This paper presents a novel decision support system (DSS) to address the University Course Timetabling Problem (UCTP). The solution decomposes the NP-complete UCTP into two sub-problems, allowing a structured approach to addressing the complexities inherent in the UCTP process. A mixed integer linear programming (MILP) model is proposed to integrate academic year course schedule planning and instructor assignment, accommodating various constraints to meet student demands. The model optimizes the number of course sections and strategically schedules instructors, aiming to reduce the number of new and distinct courses assigned to them. Historical data from an academic department encompassing multiple disciplines, including Computer Information Systems, Business Management, and Business Analytics, at a large public university in the U.S. is used to develop the model, and the results are compared with the actual course schedule and instructor assignment. The results demonstrate that the proposed DSS would result in a 14 % reduction in the number of course sections offered, translating to approximately $130,000 in annual savings. Additionally, it could significantly reduce the number of new courses assigned to instructors by up to 81 % and the number of distinct course sections assigned to them by 29 %. • We present a novel DSS model to address the university course timetabling problem. • The model optimizes the number of course sections and strategically schedules instructors. • The model aims to reduce the number of new and distinct courses assigned to instructors. • The model helped to save $130,000 and reduce the number of new courses assigned to instructors by up to 81 %. • The model helped to reduce the number of distinct course sections assigned to instructors by 29 %.

OBJECTIVE: Depression is currently the second most significant contributor to non-fatal disease burdens globally. While it is treatable, depression remains undiagnosed in many cases. As mobile phones have now become an integral part of daily life, this study examines the possibility of screening for depressive symptoms continuously based on patients' mobile usage patterns. MATERIALS AND METHODS: 412 research participants reported a range of their mobile usage statistics. Beck Depression Inventory-2nd ed (BDI-II) was used to measure the severity of depression among participants. A wide array of machine learning classification algorithms was trained to detect participants with depression symptoms (ie, BDI-II score ≥ 14). The relative importance of individual variables was additionally quantified. RESULTS: Participants with depression were found to have fewer saved contacts on their devices, spend more time on their mobile devices to make and receive fewer and shorter calls, and send more text messages than participants without depression. The best model was a random forest classifier, which had an out-of-sample balanced accuracy of 0.768. The balanced accuracy increased to 0.811 when participants' age and gender were included. DISCUSSIONS/CONCLUSION: The significant predictive power of mobile usage attributes implies that, by collecting mobile usage statistics, mental health mobile applications can continuously screen for depressive symptoms for initial diagnosis or for monitoring the progress of ongoing treatments. Moreover, the input variables used in this study were aggregated mobile usage metadata attributes, which has low privacy sensitivity making it more likely for patients to grant required application permissions.

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.

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°.

We formulate adaptation of antenna tilt angle as a utility fair optimization task. This optimization problem is nonconvex, but in this paper we show that, under reasonable conditions, it can be reformulated as a convex optimization. Using this insight, we develop a lightweight method for finding the optimal antenna tilt angles, making use of measurements that are already available at base stations, and suited to distributed implementation.

We propose a new approach for jointly scheduling deep sleep and allocating pilot and data transmit power in small cells with the aim of minimising energy consumption while maximising user utility within a mixed macro/small cell network. Our approach exploits the relatively predictable nature of traffic load across each day/week and is demonstrated to achieve an average energy reduction of 37%, and peak energy reductions of 70% under realistic traffic loads and network topologies.

In this paper we investigate the role of tilt angle adjustment in next generation LTE networks where SIMO receivers and adaptive OFDM/TDMA transmission scheduling may additionally be used to mitigate interference. We present detailed performance measurements when (i) optimal fair tilt angle adjustment is applied in combination with SIMO receivers using Linear Minimum Mean Square Error (LMMSE) detection to mitigate interference, and when (ii) tilt angle adjustment is applied in combination with proportional fair OFDM transmission scheduling which adapts the transmission rate per subcarrier (narrow-band rate allocation). We find that even when SIMO/LMMSE reception and adaptive transmission scheduling are used to mitigate interference, tilt angle adjustment still offers the potential for significant performance gains, namely increases in mean user throughput of more than 65% and improvements in the network sum-log rate of greater than 20%.

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.

Rateless code based communications for unicast applications require the decoder to indicate successful decoding of a batch of input packets before the encoder moves to a new batch. This will cause significant overhead when the number of input packets is restricted or when considering long distance, high capacity links. This is mainly due to the fact by the time the acknowledgement packet is received at the encoder, a large number of redundant encoded packets are injected into the communication link. This study proposes a practical solution to address the issue by introducing a two- level acknowledgment scheme and by dynamically accounting for the on-the-fly packets. The solution is implemented in an end-to-end rateless-code based scenario where the experimental results confirm the feasibility of the solution and notable throughput improvement gains when using this scheme.

Luby Transform (LT) codes are shown to perform very well over erasure wireless channels. However, LT codes require the number of input symbols to be sufficiently high or otherwise the coding overhead becomes significant. With large number of input symbols, the packets latency and decoding complexity as well as buffer requirement may increase. Given that Gaussian elimination is computationally intensive, this paper proposes to additionally consider higher order encoded symbols during the decoding procedure. The results show up to 47% overhead reduction when degree-2 symbols are as well contributing in the decoding procedure. The proposed method is very straightforward and requires no change to the encoder and minimal modification of the decoder.

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.

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.

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.

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 introduces a novel technique for self-optimisation of antenna down-tilting. The proposed solution consists of two phases. During the first phase, which needs to be performed quickly, a method based on the Golden Section Search (GSS) algorithm is used to find a near-optimal tilt angle. Then, in the next step, the tilt angle value is fine-tuned through frequent explorations of the near-optimal region. Additionally, the solution benefits from self-healing properties by responding to the failure of a neighbouring cell. In this case, the algorithm is adapted to find a new optimal point upon failure detection. The result shows fast convergence and close-to-optimum performance (93.8% on average) of the proposed algorithm. The overall solution is fully distributed and is 3GPP complaint.

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.

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.

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.

This paper demonstrates that growth codes, based on Raptor channel coding, allow incremental protection of H.264 video codec data-partitioned network adaption layer units. When combined with increased protection of video reference frames, in an ADSL erasure channel up to 10 dB in video quality (PSNR) can be gained through this scheme compared to equal error protection with rateless codes. Equivalent gains occur in a wireless channel from combining data-partitioning with error protection. The bitrate overhead from data-partitioning is also shown to be less than from other H.264 error resilient tools.

IPTV video services are under development for broadband networks, with final hop delivery across a wireless link. Within these networks, dedicated subchannels transport different types of traffic (video, voice, data...). This paper proposes fuzzy logic control (FLC) to allocate video streams within an IPTV subchannel subject to fluctuating available bandwidth. The method employs spatial and temporal complexity metrics to judge the appropriate share of available bandwidth, taking account of genre and dynamic coding rate fluctuations. So that the video sources can adjust their rates to the likely available bandwidth across the wireless link, reliable prediction of available bandwidth over time is also proposed. In tests, the FLC, which serves to merge the spatial and temporal metrics into a single control output, results in an overall gain in video quality over static equal allocation of bandwidth and also compensates streams that would otherwise suffer from an inadequate or alternatively an unnecessarily large bandwidth share.

This paper introduces block-based rateless coding for video streaming over a wireless interconnect, in which after a packet erasure additional coded blocks are generated and piggy-backed onto outgoing packets. The advantage is shown in comparison to default Bluetooth FEC schemes, as, through the block-based rateless scheme, transmission energy consumption is reduced by a factor of up to 1.8, depending on Rayleigh channel bad state durations. In poorer channel conditions, the rateless scheme improves delivered video quality by as much as 10 dB relative to a Bluetooth v. 2.1 EDR mode. Decode complexity for rateless Raptor codes is linear in block size.

This paper introduces a block-based form of rateless channel coding that minimizes energy consumption by reducing the overhead and latency from channel coding. Consequently, results from a Bluetooth (IEEE 802.15.1) wearable wireless network for augmented reality (AR) show a consistent reduction in energy consumption compared to Bluetooth FEC schemes. AR relies on video transmission across a wearable network. Block-based rateless coding resulted in around 5 dB improvement compared to block-based FEC in video quality at a head-mounted display in the face of worsening channel conditions. System modeling took into account cross-traffic resulting from biosensors that moderate the AR display according to the cognitive load of the wearer.

Bluetooth's default ARQ scheme is not well suited to real-time video streaming, resulting in missed display and decode deadlines. Therefore, adaptive ARQ combined with a deadline aware buffer is an alternative approach. However, even with the addition of adaptation to picture type content importance, ARQ is not ideal in conditions of deteriorating RF channel. The paper presents fuzzy logic control of ARQ, based on send buffer fullness and the head of line packet's deadline. Tests show an improvement of over 4 dB improvement in video quality compared to a non-fuzzy scheme. The scheme can be applied to any codec with I-, P- and (possibly) B-slices by inspection of packet headers without encoder intervention.