schedulers on a realistic simulation of a 4G-LTE heterogeneous cellular network. Evolved schedulers are then further augmented by human design to improve robustness. Extensive analysis of evolved solutions and their performance across a wide range of metrics reveals evolution has uncovered a new human-competitive scheduling technique which generalises well across cells of varying sizes. Furthermore, evolved methods are shown to conform to accepted scheduling frameworks without the evolutionary process being explicitly told the form of the desired solution. Evolved solutions are shown to out-perform a human-engineered state-of-the-art benchmark by up to 50%. Finally, the approach is shown to be flexible in that tailored algorithms can be evolved for specific scenarios and corner cases, allowing network operators to create unique algorithms for different deployments, and to postpone the need for costly hardware upgrades.

With the ever increasing cell densities in wireless networks, it is desired to enable more self-X features, such as self-configuration. Setting the network parameters in an autonomous manner is not only more time efficient but also increases network performance and reduces the probability of a human error. Among numerous network settings, one of the key parameters that requires such autonomous configuration is the cell ID (CID). It is used during fundamental procedures, such as network access, decoding, or handover, and therefore, its configuration is crucial for network operation. A complete CID management framework together with a centralized method for CID assignment is presented in this paper. It is not only applicable to multiple mobile standards but is also compatible with multi-vendor equipment, since it is based on the TR-069 management protocol. The proposed approach mitigates CID conflicts when there is a high level of reuse. Moreover, in the 4G case it also prevents neighbors from using different CID but with the same reference signal pattern, which avoids significant interference. The proposed method is evaluated in an enterprise small cell scenario, and in comparison with the baseline third generation partnership project approach, significant reductions of assignment conflicts are demonstrated.

As the capacity and reliability of mobile networks increases, so does the demand for more responsive end-to-end services: applications such as augmented reality, live video conferencing, and smart or autonomous vehicles require reliable, throughput-intensive end-to-end communications with strict delay constraints. Only consistently reliable delivery of data flows well within human interactivity deadlines will enable a truly immersive user experience. To enable data delivery within pre-defined deadlines, controlled on demand by an application or its user, we propose and demonstrate a novel transport-layer protocol for explicit latency control called latency-controlled end-to-end aggregation protocol (LEAP). The LEAP splits a data flow with quality of service (QoS) constraints into multiple subflows that are delivered over multiple parallel links (e.g., Wi-Fi and LTE in a standard smartphone, WiGig, and 5G in the near future). The subflow data rates are set based on a novel proactive forecasting of the achievable channel capacity, subject to application-specific QoS constraints. Cross-path encoding and redundancy adaptation are then used to deliberately balance the trade-off between maximum throughput, required delay, and minimum reliability as function of application/user-specific input parameters. When compared to leading state-of-the-art transport protocols in live network experiments, LEAP exhibits a superior capacity to reliably provide a high and stable throughput with bounded latency, both in wired and wireless scenarios. The LEAP is also the first protocol to allow applications to explicitly set their priorities, giving them the freedom to set the operating point in the trade-off between throughput, latency, and reliability.

Traditional single-tiered wireless communications networks cannot scale to satisfy exponentially rising demand. Operators are increasing capacity by densifying their existing macro cell deployments with co-channel small cells. However, cross-tier interference and load balancing issues present new optimization challenges in channel sharing heterogeneous networks (HetNets). One-size-fits-all heuristics for allocating resources are highly suboptimal, but designingad hoccontrollers requires significant human expertise and manual fine-tuning. In this paper, a unified, flexible, and fully automated approach for end-to-end optimization in multi-layer HetNets is presented. A hill climbing algorithm is developed for reconfiguring cells in real time in order to track dynamic traffic patterns. Schedulers for allocating spectrum to user equipment are automatically synthesized using grammar-based genetic programming. The proposed methods for configuring the HetNet and scheduling in the time–frequency domain can addressad hocobjective functions. Thus, the operator can flexibly tune the tradeoff between peak rates and fairness. Far cell edge downlink rates are increased by up to 250% compared with non-adaptive baselines. Alternatively, peak rates are increased by up to 340%. The experiments illustrate the utility and future potential of natural computing techniques in software-defined wireless communications networks.

The fifth-generation (5G) air interface, namely, dynamic multiple access (MA) based on multiuser superposition transmission (MUST) and orthogonal MA (OMA), may require a complicated scheduling and heavy signaling overhead. To address these challenges, we propose a unified MA scheme for future cellular networks, which we refer to as structured MUST (S-MUST). In S-MUST, we apply complex power allocation coefficients (CPACs) over multiuser legacy constellations to generate a composite constellation. In particular, the in-phase (I) and quadrature (Q) components of the legacy constellation of each user are separately multiplied by those of the CPACs. As such, the CPACs offer an extra degree of freedom for multiplexing users and guarantee fairness in symmetric broadcast channels. This new paradigm of superposition coding allows us to design IQ separation at the user side, which significantly reduces the decoding complexity without degrading performance. Hence, it supports low-complexity frequency-selective scheduling that does not entail dynamical switching between MUST and OMA. We further propose to quantize the CPACs into complex numbers where I and Q components of each quantized coefficient are primes, facilitating parallel interference cancellation at each user via modulo operations; last but not least, we generalize the design of S-MUST to exploit the capabilities of multiantenna base stations. The proposed S-MUST exhibits an improved user fairness with respect to conventional MUST (134% spectral efficiency enhancement) and a lower system complexity compared with dynamically alternating MUST and OMA.

Small Cells are being deployed alongside pre-existing Macro Cells in order to satisfy demand during the current era of exponential growth in mobile traffic. Heterogeneous networks are economical because both cell tiers share the same scarce and expensive spectrum. However, customers at cell edges experience severe cross-tier interference in channel sharing Het-Nets, resulting in poor service quality. Techniques for improving fairness globally have been developed in previous works. In this paper, a novel method for service differentiation at the level of individual customers is proposed. The proposed algorithm redistributes spectrum on a millisecond timescale, so that premium customers experience minimum downlink rates exceeding a target threshold. System level simulations indicate that downlink rate targets of at least 1 [Mbps] are always satisfied under the proposed scheme. By contrast, naive scheduling achieves the 1 [Mbps] target only 83% of the time. Quality of service can be improved for premium customers without significantly impacting global fairness metrics. Flexible service differentiation will be key to effectively monetizing the next generation of 5G wireless communications networks.

In this paper, an efficient approach for weighted sum rate maximization (WSRMax) for zero-forcing (ZF) methods with general linear transmit covariance constraints (LTCCs) is proposed. This problem has been extensively studied separately for some special cases such as for sum power or per-antenna power constraint (PAPC). Due to some practical and regulatory requirements, these power constraints alone are not in general sufficient, which motivates the consideration of general LTCCs. On the other hand, the zero-forcing (ZF) is a simple linear precoding technique to mitigate inter-user interference. The problem of WSRMax for ZF methods with LTCCs was studied previously using a gradient descent algorithm with barrier functions, but this method was also shown to converge slowly. To derive an efficient solution to this problem, we first reformulate it as an equivalent minimax problem using Lagrangian duality. The obtained result in fact resembles BC-MAC duality but is specialized for ZF methods. We then combine alternating optimization and concave-convex procedure to efficiently compute a saddle point of the minimax problem. The proposed method is numerically shown to converge very fast and its complexity scales linearly with the number of users.

This paper presents an efficient approach to computing the capacity of multiple-input multiple-output (MIMO) channels under multiple linear transmit covariance constraints (LTCCs). LTCCs are general enough to include several special types of power constraints as special cases such as the sum power constraint (SPC), per-antenna power constraint (PAPC), or a combination thereof. Despite its importance and generality, most of the existing literature considers either SPC or PAPC independently. Efficient solutions to the computation of the MIMO capacity with a combination of SPC and PAPC have been recently reported, but were only dedicated to multiple-input single-output (MISO) systems. For the general case of LTCCs, we propose a low-complexity semi-closed-form approach to the computation of the MIMO capacity. Specifically, a modified minimax duality is first invoked to transform the considered problem in the broadcast channel into an equivalent minimax problem in the dual multiple access channel. Then alternating optimization and concave-convex procedure are utilized to derive water-filling-based algorithms to find a saddle point of the minimax problem. This is different from the state-of-the-art solutions to the considered problem, which are based on interior-point or subgradient methods. Analytical and numerical results are provided to demonstrate the effectiveness of the proposed low-complexity solution under various MIMO scenarios.

Providing consistently high wireless capacity is becoming increasingly important to support the applications required by future digital enterprises. In this paper, we propose Eigen-direction-aware ZF (EDA-ZF) with partial coordination among base stations (BSs) and distributed interference suppression as a practical approach to achieve this objective. We compare our solution with Zero Forcing (ZF), entailing neither BS coordination or inter-cell interference mitigation, and Network MIMO (NeMIMO), where full BS coordination enables centralized inter-cell interference management. We also evaluate the performance of said schemes for three sub-6 GHz deployments with varying BS densities — sparse, intermediate, and dense — all with fixed total number of antennas and radiated power. Extensive simulations show that: (i) indoor massive MIMO implementing the proposed EDA-ZF provides uniformly good rates for all users; (ii) indoor network densification is detrimental unless full coordination is implemented; (iii) deploying NeMIMO pays off under strong outdoor interference, especially for cell-edge users.

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 locationand delayawareness. 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 tradeoff 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 peruser throughput of 100 [Mbps], in a 15 × 15 × 5 [m3] indoor environment associated with 8 × 8 APs and 20 users walking at 1 [m/s].

We consider cellular base stations (BSs) equipped with a large number of antennas and operating in the unlicensed band. We denote such system as massive MIMO unlicensed (mMIMO-U). We design the key procedures required to guarantee coexistence between a cellular BS and nearby Wi-Fi devices. These include: neighboring Wi-Fi channel covariance estimation, allocation of spatial degrees of freedom for interference suppression, and enhanced channel sensing and data transmission phases. We evaluate the performance of the so-designed mMIMO-U, showing that it allows simultaneous cellular and Wi-Fi transmissions by keeping their mutual interference below the regulatory threshold. The same is not true for conventional listen-before-talk (LBT) operations. As a result, mMIMO-U boosts the aggregate cellular-plus-Wi-Fi data rate in the unlicensed band with respect to conventional LBT, exhibiting increasing gains as the number of BS antennas grows.

Genetic Algorithms (GAs) have been shown to be a very effective optimisation tool on a wide variety of problems. However, they are not without their drawbacks. GAs require time to run, and evolve a bespoke solution to the desired problem in real time. This requirement can prove to be prohibitive in a high-frequency dynamic environment where on-line training time is limited. Neural Networks (NNs) on the other hand can be trained at length off-line, before being deployed on-line, allowing for fast generation of solutions on demand. This study presents a hybrid approach to time-frame scheduling in a high frequency domain. A GA approach is used to generate a dataset of optimised human-competitive solutions. Deep Learning is then deployed to extract the underlying model within the GA, enabling fast optimisation on unseen data. This hybrid approach allows for NNs to generate GA-quality schedules on-line, almost 100 times faster than running the GA.

In this paper, we propose a new method for nonorthogonal multiuser downlink transmission, referred to as multiple-input multiple-output lattice partition multiple access (MIMO-LPMA). The new method is based on the combination of user selection, zero forcing beamforming, and LPMA, and it uses N base station antennas to deliver information to N clusters of M users each. The method thus accommodates a total of N ·M users on a given time/frequency resource in a non- orthogonal fashion. The user selection algorithm ensures that all users in each cluster are sufficiently aligned with their respective transmission beam. Zero forcing beamforming controls the inter-cluster crosstalk, ensuring that beams transmitted to different clusters do not interfere to one another. LPMA encoding and decoding allows to superimpose and separate multiple messages on the same beam. Simulations show that the proposed MIMO-LPMA method outperforms the conventional approach based on non-orthogonal multiple access (MIMO-NOMA) in terms of achievable rates. In particular, our results indicate that MIMO-LPMA guarantees a significantly larger 95%-likely rate.

We propose to operate massive multiple-input multiple-output (MIMO) cellular base stations (BSs) in unlicensed bands. We denote such systems as massive MIMO unlicensed (mMIMO-U) ones. We design the key procedures required at a cellular BS to guarantee coexistence with nearby Wi-Fi devices operating in the same band. In particular, spatial reuse is enhanced by actively suppressing interference toward neighboring Wi-Fi devices. Wi-Fi interference rejection is also performed during an enhanced listen-before-talk phase. These operations enable Wi-Fi devices to access the channel as though no cellular BSs were transmitting, and vice versa. Under concurrent Wi-Fi and BS transmissions, the downlink rates attainable by cellular user equipment (UEs) are degraded by the Wi-Fi-generated interference. To mitigate this effect, we select a suitable set of UEs to be served in the unlicensed band accounting for a measure of the Wi-Fi/UE proximity. Our results show that the so-designed mMIMO-U allows simultaneous cellular and Wi-Fi transmissions by keeping their mutual interference below the regulatory threshold. Compared with a system without interference suppression, Wi-Fi devices enjoy a median interference power reduction of between 3 dB with 16 antennas and 18 dB with 128 antennas. With mMIMO-U, cellular BSs can also achieve large data rates without significantly degrading the performance of Wi-Fi networks deployed within their coverage area.

Heterogeneous cellular networks are composed of macro cells (MCs) and small cells (SCs) in which all cells occupy the same bandwidth. Provision has been made under the third generation partnership project-long term evolution framework for enhanced intercell interference coordination (eICIC) between cell tiers. Expanding on previous works, this paper instruments grammatical genetic programming to evolve control heuristics for heterogeneous networks. Three aspects of the eICIC framework are addressed including setting SC powers and selection biases, MC duty cycles, and scheduling of user equipments (UEs) at SCs. The evolved heuristics yield minimum downlink rates three times higher than a baseline method, and twice that of a state-of-the-art benchmark. Furthermore, a greater number of UEs receive transmissions under the proposed scheme than in either the baseline or benchmark cases.

Network operators are struggling to cope with exponentially increasing demand. Capacity can be increased by densifying existing Macro Cell deployments with Small Cells. The resulting two-tiered architecture is known as a Heterogeneous Network or 'HetNet'. Significant inter-tier interference in channel sharing HetNets is managed by resource interleaving in the time domain. A key task in this regard is scheduling User Equipment to receive data at Small Cells. Grammar-based Genetic Programming (GBGP) is employed to evolve models that map measurement reports to schedules on a millisecond timescale. Two different fitness functions based on evaluative and instructive feedback are compared. The former expresses an industry standard utility of downlink rates. Instructive feedback is obtained by computing highly optimised schedules offline using a Genetic Algorithm, which then act as target semantics for evolving models. This paper also compares two schemes for mapping the GBGP parse trees to Boolean schedules. Simulations show that the proposed system outperforms a state of the art benchmark and is within 17% of the estimated theoretical optimum. The impressive performance of GBGP illustrates an opportunity for the further use of evolutionary techniques in software-defined wireless communications networks.

In this paper, we propose a new downlink non-orthogonal multiuser superposition transmission scheme for future 5G cellular networks, which we refer to as the lattice partition multiple access (LPMA). In this proposed design, the base station transmits multilevel lattice codes for multiple users. Each user's code level corresponds to a distinct prime and is weighted by a product of all distinct primes of the other users excluding its own. Due to the structural property of lattice codes, each user can cancel out the interference from the other code levels by using the modulo lattice operation in a successive/parallel manner. LPMA can provide better user fairness in symmctrical broadcast channels, compared with non- orthogonal multiple access (NOMA). We demonstrate that the proposed LPMA shows a clear throughput enhancement over the current NOMA scheme.

Self-configuration of network parameters is a desired network management feature, especially in dense small cell deployments. One of the key parameters that requires such autonomous set up is the cell ID. Its configuration to a large extent influences the network performance affecting such mechanisms as network access, decoding, handover, and is therefore of a very high importance. In this paper a new centralised method for Physical Layer Cell Identity (PCI) assignment is presented. The proposal is based on the network information available via the TR-069 management protocol, which makes it applicable also to multi-vendor deployments. In contrast with the existing schemes, the proposed algorithm mitigates not only the colliding and confusing assignments but also prevents neighbours from using different PCIs but introducing overlap in their reference signal pattern, which has not been addressed before. The results show a significant reduction of assignment conflicts when compared with the baseline 3rd Generation Partnership Project (3GPP) approach.

Location fingerprinting (LF) is an attractive localization technique which relies on existing infrastructures. The major drawback of LF is the requirement of having an updated fingerprint database. Gaussian Process (GP) is a non-parametric modeling technique which can be used to model the received signal strength indicator (RSSI) and create the fingerprint database based on few training data. In this paper we use a parametric pathloss model for the GP mean and a flexible non-parametric covariance function, so we can get reliable estimates with low fingerprinting effort. In our experiment, we show that with 23 fingerprint locations we perform as well as traditional fingerprinting with over 230 fingerprinted locations for an office space of 2500m2.

The use of small cells is a promising way of providing the required capacity in future cellular networks through densification. Self-organizing network (SON) techniques are required in small cells, particularly in femtocells where plug-and-play user deployments are used. However, these techniques have tended to be implemented by vendors with proprietary algorithms that use low-level UE measurements which are not easily accessible to network operators. This makes the management of femtocells in a multi-vendor environment difficult. In this paper, we present a centralized SON technique to perform load balancing, coverage optimization and manage sleep modes that utilizes high-level measurements made at the femtocells which are easily available through the widely used TR-069 device management protocol standard. This allows network operators the flexibility of implementing and coordinating the SON techniques in multi-vendor femtocell deployments. We show that, despite the limited amount of information used, the proposed solution is able to effectively optimize the coverage in order to balance load and manage sleep modes whilst ensuring that no coverage gaps occur.

In this paper, we design an energy efficient indoor visible light communications (VLC) system from a radically new perspective based on an amorphous user-to-network association structure. Explicitly, this intriguing problem is approached from three inter-linked perspectives, considering the cell formation, link-level transmission and system-level optimisation, critically appraising the related optical constraints. To elaborate, apart from proposing hitherto unexplored amorphous cells (A-Cells), we employ a powerful amalgam of asymmetrically clipped optical orthogonal frequency division multiplexing (ACO-OFDM) and transmitter pre-coding aided multi-input single-output (MISO) transmission. As far as the overall system-level optimisation is concerned, we propose a low-complexity solution dispensing with the classic Dinkelbach's algorithmic structure. Our numerical study compares a range of different cell formation strategies and investigates diverse design aspects of the proposed A-Cells. Specifically, our results show that the A-Cells proposed are capable of achieving a much higher energy efficiency per user compared to that of the conventional cell formation for a range of practical field of views (FoVs) angles.

Higher order sectorization (HOS), which splits macrocells into a larger number of smaller sectors, are receiving significant interest as a cost-effective means of improving network capacity. Potentially, the capacity gain with HOS is proportionally linear to the number of sectors per cell due to spatial reuse, but factors such as non-ideal antenna radiation patterns together with inter-cell interference can significantly reduce this capacity gain. We develop a statistical model to theoretically characterize the performance of HOS deployments in wireless networks using orthogonal frequency division multiple access. Moreover, a fractional frequency reuse scheme is considered, which aids to mitigate inter-cell interference. The model provides a fast and effective tool for studying network performance in terms of user signal quality, site throughput, and outage probability, and it can be used to speed up network planning and optimization. In addition, we consider the impact of user classification methods in the analysis, and propose a new spectrum efficiency-based user classification method that improves resource utilization and allocation fairness. Performance results indicate that the proposed model is accurate, and shows a diminishing performance gain of HOS deployments with the number of sectors. The proposed user classification method improves network performances with respect to the state-of-the-art approaches.

Grammatical Evolution (GE) is applied to the problem of load balancing in heterogeneous cellular network deployments (HetNets). HetNets are multi-tiered cellular networks for which load balancing is a scalable means to maximise network capacity, assuming similar traffic from all users. This paper describes a proof of concept study in which GE is used in a genetic algorithm-like way to evolve constants which represent cell power and selection bias in order to achieve load balancing in HetNets. A fitness metric is derived to achieve load balancing both locally in sectors and globally across tiers. Initial results show promise for GE as a heuristic for load balancing. This finding motivates a more sophisticated grammar to bring enhanced Inter-Cell Interference Coordination optimisation into an evolutionary framework.

In this work we investigate the reassignment of User Equipments (UEs) between adjacent small cells to concurrently enable spatial multiplexing gains through Multi-User MIMO (MU-MIMO) and reductions in energy consumption though switching emptied small cells to a sleep state. We consider a case where UEs can be reassigned between adjacent small cells provided that the targeted neighbouring cell contains a UE with which the reassigned UE can perform MU-MIMO without experiencing excessive multi-user interference, and whilst achieving a minimum expected gain in spectral efficiency over the previous original cell transmissions as a result. We formulate the selection decision of which UEs to reassign as a set covering problem with the objective of maximising the number of small cell base stations to switch to a sleep state. Our results show that, for both indoor and outdoor LTE small cell scenarios, the proposed MU-MIMO-based reassignments achieve significant reductions in the required number of active small cell base stations, whilst simultaneously achieving increases in spectral efficiency.

Co-location information of devices, people, and activities can be used in numerous applications in areas of social networking, mobile networking, spatial and socio-economics, and securing interactions. People co-location can be used to infer their communications and interactions. This information can be exploited for many purposes such as gaining understanding of human social interactions and behaviours. In this paper, we propose a real-time co-localization technique which provides accurate people co-location information with sub-meter accuracy. We construct a connectivity graph representing the potential colocated users based on pairwise similarity of RF measurements from user's mobile phones. We then apply community-detection tools to cluster users into co-located groups. Since our approach does not estimate the absolute location of individual users, it is robust to localization errors and protects the location privacy of mobile users. Our approach does not involve labour-intensive calibration as required for most localization approaches. We prototyped our proposed solution to detect co-located users in an enterprise building scenario. Android mobile users connected to our cloud localization server were accurately clustered according to their geographical proximity.

In system-level simulations, it is vital to appropriately model spatially correlated shadow fading, especially the auto-correlation, so as to emulate realistic scenarios. However, as the size of the network increases, the complexity of the straightforward approach involving the Cholesky decomposition grows exponentially. Alternatively, low-complexity oriented schemes to generate correlated shadow fading value (SFV) maps for large networks are more preferable. However, the existing schemes do not consider wrap-around, which should be taken into account in the generation of SFV maps to facilitate user equipment (UE) mobility simulation and avoid the reduced interference in the outer-rim region. In order to prevent discontinuous shadow fading across the border of the simulated area and the wrap-around area, new methods to efficiently generate SFV maps are required. In this paper, a novel scheme to generate SFV maps with wrap-around is proposed, and its significant accuracy is analysed using computer simulations.

The deployment of both outdoor and indoor Small Cell Base Stations (SCBSs) has attracted significant interest in the wireless industry. However, a critical concern in a large scale deployment is the efficient control of the small cell's transmission mode. In this paper we propose a load aware approach for dynamic idle mode selection where the load distribution is estimated by the “RF Fingerprints” of the users. Our approach allows pilot signals and most of the processing power of a SCBS to be completely switched off when no active user is in its vicinity, or when the required Quality of Service (QoS) can be provided by the underlying macro base stations. Such an approach significantly reduces the energy consumption of the small cells as well as reducing the pilot pollution and signalling overhead. We evaluate the efficiency of our approach using field measurements in central Dublin as well as with system-level simulations. We show that the proposed method is capable of identifying idle cells with an average prediction error rate of 1.9%. Moreover we show that it has the potential to achieve an average reduction of 90% in kWh power consumption compared to the concurrent operation of all SCBSs.

This paper presents switched Multi-Element Antennas (MEAs) as a simple, yet effective, method of enhancing the performance of small cell heterogeneous networks and compensating for the small cell base station sub-optimal placement. The switched MEA system is a low-cost system which enables the small cell to dynamically direct its transmission power toward locations of high user density, in other words demand hotspots. Our simulation results show that small cell base stations equipped with switched MEA systems offer greater performance than base stations equipped with omni-directional antennas in terms of both the number of users that can be served (and hence offloaded from the macrocell network) and in terms of overall network capacity. We also compare the performance of the switched MEA with fixed directional antennas and show that fixed-directional antennas can only outperform the switched MEA if the misalignment between their direction of transmission and the direction to the demand hotspot is less than 22.5°.

The operation of wireless cellular networks can be efficiently supported by secondary small cells that are deployed at traffic hotspots within the coverage area of the primary macro cells. To this end, we examine the conditions under which the secondary base stations can share the same communication channel with the primary base stations under the constraint of a predefined quality of service, typical for uplink communications. In particular, requiring a target signal-to-interference-and-noise ratio (SINR) for each transmission accommodated in a common interference-limited channel, we formally assess the impact of the secondary infrastructure in a W-CDMA system on the global achievability of the uplink target SINRs via distributed closed-loop power control. The effects of cell load and inter-cell coupling are distinguished. As a result, we define solutions to the problems of base station placement, cell coverage optimization, and target SINR adaptation in W-CDMA heterogeneous networks, as well as discuss their compatibility with standard downlink-oriented deployment guidelines. Analytical conclusions are validated numerically by 3GPP-compliant simulations of a W-CDMA heterogeneous network.

In order to realise the potential benefits of co-channel deployments in Heterogeneous Networks (HetNets) and achieve an efficient spectrum reuse, a joint optimisation of small cell and macrocell network operation is highly beneficial. In this paper, a novel cellular network and frequency reuse configuration for both small cells and macrocells is proposed to address inter-cell interference and handover issues in HetNets. The proposed configuration complements Release 10 enhanced inter-cell interference coordination, and further increases its interference mitigation capabilities. Simulations for LTE show that compared to a network that uses standardised Release 10 Almost Blank Subframes (ABS), the proposed configuration can improve expanded-region User Equipments (UEs) performance by 50 % for a given ABS duty cycle. Moreover, for a given targeted expanded-region UEs performance, the proposed configuration can decrease the ABS duty cycle by up to a factor of 2, thus improving macrocell UEs performance up to 31 %. The proposed configuration also mitigates handover failures by 46 %, since handovers start at a higher signal quality.

The outdoor deployment of small cells (SCs) in heterogeneous cellular networks for mobile communications is advantageous from both the capacity and the power consumption point of view. In practice, however, the network operators are limited by the high costs associated with the provisioning of backhaul communication and power supply to the SCs. This paper addresses these operational challenges by using optical links at the same time for high-capacity backhaul and wireless power supply. In particular, we present an experimental design of an optical wireless link for energy harvesting (EH). Our system consists of (i) a high-brightness white light-emitting diode (WLED) with an output luminous flux of 2200 lm, and (ii) an amorphous silicon (a-Si) solar panel. The distance of the experimental link ranges from 0.5m to 5m. The visible-light EH from the solar panel is negatively affected by the Lambertian radiation pattern of the WLED. In order to increase the collimation of the source light, we use three optical elements - a spherical lens, a reflector, and a parabolic mirror. The latter is shown to achieve a gain of 28.4 dB in EH over a distance of 5 m compared to the non-collimated case.

The operation of wireless cellular networks can be efficiently supported by secondary small cells that are deployed on-demand in traffic hotspots within the coverage area of primary network cells. In view of the growing traffic demand and the limited spectrum available, the objective of this study is to examine the conditions under which the secondary base stations can share the same communication channel with the primary base stations under the constraint of a predefined quality of service, typical for uplink communications. In particular, considering a minimum required signal-to-interference-and-noise ratio (SINR) for each uplink transmission accommodated in a common interference-limited channel, we formally assess the impact of the secondary infrastructure on the global achievability of the target SINRs via distributed closed-loop power control. Both the effects of intra-cell load and inter-cell coupling are investigated with the aim of providing deployment guidelines for the secondary small-cell infrastructure. Analytical conclusions are validated numerically by 3GPP-compliant simulations of a network deployed in Dublin.

This study examines the trade-offs related to the problem of coverage auto-configuration of open access small cells (SC) that (i) are on-demand deployed in the macro-cells (MC) of a W-CDMA network, and (ii) operate in the same frequency channel as the hosting/neighboring MCs. The goal is to identify stable and practically detectable equilibrium states whose achievement can represent an optimality criterion for implementing automatic coverage adaptation of the SCs. It is shown that, on the downlink (DL), the offloading of MC hotspot users by a nearby SC improves the DL signal-to-interference-and-noise ratio (SINR) and the DL effective throughput of the MC users at the expense of the SC users. Similarly on the uplink (UL), the maximum achievable UL SINR of the SC (MC) users decreases (increases) with a growing SC coverage, but a distinct equilibrium state of the MC-vs-SC trade-off defined on the basis of the spectral radius of a network information matrix can be observed. However, the DL and UL equilibria do not generally coincide unless the MC and SC performance requirements are identical. This fact complicates autonomous optimum partitioning of the SC power budget between data transmissions and coverage-defining pilot signals. Numerical simulations are based on a 3GPP-compliant system model.

Digital Fountain Codes (DCFs) are proven to be very beneficial in providing reliable, yet flexible solutions in data communications over erasure wireless channels. Their randomness can additionally provide a strong protection against security and privacy risks. However, existing solutions based on DCFs suffer from increased packets, latency, increased decoding complexity, and require very large buffers both at the transmitter and receiver sides. This paper presents a practical solution to overcome most of these issues by exploiting the feedback path. Simulation results suggest significant up to 77% improvement in latency and up to 94% reduction in buffer requirements compared to the existing solutions.

With the introduction of femtocells, cellular networks are moving from the conventional centralized network architecture to a distributed one, where each network cell should make its own radio resource allocation decisions, while providing inter-cell interference mitigation. However, realizing such distributed network architecture is not a trivial task. In this paper, we first introduce a simple self-organization rule, based on minimizing cell transmit power, following which a distributed cellular network is able to converge into an efficient resource reuse pattern. Based on such self-organization rule and taking realistic resource allocation constraints into account, we also propose two novel resource allocation algorithms, being autonomous and coordinated, respectively. Performance of the proposed self-organization rule and resource allocation algorithms are evaluated using system-level simulations, and show that power efficiency is not necessarily in conflict with capacity improvements at the network level. The proposed resource allocation algorithms provide significant performance improvements in terms of user outages and network capacity over cutting-edge resource allocation algorithms proposed in the literature.

In this paper, a novel channel estimation (CE) method is proposed for spatial modulation (SM), a unique single-stream multiple-input-multiple-output transmission technique. In SM, there is only one transmit antenna being active at any time instance. While this property completely avoids inter-channel interference, it results in a challenge to estimate the channel information. In conventional CE (CCE) methods for SM, all transmit antennas have to be sequentially activated for sending pilots. Therefore, the time consumed in CE is proportional to the number of transmit antennas, which significantly compromises the throughput. By exploiting channel correlation, the proposed method, named transmission cross CE (TCCE), has the following characteristics: i) the entire channel is estimated by sending pilots through one transmit antenna; ii) it requires no overhead or feedback; and iii) it achieves a low computational complexity at the receiver. In addition, we propose an analytical framework to compute the distribution of the CE errors over time-varying fading channels. The corresponding average bit error probability (ABEP) bound of SM is also derived for the proposed method. Results show that the proposed ABEP bound matches with the simulations very well. When compared with CCE, the new method obtains a signal-to-noise ratio gain of up to 7.5 dB for medium and high correlations between the transmit antennas. Moreover, an adaptive CE technique can be readily implemented for SM via switching between CCE and TCCE.

Macrocellular networks with frequency reuse across cells suffer from high interference at the cell boundaries, resulting in a very uneven distribution of user throughput. Recently it has been shown that for multi-carrier networks a horizontal angular offset configuration for the second carrier can significantly improve the performance and call drop rate of the network. In this paper this concept is extended to the vertical dimension by providing additional cells with higher downtilt while retaining the naturally resulting interference avoidance properties. Simulations for UMTS HSDPA show that with the additional cell layer with vertical downtilt can increase the average user throughput by 126% and the throughput of the worst 5th percentile by 205%, compared to a traditional 3 sector macrocell configuration. It is shown that the proposed cell configuration also outperforms conventional 6 sector configurations with the same number of antennas and cells per site. Furthermore, it is shown that the addition of the second cell layer does not significantly increase the handover failure rates over conventional cell configurations. The concept scales well to higher sectorizations.

The wide adoption of smartphones has resulted in an exponential increase in the demand for wireless data. To address this problem, operators have started deploying large numbers of small cells. In order to operate such small cell network cost-effectively they need to be able to intelligently optimize their configuration, which can be achieved by applying machine learning techniques such as genetic programming. The use of genetic programming has previously been used to derive joint coverage algorithms for a group of enterprise femtocells. However, the evolution of the algorithms was performed in an offline manner, on a pre-defined simulation model of the deployment scenario. In this paper, an approach to perform the evolution in an online manner using an automated model building process is presented. The model building process uses network traces as inputs to create a hierarchical Markov model that is shown to be able to capture the behavior of the femtocell network well. It is shown that the resulting environment model can effectively drive the on-line evolution of coverage optimization algorithms.

Macrocellular networks with frequency reuse across sectors of the same eNodeB and neighbouring eNodeBs suffer from high interference at the cell boundaries, resulting in an uneven distribution of User Equipment (UE) throughputs. Recently, it has been shown that for two-carrier HSDPA networks a horizontal sector offset configuration for the second carrier can significantly enhance network performance at the cost of an increased number of handovers. In this paper, this sector offset configuration is extended to LTE through a novel approach, which allows deploying offset antennas in eNodeBs without significantly increasing the number of handovers. The proposed sector offset configuration is compatible with current channel dependent schedulers, and provides significant gains. Simulation results show that for LTE the proposed sector offset configuration can increase the average UE throughput and 5%-tile UE throughput by up to 22% and 32%, respectively, while slightly increasing the number of handovers compared to the traditional eNodeB configuration. Moreover, results show that the new configuration can significantly reduce the handover failure rate by up to 69%.

This paper introduces the problem associated to long neighbour cell lists as a result of introducing and reserving identity codes for small cells in heterogeneous networks. It is shown that the number of reserved codes needs to be sufficiently high or identity confusions and collisions are inevitable. This results in long neighbouring lists that effectively increases measurement periods and hand over failures subsequently. To address the issue, a standard compliant mechanism is proposed in that the neighbouring cell list contains only relevant macrocell neighbours and is updated dynamically according to the users' measurements. The simulation scenario used in this study refers to a part of the 3G network in south east of Berlin and belongs to a top tier mobile operator in Germany. The result confirms performance improvement both in terms of handover success rate as well as impact on battery life of users' equipment when the proposed scheme is deployed.

We propose a distributed and coordinated radio resource allocation algorithm for orthogonal frequency division multiple access (OFDMA)-based cellular networks to self-organize efficient and stable frequency reuse patterns. In the proposed radio resource allocation algorithm, each cell independently and dynamically allocates modulation and coding scheme (MCS), resource block (RB), and transmit power to its users in a way that its total downlink (DL) transmit power is minimized, while users' throughput demands are satisfied. Moreover, each cell informs neighboring cells of the RBs that have been scheduled for its cell-edge users' DL transmissions through message passing. Accordingly, the neighboring cells abstain from assigning high transmit powers to the specified RBs. Extensive simulation results attempt to demonstrate that DL power control on a per-RB basis may play a key role in future networks, and show that the distributed minimization of DL transmit power at each cell, supported by intercell interference coordination, is able to provide a 20% improvement of network throughput, considerably reduce the number of user outages, and significantly enhance spatial reuse, as compared to cutting-edge resource allocation schemes.

This paper introduces the problem of Primary Scrambling Code (PSC) selection in small cell networks and proposes a novel optimisation technique. Small cells introduce challenges not present in conventional macrocell scrambling code allocation, including the need for dynamic allocation, scalable distributed allocation algorithms, and support for unplanned and organic deployments. To the best of our knowledge this is the first study addressing the issue of distributed scrambling code selection for small cell networks. We propose a decentralized learning algorithm which does not require any collaboration between the neighbouring base-stations and which finds a feasible allocation whenever one exists. The performance of the algorithm is compared against two variations of a greedy algorithm which is the current 3GPP recommendation and is shown to offer significant performance benefits.

Macrocellular networks with frequency reuse across cells suffer from high interference at the cell boundaries, resulting in a very uneven distribution of user throughput. In this paper a sector offset configuration for multi-carrier networks is proposed that can significantly improve the cell edge performance without reducing the frequency re-use factor. Simulations for UMTS HSDPA show that for a 3 sector base station transmitting on two carriers the proposed approach can increase the average user throughput by 38% and the throughput of the worst 5th percentile by 94%, with a small increase in the total number of handovers. In addition it is shown that the new configuration results in a significantly reduced handover failure rate.

This paper investigates a handover problem occurring with current power control mechanisms in co-channel WCDMA heterogeneous networks. The problem is associated to the mismatch between the required uplink transmit power when a user is communicating to a small cell and an underlying microcellular base-station. As a result, there is a need for significant increases of users' transmit power when handing off from small cell to the macrocell tier. This sudden increase of the transmit power results in increased uplink interference and major SINR drops for existing small cell users. This paper introduces a realistic and efficient solution to adapt the transmit power of the small cell users during the handover regime to prevent such SINR drops. Simulation results confirm significant performance improvement when using the proposed scheme.

This paper introduces a solution to provide selective access in cellular wireless networks. The current best known solution for preventing users from service access in a cellular network is to deploy signal jammers. However, due to several shortcomings associated to the use of signal jammers, their deployment is facing several technical and legal issues. The proposed solution of this paper is based on deployment of small cells that allows for flexible access mechanism and full control of the operators. Coverage optimisation is one of the most important aspects that assures successful deployment of the solution whilst minimizing the service disruption to other users. Hence, depending on the application scenario and deployment priorities, a method of coverage optimisation is introduced. The overall solution is evaluated through accurate ray tracing simulations for Hynes convention centre in Boston for which the validity of the ray tracing simulation model is verified through measurements.

This paper investigates the effect of the other-cell interference in WCDMA small cell networks when operating on the same frequency channel of the hosting macrocell tier. In this paper, the problem formulation is presented and it is shown that the co-channel interference and the effective capacity can not be analytically traced. Consequently, using simulation and modelling, the paper investigates the interference exposure of the macrocell tier on the small cells in regards to the small cell's distance to the macrocell Base Station (BS), the small cell transmission power, the environment shadowing and macrocell load level. The results can serve as essential guidelines for operators when estimating the effective capacity of small cells in co-channel deployment scenarios.

Femtocells, despite their name, pose a potentially large disruption to the carefully planned cellular networks that now connect a majority of the planet's citizens to the Internet and with each other. Femtocells - which by the end of 2010 already outnumbered traditional base stations and at the time of publication are being deployed at a rate of about five million a year - both enhance and interfere with this network in ways that are not yet well understood. Will femtocells be crucial for offloading data and video from the creaking traditional network? Or will femtocells prove more trouble than they are worth, undermining decades of careful base station deployment with unpredictable interference while delivering only limited gains? Or possibly neither: are femtocells just a "flash in the pan"; an exciting but short-lived stage of network evolution that will be rendered obsolete by improved WiFi offloading, new backhaul regulations and/or pricing, or other unforeseen technological developments? This tutorial article overviews the history of femtocells, demystifies their key aspects, and provides a preview of the next few years, which the authors believe will see a rapid acceleration towards small cell technology. In the course of the article, we also position and introduce the articles that headline this special issue.

This paper introduces a Switched Multi-Element Antenna (SMEA) solution for interference mitigation in femtocell networks. While the main objective is to protect the femtocell users against uplink interference, the downlink interference from femtocell base stations to the other users is simultaneously reduced as a by-product of this technique. A tailored form of reinforcement learning is used to allow for self-configuration of the femtocell base station and to adaptively select the optimal antenna configuration in a time varying environment. Compared to the traditional Omni-directional antenna systems, the results show an average of 2.5dB gain in uplink direction in terms of reduced transmission power and approximately 1dB of gain in the downlink channel.

With the introduction of femtocells, cellular networks are moving from the conventional centralised architecture to a distributed one, where each network cell should make its own radio resource management decisions, while providing inter-cell interference mitigation. However, realising this distributed cellular network architecture is not a trivial task. In this paper, we first introduce a simple self-organisation rule under which a distributed cellular network is able to converge into an efficient resource allocation pattern, then propose a novel resource allocation model taking realistic resource allocation constraints into account, and finally evaluate the performance of the proposed self-organisation rule and resource allocation model using system-level simulations.

Radio coverage optimization is a key element in improving the overall performance of femtocell cellular networks. Owing to the increased size and complexity of femtocell networks, there is an imperative need to develop decentralized coverage optimization algorithms, which is a highly challenging task as these algorithms have to work without global information and coordinated central control of network nodes. In this paper, we consider a deployment scenario of a group of N femtocells in an enterprise environment and propose a decentralized algorithm for joint femtocell coverage area optimization. The algorithm updates the femtocell's pilot Tx power only and serves to balance the user load amongst the collocated femtocells as well as minimizing the coverage holes and pilot Tx power. Compared to the fixed pilot Tx power allocation, the algorithm introduces an improvement of approximately 18% in terms of supported user traffic, in addition to a significant reduction of pilot leakage to neighboring cells.

The neighbour cell list (NCL) in cellular networks has an important impact on the number of dropped calls and is traditionally optimized manually with the help of planning tools. In this paper, a method for automatically optimizing a NCL is presented, which consists of an initialization using a self-configuration phase, followed by a self-optimization phase that further refines the NCL based on measurements provided by mobile stations during the network operation. The performance of the proposed methods is evaluated for different user speeds and different NCL sizes. Besides, the convergence speed of the proposed self-optimization method is evaluated. It is shown that when about 6000 measurements are reported by mobile stations, the proposed self-optimization method attains a stable maximum performance about 99% of success rate.

This paper introduces a solution to enable self-optimization of coverage and capacity in LTE networks through base stations' downtilt angle adjustment. The proposed method is based on fuzzy reinforcement learning techniques and operates in a fully distributed and autonomous fashion without any need for a priori information or human interventions. The solution is shown to be capable of handling extremely noisy feedback information from mobile users as well as being responsive to the changes in the environment including self-healing properties. The simulation results confirm the convergence of the solution to the global optimal settings and that the proposed scheme provides up to 20% performance improvement when compared with an existing fuzzy logic based reinforcement learning approach.

Femtocells, which operate in licensed spectrum and provide improved cellular coverage inside homes and offices, have attracted significant interest in the wireless industry. As a result, an extensive deployment of femtocells is expected in the near future. One prime concern with a large-scale femtocell deployment is the resultant substantial energy consumption. In this paper, we address this issue by proposing a novel energy saving procedure which allows the femtocell base station (BS) to completely switch off its radio transmissions and associated processing when not involved in an active call. The results indicate that based on a certain voice traffic model, the proposed procedure introduces an average reduction of approximately 37.5% in the femtocell's power consumption. Moreover, for a high femtocell traffic scenario, a fivefold reduction in the occurrence of mobility events can also be achieved, compared to a fixed pilot transmit power strategy.

Femtocells are low-cost, low-power cellular base stations that are deployed by the end user to supplement macro-cellular coverage and provide high data rates in the customer's premises. In femtocell deployments, leakage of the pilot signal to the outside of a house can result in a highly increased signalling load to the core network as a result of the higher number of mobility events caused by passing users. In this paper, a low cost multi-element antenna solution is proposed to reduce the core network mobility signalling over previously published results using a single antenna only. Antenna gain pattern measurements of a prototype with two patches and two inverted F antennas are presented and a corresponding feeder network is discussed. Self-optimization methods are proposed that jointly select an appropriate antenna pattern and optimize the pilot power. This allows to better match the femtocell coverage to the shape of each individual house and results in an improvement of both indoor coverage and core network signalling resulting from mobility events.

Femtocells are low-cost, low-power cellular base stations that are designed to provide high quality cellular service in residential or enterprise environments and operate in licensed spectrum to connect conventional, unmodified mobile terminals to a mobile operator's network. The deployment of femtocells will reduce the number of indoor users that need to be served by the macrocellular network. Moreover, since indoor users usually have poorer radio channel conditions to the macrocell compared to outdoor users, for each indoor user that is served by a femtocell, the equivalent system resources of the macrocellular network might be able to serve more than one outdoor user, resulting in a potential macrocell offloading gain. The macrocell offloading benefits achievable in joint macro-femtocell deployments are investigated using system level simulations. It is shown that the potential increase in the number of supported users per macrocell in joint macro- and femtocell deployments depends on the macrocellular network configuration and can range from no gain for a system which is only interference limited to more than 30% in cases where noise is the limiting factor for indoor users at the macrocell edge.

The use of a group of femtocells to jointly provide coverage in an enterprise environment introduces several challenges in the introduction of self-configuration and self-optimization capabilities required for plug-and-play styles of deployment. In this paper, an approach to automatically derive a distributed algorithm to dynamically optimize the coverage of a femtocell group using genetic programming is described. The resulting evolved algorithm showed the ability to optimize the coverage well, and is able to offer increased overall network capacity compared with a fixed coverage femtocell deployment.

In this paper the feasibility of femtocells partially reusing GSM spectrum is investigated. Due to the increasing scarcity and cost of spectrum, partial spectrum reuse by secondary networks is being considered to improve spectral efficiency. Additionally, new revenue streams can be extracted from existing resources by utilising partial spectrum reuse. The partial reuse of spectrum depends largely on the ability of the secondary network to mitigate the negative interference impact upon the primary network. This can be achieved by the establishment of acceptable interference criteria. This paper presents a solution to the problem of meeting the interference criteria with regards to frequency allocation and power configuration within the femtocell network. We demonstrate that these solutions can be applied successfully to allow for partial spectrum reuse in the GSM band. Furthermore we present an analysis of both the theoretical performance of femtocells and the resulting impact on the macrocellular network.

Rising energy costs and the recent international focus on climate change issues has resulted in a high interest in improving the energy efficiency in the telecommunications industry. In this paper the effects of a joint deployment of macrocells for area coverage and publicly accessible residential picocells on the total energy consumption of the network is investigated. The decreasing energy efficiency of todaypsilas macrocellular technologies with increasing user demand for high data rates is discussed. It is shown that a joint deployment of macro- and publicly accessible residential picocells can reduce the total energy consumption by up to 60% in an urban area with todaypsilas technology. Furthermore the impact of future technologies for both macro- and femtocells on the energy efficiency is investigated. It is shown that the benefits of a joint macro- and picocell deployment will increase further as both technologies mature, and will result in a significant reduction of the network energy consumption as the user demand for high data rates increases.

The increasing demand for higher data rates in cellular networks has resulted in a trend to smaller cell sizes (femto cells) and pico-cellular hot spot coverage. In this paper the financial impact of pico-cellular home base station deployment in a macro-cellular network is explored. For increasing data rates, it becomes evident that current macro-cellular network deployment becomes less economically viable. It is shown that in urban areas a combination of publicly accessible home base stations, randomly deployed by the end user, and macro-cells for area coverage, deployed by an operator, can result in significant reductions (up to 70% in the investigated scenario) of the total annual network costs compared to a pure macro-cellular network.

In this paper, the feasibility of user deployed femtocells in the same frequency band as an existing macrocell network is investigated. Key requirements for co-channel operation of femtocells such as auto-configuration and public access are discussed. A method for power control for pilot and data that ensures a constant femtocell radius in the downlink and a low pre-definable uplink performance impact to the macrocells is proposed, and the theoretical performance of randomly deployed femtocells in such a hierarchical cell structure is analysed for one example of a cellular UMTS network using system level simulations. The resulting impact on the existing macrocellular network is also investigated.

The femtocell concept aims to combine fixed-line broadband access with cellular telephony using the deployment of low-cost, low-power 3G base stations in the subscriber's homes. These plug-and-play residential base stations would be deployed without much consideration to cell planning on the part of the user, relying instead on inbuilt auto-configuration abilities to minimise the impact on the macro cellular network by self-provisioning parameters such as the transmit and pilot power levels. In this paper, simulations of the deployment of such femtocells in a residential scenario were performed to study its effects on the service experienced by users that are connected to the underlay macrocells. The results show that with auto-configuration, the deployment of the femtocells would not pose a significant impact on the dropped call rate, causing an additional 0.45% increase in chance of a macrocell user's call dropping in the simulation's worst case scenario. In addition the impact of such femtocell deployment on the network signalling is discussed.

The idea of a completely independent, self-healing, self-deploying, and self-optimising communication network is an appealing one. This paper outlines why financial pressures will drive wireless communication networks towards the adoption of autonomic systems with the above characteristics, that will eventually become cognisant, exhibiting some degree of self- awareness. As a step in this direction, we explore the concept of robotic wireless base stations and discuss their behaviour subject to principles inspired by Asimov's Laws of Robotics. Finally, a discussion of how such laws would be applicable in current and future scenarios is presented.

The deployment and configuration of wireless access networks represents an increasing cost factor. This problem is addressed with the novel concept of a self-deploying network, which is able to autonomously identify the need of changes in position and configuration of wireless access nodes and adapt the network to its environment. Distributed algorithms for selfdeployment and load balancing are proposed that improve both, network performance and convergence speed over previous work. It is shown that self-deploying networks using the proposed algorithms are able to significantly outperform statically deployed networks in environments with dynamic user demand and provide robustness against failing nodes.

This paper provides a broad overview of a number of approaches to dynamic spectrum sharing, and presents a specific case study involving hierarchical co-channel macro- and pico-cells. This case study demonstrates the feasibility of implementing a private commons approach to spectrum sharing in a near term, commercial application. Finally, a roadmap for evolution towards dynamic spectrum sharing is put forward.

With the emergence of multiple wireless operators using a variety of access technologies, congested spectrum bands, and multi-standard user terminals, there is a pressing need to see what implications autonomic communication systems can have on business models in the wireless access business. This paper outlines and discusses some perspectives on the future impact of autonomicity in a demand-, resource-, and competition-driven environment and on the strategies of wireless access providers. The effects are investigated for a standards-neutral, multi-agent architecture that is capable of per-session/call trading of access to wireless network and spectrum resources, exploiting the capability of autonomous agents to represent the business interests of individual players. Results from a joint radio-resource/inter-agent-negotiation simulator are used to quantitatively assess the economic outcomes in various multi-operator competitive scenarios in a realistic environment (an international airport).

For simulating mobility of both user terminals and base stations in wireless access systems, it is often advantageous to generate environment "maps" of the channel attenuations including path loss and shadow fading. However, the generation of a large number of spatially correlated shadow fading values, using for example the Cholesky decomposition of the corresponding correlation matrix, is computationally complex and requires a large amount of memory. In this paper an efficient low complexity alternative is proposed, where each new fading value is generated based only on the correlation with selected neighbouring values in the map. This results in a significant reduction of the computational complexity and memory requirements. It is shown that taking a small number of neighbouring values into account is sufficient to achieve relatively small correlation errors in the generated shadow fading maps.

Turbo-encoded multiple-input multiple-output (MIMO) radio links have been recently proposed for high-speed downlink packet access (HSDPA) in UMTS, where the reuse of spreading codes across the transmitter antennas results in high levels of interference. A state-of-the-art receiver chain for such a link incorporates space-time channel equalization, despreading, prewhitening, and detection. In this paper, the impact of modeling errors at the equalizer output on the receiver performance is investigated. Both a precise model and a more pragmatic approximate model are considered. Degradations resulting from imperfect knowledge of the channel state information at the receiver are also evaluated. The a posteriori probability (APP) detector and its max-log variant as well as the multistage partial parallel interference canceller (MS-PPIC) are examined as detection candidates in 1/3-rate and 1/2-rate coded 4/spl times/4 MIMO links. It is shown that, surprisingly, the MS-PPIC can provide superior performance compared to max-log-APP.

The demand for low-cost and low-power decoder chips has resulted in renewed interest in low-complexity decoding algorithms. In this paper a novel modification of the Max-Log-MAP algorithm is proposed for use in a turbo decoding process. This is achieved by scaling the a priori information by correction weights at each iteration, in order to maximize the exchange of mutual information between the component decoders. It is shown that the proposed technique results in a performance which approaches that of a turbo decoder using the optimum MAP algorithm, while maintaining the advantages of low complexity and insensitivity to input scaling inherent in the Max-Log-MAP algorithm. A second contribution of this paper is a method for off-line computation of the optimum weight values. The convergence behaviour of the proposed decoder is analysed via extrinsic information transfer (EXIT) charts.