Spatial correlation has been recognized by 3GPP as one of the key elements in millimeter-wave (mmWave) channel modeling. Correlated channel behavior is induced by macro objects, such as buildings, as well as by micro objects, including humans around the mmWave receivers. The 3GPP's three-dimensional (3D) spatially consistent channel model designed to capture these phenomena assumes a-priori knowledge of the correlation distance between the receivers. In this paper, we propose a novel spatially-consistent human body blockage state generation procedure, which extends the standardized 3D channel model by 3GPP to capture the correlation between the line-of-sight (LoS) links and the reflected cluster states affected by human body blockage. The proposed model is based on analytical expressions for the conditional link state probability, thus permitting the parametrization of the spatial field of receivers. It also does not require any a-priori information on the correlation distance as the latter is identified explicitly based on the environmental parameters. We compare the results for the proposed model with those obtained with the uncorrelated blockage model and conclude that in many special cases correlation manifests itself in quantitatively different propagation conditions experienced at the nearby receivers.

The use of extremely high frequency (EHF) or millimeter-wave (mmWave) band has attracted significant attention for the next generation wireless access networks. As demonstrated by recent measurements, mmWave frequencies render themselves quite sensitive to “blocking” caused by obstacles like foliage, humans, vehicles, etc. However, there is a dearth of analytical models for characterizing such blocking and the consequent effect on the signal reliability. In this paper, we propose a novel, general, and tractable model for characterizing the blocking caused by humans (assuming them to be randomly located in the environment) to mmWave propagation as a function of system parameters like transmitter-receiver locations and dimensions, as well as density and dimensions of humans. Moreover, the proposed model is validated using a ray-launcher tool. Utilizing the proposed model, the blockage probability is shown to increase with human density and separation between the transmitter-receiver pair. Furthermore, the developed analysis is shown to demonstrate the existence of a transmitter antenna height that maximizes the received signal strength, which in turn is a function of the transmitter-receiver distance and their dimensions.

Millimeter wave (mmWave) cellular systems will require high-gain directional antennas and dense base station (BS) deployments to overcome a high near-field path loss and poor diffraction. As a desirable side effect, high-gain antennas offer interference isolation, providing an opportunity to incorporate self-backhauling, i.e., BSs backhauling among themselves in a mesh architecture without significant loss in the throughput, to enable the requisite large BS densities. The use of directional antennas and resource sharing between access and backhaul links leads to coverage and rate trends that significantly differ from conventional UHF cellular systems. In this paper, we propose a general and tractable mmWave cellular model capturing these key trends and characterize the associated rate distribution. The developed model and analysis are validated using actual building locations from dense urban settings and empirically derived path loss models. The analysis shows that, in sharp contrast to the interference-limited nature of UHF cellular networks, the spectral efficiency of mmWave networks (besides the total rate) also increases with the BS density, particularly at the cell edge. Increasing the system bandwidth does not significantly influence the cell edge rate, although it boosts the median and peak rates. With self-backhauling, different combinations of the wired backhaul fraction (i.e., the fraction of BSs with a wired connection) and the BS density are shown to guarantee the same median rate (QoS).

Load balancing by proactively offloading users onto small and otherwise lightly-loaded cells is critical for tapping the potential of dense heterogeneous cellular networks (HCNs). Offloading has mostly been studied for the downlink, where it is generally assumed that a user offloaded to a small cell will communicate with it on the uplink as well. The impact of coupled downlink-uplink offloading is not well understood. Uplink power control and spatial interference correlation further complicate the mathematical analysis as compared to the downlink. We propose an accurate and tractable model to characterize the uplink SINR and rate distribution in a multi-tier HCN as a function of the association rules and power control parameters. Joint uplink downlink rate coverage is also characterized. Using the developed analysis, it is shown that the optimal degree of channel inversion (for uplink power control) increases with load imbalance in the network. In sharp contrast to the downlink, minimum path loss association is shown to be optimal for uplink rate. Moreover, with minimum path loss association and full channel inversion, uplink SIR is shown to be invariant of infrastructure density. It is further shown that a decoupled association-employing differing association strategies for uplink and downlink-leads to significant improvement in joint uplink-downlink rate coverage over the standard coupled association in HCNs.

The use of dense millimeter wave (mmWave) cellular networks with highly directional beamforming stands as an intriguing solution to the current spectrum congestion problem. Due to significantly different propagation characteristics at such high frequencies, however, the coverage and rate trends differ drastically from conventional microwave networks. This paper aims to gain insights into the coverage and rate performance of mmWave cellular networks in major metropolitan cities. Our results confirm that, unlike conventional cellular networks, mmWave networks operating at 73 GHz carrier frequency are pre-dominantly noise-limited. Though larger system bandwidth leads to higher peak rates, it does not improve the cell edge rates. It is observed that dense base station (BS) deployment is the key to achieve both better coverage and rates in mmWave cellular networks. Further, based on actual building locations, we show the inadequacy of existing blockage models and validate a better blockage model.

In this paper, we consider the broadcast channel with confidential messages and eavesdroppers (BCCE), where a multi-antenna base station simultaneously communicates to multiple potentially malicious users, in the presence of external eavesdroppers randomly located according to a Poisson point process (PPP). By using techniques from stochastic geometry and random matrix theory, we obtain explicit expressions for the secrecy outage probability and mean secrecy rate achievable with regularized channel inversion precoding. We show that both these metrics scale as -4fe, where N is the number of transmit antennas and Aeis the density of external eavesdroppers.

Base station (BS) cooperation is set to play a key role in managing interference in dense heterogeneous cellular networks (HCNs). Non-coherent joint transmission (JT) is particularly appealing due to its low complexity, smaller overhead, and ability for load balancing. However, a general analysis of this technique is difficult mostly due to the lack of tractable models. This paper addresses this gap and presents a tractable model for analyzing non-coherent JT in HCNs, while incorporating key system parameters such as user-centric BS clustering and channel-dependent cooperation activation. Assuming all BSs of each tier follow a stationary Poisson point process, the coverage probability for non-coherent JT is derived. Using the developed model, it is shown that for small cooperative clusters of small-cell BSs, non-coherent JT by small cells provides spectral efficiency gains without significantly increasing cell load. Further, when cooperation is aggressively triggered intra-cluster frequency reuse within small cells is favorable over intra-cluster coordinated scheduling.

In this paper, we consider the broadcast channel with confidential messages and external eavesdroppers (BCCE), where a multi-antenna base station simultaneously communicates to multiple potentially malicious users, in the presence of randomly located external eavesdroppers. Using the proposed model, we study the secrecy rates achievable with regularized channel inversion (RCI) precoding by performing a large-system analysis that combines results from stochastic geometry and random matrix theory, where the number of users K and the number of transmit antennas N both grow to infinity in a fixed ratio. We obtain explicit expressions for the probability of secrecy outage and an upper bound on the rate loss due to the presence of external eavesdroppers. We show that both these quantities scale as \fraclambda_esqrt{N} as the density of external eavesdroppers λ_e grows, irrespective of their collusion strategy. Furthermore, we derive a practical rule for the choice of the regularization parameter, which is agnostic of channel state information and location of eavesdroppers, and yet provides close to optimal performance.

In heterogeneous cellular networks (HCNs), it is desirable to offload mobile users to small cells, which are typically significantly less congested than the macrocells. To achieve sufficient load balancing, the offloaded users often have much lower SINR than they would on the macrocell. This SINR degradation can be partially alleviated through interference avoidance, for example time or frequency resource partitioning, whereby the macrocell turns off in some fraction of such resources. Naturally, the optimal offloading strategy is tightly coupled with resource partitioning; the optimal amount of which in turn depends on how many users have been offloaded. In this paper, we propose a general and tractable framework for modeling and analyzing joint resource partitioning and offloading in a two-tier cellular network. With it, we are able to derive the downlink rate distribution over the entire network, and an optimal strategy for joint resource partitioning and offloading. We show that load balancing, by itself, is insufficient, and resource partitioning is required in conjunction with offloading to improve the rate of cell edge users in co-channel heterogeneous networks.

This paper presents a tractable model for analyzing noncoherent joint-transmission base station (BS) cooperation, taking into account the irregular BS deployment typically encountered in practice. In addition to cellular-network specific aspects, such as BS density, channel fading, average path loss, and interference, the model also captures relevant cooperation mechanisms, including user-centric BS clustering and channel-dependent cooperation activation. The locations of all BSs are modeled by a Poisson point process. Using tools from stochastic geometry, the signal-to-interference-plus-noise ratio (SINR) distribution with cooperation is precisely characterized in a generality-preserving form. The result is then applied to practical design problems of recent interest. We find that increasing the network-wide BS density improves the SINR, while the gains increase with the path loss exponent. For pilot-based channel estimation, the average spectral efficiency saturates at cluster sizes of around seven BSs for typical values, irrespective of backhaul quality. Finally, it is shown that intra-cluster frequency reuse is favorable in moderately loaded cells with generous cooperation activation, while intra-cluster coordinated scheduling may be better in lightly loaded cells with conservative cooperation activation.

Mobile users proactively offloaded from macro cells to small cells in heterogeneous cellular networks (HCNs) encounter less congestion but higher interference. Resource partitioning protects the offloaded users from macro tier interference by muting the macro tier on certain fraction of resources and scheduling the offloaded users on those resources. However, the extent to which joint offloading and resource partitioning is needed remains unanswered. This is partly due to the lack of tractable frameworks to analyze downlink rate in the presence of such techniques. In this paper, we develop a tractable framework to analyze joint resource partitioning and offloading in a two-tier cellular network and derive the downlink rate distribution over the entire network. Each tier of base stations is modeled as a Poisson point process (PPP) where each tier differs in transmit power and deployment density. Using the developed analysis, we show that the optimal combination of load balancing and resource partitioning improves the rate of cell edge users in co-channel heterogeneous networks.

Current wireless networks are becoming increasingly heterogeneous both in terms of the radio access technologies (RATs) and the base station capabilities. Downlink rate or throughput in such heterogeneous networks is a crucial metric and has been primarily studied using system level simulations. To derive the distribution of rate analytically we develop a fairly general tractable model that consists of M different RATs, each deploying up to K different classes of access points (APs), where each class differs in transmit power, path loss exponent, and deployment density. Each class of APs is modeled as an independent Poisson point process (PPP), with mobile user locations modeled as another independent PPP, all channels further consisting of i.i.d. Rayleigh fading. Using a weighted association strategy, the distribution of rate over the entire network is derived. Further, it is shown that in a two-RAT setting there exists an optimum fraction of traffic that should be associated with each RAT to maximize rate coverage, defined as the fraction of users achieving a given rate.

Pushing data traffic from cellular to WiFi is an example of inter radio access technology (RAT) offloading. While this clearly alleviates congestion on the over-loaded cellular network, the ultimate potential of such offloading and its effect on overall system performance is not well understood. To address this, we develop a general and tractable model that consists of M different RATs, each deploying up to K different tiers of access points (APs), where each tier differs in transmit power, path loss exponent, deployment density and bandwidth. Each class of APs is modeled as an independent Poisson point process (PPP), with mobile user locations modeled as another independent PPP, all channels further consisting of i.i.d. Rayleigh fading. The distribution of rate over the entire network is then derived for a weighted association strategy, where such weights can be tuned to optimize a particular objective. We show that the optimum fraction of traffic offloaded to maximize SINR coverage is not in general the same as the one that maximizes rate coverage, defined as the fraction of users achieving a given rate.

Quality of Experience (QoE) has taken a center stage in the performance evaluation of multimedia delivery technologies. The Third Generation Partnership Project (3GPP) Long Term Evolution (LTE) system is the latest generation of wireless cellular technology expected to deliver higher data rates and meet the burgeoning data demand. With the projected dominant share of video services in mobile traffic, providing satisfactory QoE to video users is a key objective for LTE system design. In this paper, we present a QoE-based evaluation methodology to assess the LTE system video capacity in terms of the number of unicast video consumers that can be simultaneously supported for a given target QoE. We define and use the notion of rebuffering outage capacity to quantify the video service capacity. Our evaluation further incorporates adaptive streaming, a promising technology for video delivery over wireless, and presents its consequent QoE-capacity tradeoff. The impact of QoE-based outage criteria is also investigated on the downlink video capacity. Finally, we propose a QoE-aware radio resource management (RRM) framework which allows the network operator to further enhance the video capacity. Our results demonstrate that there is a significant potential to optimize video capacity through QoE awareness both at the application level and radio access network (RAN) level.

The unpredictability of the wireless medium poses a major challenge to delivering a high quality of experience (QoE) for real-time video services. Bursty co-channel interference is a prominent cause of wireless throughput variability, which leads to video QoE degradation, even for a fixed average channel quality. In this paper, we propose and analyze a network-level resource management algorithm termed interference shaping to smooth out the throughput variations (and hence improve the QoE) of video users by decreasing the peak rate of co-channel best effort users. Wireless link capacity variations are mapped to the real-time video packet loss rate, and the interference shaping QoE gain for video users is quantified by benchmarking against a modified multi-scale structural similarity (H-MS-SSIM) index. H-MS-SSIM is an accurate perceptual video quality metric that incorporates the important hysteresis effect whereby the current QoE (which is subjective) may strongly depend on the recent past. The proposed technique increases mean QoE and reduces the QoE variability over time, with a net perceptual increase of about 2-3x in illustrative settings while incurring insignificant decrease in the QoE for co-channel best effort users. Interference shaping can be implemented in both unicast and multicast real-time video streaming with much higher potential gains for multicast.

A new blind equalisation algorithm is presented for multi-level quadrature amplitude modulation schemes. The presented technique provides faster convergence and improved performance in terms of mean square error, which is demonstrated through various simulations. A mathematical analysis is also presented for the steady-state mean square error, which verifies the better performance of the proposed scheme over the conventional ones. The performance is also compared on the basis of inter-symbol interference metric. A computationally efficient implementation of the proposed algorithm is also presented. Simulations are done with various types of fading channels ranging from highly degrading Hoyt fading channels to moderate Rayleigh fading channels.

We develop an alternate characterization of the statistical distribution of the inter-cell interference power observed in the uplink of CDMA systems. We show that the lognormal distribution better matches the cumulative distribution and complementary cumulative distribution functions of the uplink interference than the conventionally assumed Gaussian distribution and variants based on it. This is in spite of the fact that many users together contribute to uplink interference, with the number of users and their locations both being random. Our observations hold even in the presence of power control and cell selection, which have hitherto been used to justify the Gaussian distribution approximation. The parameters of the lognormal are obtained by matching moments, for which detailed analytical expressions that incorporate wireless propagation, cellular layout, power control, and cell selection parameters are developed. The moment-matched lognormal model, while not perfect, is an order of magnitude better in modeling the interference power distribution.

A detailed characterization of interference power statistics in CDMA systems is of considerable practical and theoretical interest. Such a characterization for uplink inter-cell interference has been difficult because of transmit power control, randomness in the number of interfering mobile stations, and randomness in their locations. We develop a new method to model the uplink inter-cell interference power as a lognormal distribution, and show that it is an order of magnitude more accurate than the conventional Gaussian approximation even when the average number of mobile stations per cell is relatively large and even outperforms the moment-matched lognormal approximation considered in the literature. The proposed method determines the lognormal parameters by matching its moment generating function with a new approximation of the moment generating function for the inter-cell interference. The method is tractable and exploits the elegant spatial Poisson process theory. Using several numerical examples, the accuracy of the proposed method in modeling the probability distribution of inter-cell interference is verified for both small and large values of interference.