In this paper, we investigate the load balancing problem in multi-carrier LTE networks. We propose a probabilistic framework for idle-mode parameter optimization in Self-Optimizing Networks (SON) in order to achieve the target traffic distribution per carrier. In particular, we consider different layer management strategies such as hierarchical, equal priorities. For each strategy, we mathematically optimize the underlying idle-mode parameters defined by 3GPP specification, based on the statistical information of radio frequency (RF) propagation in order to achieve the target traffic distribution. Moreover, we characterize the underlying tradeoffs between the traffic controllability and user experience for each strategy. Finally, we show the performance of our proposed framework in real network deployments.

We consider the uplink open-loop power control (OLPC) procedure in Long Term Evolution (LTE) cellular networks. We propose a UE based adaptive OLPC approach where a UE selectively, and autonomously, applies pre-determined offsets to the standardized open-loop power control parameters such as PO_UE_PUSCHand α as a function of its relative location. The proposed approach increases overall uplink signal to interference and noise ratio (UL SINR) at eNodeBs. As a result, UL cell throughput improves with no additional over the air signaling or extra processing load at eNodeBs. Our simulation results show that the UE-based adaptive OLPC approach significantly increases the center-cell UE's throughput at the expense of a slight, and controllable, throughput degradation of the UEs at the cell-edge.

Interference mitigation is one of the main challenges in LTE TDD networks, which requires that all cells in the network follow the same TDD UL/DL configuration. However, different UEs in the network may have different UL or DL traffic types, which may also vary with time; as a result, different cells may require different TDD UL/DL configurations in time. In this paper, we consider an LTE network, where each eNodeB in the network has backhaul links, i.e. X2 interfaces, only with its immediate neighboring eNodeBs. We investigate an adaptive optimization of TDD UL/DL configuration index in mean square error (MSE) sense based on the available UL and DL traffic of all RRC connected UEs in different cells within the network. We then propose a distributed algorithm, where all eNodeBs in the network will reach consensus on the optimum TDD UL/DL configuration through local communications with their neighboring eNodeBs over X2 interfaces. Our simulation results show that the LTE network, which is running the proposed algorithm, provides the better utilization of the available frequency-time resources and achieves a higher UE Throughput compared to a fixed TDD UL/DL configuration.

Level‐crossing (LC) analog‐to‐digital (A/D) converters can efficiently sample certain classes of signals. An LC A/D converter is a real‐time asynchronous system, which encodes the information of an analog signal into a sequence of non‐uniformly spaced time instants. In particular, this class of A/D converters uses an asynchronous data conversion approach, which is a power efficient technique. In this study, the authors propose adaptive and multi‐level adaptive LC sampling models as alternatives to conventional LC schemes and apply an iterative algorithm to improve the reconstruction quality of LC A/D converters. This simulation results show that multi‐level adaptive LC outperforms conventional A/D converters such as sigma‐delta A/D converters in terms of performance and computational complexity.

Due to the scarcity of spectrum below 3 GHz for wireless communications, there have been proposals to explore millimeter wave (mmWave) spectrum (3- 300 GHz) for commercial mobile applications. MmWave spectrum provides unique advantages such as availability of GHz bandwidth and use of antenna arrays with beamforming to compensate for path loss. While there exist well-established models for mmWave indoor non-line-of-sight (60 GHz) and mmWave outdoor line-of-sight (backhaul) communication, mmWave channels for outdoor, non-line-of-sight, mobile communication have not been explored sufficiently. We present penetration and reflection measurements for different materials and line-of-sight and non- line-of-sight measurements for outdoor mmWave mobile communication. We find that while well-known lossy objects such as human body and concrete can have poor penetration, they are good reflectors at these frequencies, enabling the receiver to capture secondary reflections for non-line-of-sight communication. We also show that a wide beam width, low gain antenna at the mobile receiver can capture more energy in scattered non-line-of-sight environments and thus, can provide more gain than a narrow beam, high gain antenna for mobile communication. Our initial measurements motivate utilizing mmWave frequencies for outdoor non-line- of-sight mobile communication.

In this paper, we are interested in fundamentally understanding the spatial predictability of wireless channels. We propose a probabilistic channel prediction framework for predicting the spatial variations of a wireless channel, based on a small number of measurements. By using this framework, we then develop a mathematical foundation for understanding the spatial predictability of wireless channels. More specifically, we characterize the impact of different environments, in terms of their underlying parameters, on wireless channel predictability. We furthermore show how sampling positions can be optimized to improve the prediction quality. Finally, we show the performance of the proposed framework in predicting (and justifying the predictability of) the spatial variations of real channels, using several measurements in our building.

In this paper, we propose to use binary consensus algorithms for distributed cooperative spectrum sensing in cog-nitive radio networks. We propose to use two binary approaches, namely diversity and fusion binary consensus spectrum sensing. The performance of these algorithms is analyzed over fading channels. The probability of networked detection and false alarm are characterized for the diversity case. We then show that binary consensus cooperative spectrum sensing is superior to quantized average consensus in terms of agility, given the same number of transmitted bits.

In this paper we consider estimating the spatial variations of a wireless channel based on a small number of measurements in a robotic network. We use a multi-scale probabilistic model in order to characterize the channel and develop an estimator based on this model. We show that our model-based approach can estimate the channel well for several scenarios, with only a small number of gathered measurements. We furthermore consider a sparsity-based channel estimation approach, in which we utilize the compressibility of the channel in the frequency domain. Our results show that this approach can also be effective in several scenarios. We then discuss the underlying tradeoffs between the two approaches. For the model-based approach, we show the impact of the error in the underlying model as well as the error in the estimation of the parameters of the model on the overall performance. For the sparsity-based approach, we show the impact of channel compressibility on the performance. Overall, the proposed framework can be utilized for communication-aware motion planning in robotic networks, where a prediction of the link qualities is needed.

In this paper we consider a network of distributed sensors that are trying to measure a parameter of interest cooperatively, by exchanging their acquired information repeatedly over fading channels. We consider different ways of using the available bandwidth, in terms of what each node can send to its neighbors. More specifically, we characterize the impact of local fusion and show how it is a suitable policy when graph connectivity is low. When poor link qualities are the main bottleneck, on the other hand, we show how a diversity approach can be more beneficial. The proposed framework highlights the underlying tradeoffs between fusion and diversity approaches in cooperative networks. It furthermore explores the impact of multiple sensing on the overall performance.

In this paper we consider a cooperative network that is trying to reach binary consensus over fading channels. We first characterize the impact of fading on network consensus by upper bounding the second largest eigenvalue of the underlying probability transition matrix in fading environments. Using the information of link qualities, we then propose a novel consensus-seeking protocol based on the best affine estimation of network state. We characterize the performance of our proposed strategy mathematically. We derive an approximated expression for the second largest eigenvalue in order to characterize the convergence rate. Our results show the impact of fading on network consensus. They furthermore indicate that the proposed technique can improve the consensus performance considerably.