Distributed MIMO has long been known theoretically to bring large throughput gains to wireless networks. Recent years have seen significant interest and progress in developing practical distributed MIMO systems. However, these systems only distribute the transmission function across the multiple nodes. The control fabric that synchronizes the nodes to a common reference phase still fundamentally requires a single leader that all nodes in the network are capable of hearing.

We consider user to cell association in a heterogeneous network with a mix of LTE/3G and WiFi cells. Individual user preferences are often neglected when a user to cell association decision is made. In this paper we propose use of a recommender system to inform the mapping of users to cells. We demonstrate the effectiveness of the proposed grouped-based user to cell associations for a set of synthetically generated user/cell ratings.

Smart mobile devices are one of the main factors of realizing the smart city of tomorrow. To this aim, in this paper, we investigate utilizing cloud robotics as a practical processing platform for making the existing inexpensive mobile devices such as robots smarter and collaborative. Smart cities with the scattered massive number of sensors would need complicated infrastructure to connect and process large quantities of data. Cloud robotics has emerged as an efficient computing means in intensive data processing applications. Hence, we introduce C2RO cloud robotics platform that uses real-time stream processing technology to virtually connect the energy-efficient and low-cost city mobile devices or sensors. Moreover, to mitigate the effects of latency, we propose the hybrid cloud robotics computation model used in C2RO cloud robotics platform as a processing model employing both edge and cloud computing technologies in robotics. We demonstrate the superiority of C2RO platform through practical experiments and show that our proposed computational model provides significant optimization in the latency and processing rate, and achieves the real-time target of a high-profile urban project.

We consider proportional fair rate allocation in a heterogeneous network with a mix of LTE and 802.11 cells which supports multipath and multihomed operation (simultaneous connection of a user device to multiple LTE base stations and 802.11 access points). We show that the utility fair optimization problem is non-convex but that a global optimum can be found by solving a sequence of convex optimizations in a distributed fashion. The result is a principled approach to offload from LTE to 802.11 and for exploiting LTE/802.11 path diversity to meet user traffic demands.

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.

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%.