In the next generation of wireless systems, massive Multiple Input Multiple Output (MIMO) offers high angular resolution for localization. By virtue of large number of antennas, Angle of Arrival (AoA) of User Terminals (UTs) can be estimated accurately. Measurements of Dense Multipath Channel (DMC) indicate multipath signals contribute up to 95% of total link power. This fact urges the necessity of studying the contribution of multipath signals accompanying dominant path in AoA estimation. We obtain a deterministic form for Cramer-Rao Lower Bound ($CRLB$) in multi-user scenario when contribution of multipath signals is considered. We do this when the multipath signals are independent and identically distributed (i.i.d) with arbitrary distribution. Then, we redefine a localization efficiency function for multi-user scenario and optimize it with respect to (w.r.t) the number of antennas. When only a subset of available antennas is used, we prove that$CRLB$can be minimized w.r.t which set is used. An antenna selection strategy that minimizes$CRLB$is proposed. As a benchmark, we apply the proposed antenna selection to MUltiple SIgnal Classification (MUSIC) algorithm and study its efficiency. Numerical results validate the accuracy of our analysis and show significant improvement in efficiency when the proposed antenna selection is employed.

In this paper, we consider Internet of Things (IoT) based mobile edge computing (MEC) system. IoT devices are controlled by access points (APs), which do not have access to any licensed spectrum. This puts limitations on IoT devices’ offloading capability and hence effectively reduces the number of computed bits. Under such scenario, we consider spectrum sharing, where IoT networks get access to licensed spectrum by helping a primary network (owns licensed spectrum) in cooperative relaying. We aim to maximize sum of the primary network’s communication rate and IoT networks’ total number of computed bits by jointly optimizing relay selection, licensed spectrum allocation, local computation, and offloading sequence selection. Moreover, we consider important constraints, e.g., the guaranteed rate for the primary network, IoT devices’ available energy, and available licensed spectrum resource blocks (RBs). We observe that the optimization problem is combinatoric in nature, which becomes more complicated when IoT devices’ scheduling order comes into the formulation. From our analysis, we devise an optimal and computationally efficient algorithm. In the result section, we compare with relevant benchmark systems and show the efficacy of our proposed model. Moreover, we also show how IoT and primary networks benefit from spectrum sharing.

Massive MIMO systems provide high angular resolution in the next generation of wireless systems. This opportunity can be used to estimate the location of user terminals (UTs) accurately. In this paper, we analyze the Cramer-Rao lower bound (CRLB) of planar antenna arrays in Massive MIMO systems for Angle of Arrival (AoA) estimation. With the help of Random Matrix Theory, we prove that for Massive antenna arrays with independent and identically distributed (i.i.d) multipath signals, instantaneous CRLB for AoA estimation converges toward a deterministic value, regardless of channel distribution. In this scenario, CRLB is a function of channel variance instead of instantaneous realizations. Then, antenna selection is studied, and it is shown that using different subsets significantly affects the CRLB of a planar array. Numerical results confirm the convergence of deterministic results and indicate the benefits of antenna selection and the importance of the selection strategy.

In this paper, we consider a system model in conjunction with two major technologies in 5G communications, i.e., mobile edge computing and spectrum sharing. An IoT network, which does not have access to any licensed spectrum, carries its computational task offloading activities with help of spectrum sharing. The IoT network cooperates with a licensed spectrum holding network by relaying its data and in return gets access to the licensed spectrum. The licensed spectrum holding network focuses on throughput maximization, whereas, the IoT network tries to maximize task computation rate. We formulate an optimization problem giving importance to both networks’ interests. Typical IoT nodes may be energy-constrained, which is considered along with task computation time constraints. Parallel computation is considered while computing IoT nodes’ computational tasks at the mobile edge computing server, where the processor is allocated based on IoT nodes’ offloading capabilities. The advantage of such processor allocation is shown in the result section. Moreover, we also show how both IoT and licensed spectrum holding networks benefit from the spectrum sharing by caring for each other requirements, which echoes the fact, i.e., Sharing is Caring.

In this paper we analyze the performance of a mobile edge computing (MEC) system in the presence of spatially correlated shadowing. To achieve this goal, obstacles like walls are modelled by a Manhattan Poisson line Process (MPLP). Simultaneous wireless information and power transfer (SWIPT) is used to send command messages as well as to deliver the power to low power devices (LPDs). The energy harvested by the LPDs in the downlink phase is then used to either locally execute the assigned computation task or to offload it to the MEC server. By leveraging stochastic geometry, we derive analytical expressions for the success probability of the system which is defined as the joint probability of SINR downlink coverage and of harvesting enough energy to execute the computation task. Moreover, the impact of different system parameters on the trade-off between the harvested energy and the computational task admission rate is analyzed.

The integration of simultaneous wireless information and power transfer (SWIPT) and mobile-edge computing (MEC) technologies is emerging as a promising technique to overcome the performance limits of ultra-low power devices (ULPD) due to their low battery capacities and their limited computation capabilities in the Internet of Things (IoT) era. In this paper, we propose an online resource allocation algorithm for multiuser SWIPT-based MEC systems with the aim of maximizing the proportional fairness computational utility function subject to the stability of task and energy queues. Lyapunov optimization framework is used to jointly optimize the amounts of time allocated for energy harvesting, information decoding and offloading, the transmission power for offloading and CPU-cycles frequencies for local computing. Moreover, rigorous performance analysis has been done to prove the asymptotic optimality of our proposed algorithm. Simulation results are also presented to demonstrate the gains of our proposed algorithms over alternative online approaches and the impact of different network parameters on the performance of our algorithm.

In this paper, we have considered a simultaneous wireless information and power transfer (SWIPT)-enabled mobile edge computing (MEC) system where a low power device (LPD) has to decide either to perform local computation or offloading. A decision strategy has been proposed and the impact of this strategy on a triple trade-off between energy, local computation and offloading has been evaluated. Moreover, in the local computation case, due to the existence of an uploading phase in which results are sent to the MEC server, another communication-computation trade-off has been analysed. In order to provide a system-level perspective on the performance of such a SWIPT-MEC network, we have employed stochastic geometry to characterise these communication-computation trade-offs of the system and we have investigated joint success probability of local computation and offloading.

In this paper, we investigate the achievable semantic secrecy rate of existing lattice coding schemes, proposed in [6], for both the mod-Λ Gaussian wiretap and the Gaussian wiretap channels. For both channels, we propose new upper bounds on the amount of leaked information which provide milder sufficient conditions to achieve semantic secrecy. These upper bounds show that the lattice coding schemes in [6] can achieve the secrecy capacity to within ½ln e/2 nat for the mod-Λ Gaussian and to within ½(1 - ln (1 + SNRe/ SNRe+1)) nat for the Gaussian wiretap channels where SNReis the signal-to-noise ratio of Eve.