Network lifetime is a crucial performance metric to evaluate data-gathering wireless sensor networks

(WSNs) where battery-powered sensor nodes periodically sense the environment and forward collected

samples to a sink node. In this paper, we propose an analytic model to estimate the entire network

lifetime from network initialization until it is completely disabled, and determine the boundary of

energy hole in a data-gathering WSN. Specifically, we theoretically estimate the traffic load, energy

consumption, and lifetime of sensor nodes during the entire network lifetime. Furthermore, we

investigate the temporal and spatial evolution of energy hole and apply our analytical results to WSN

routing in order to balance the energy consumption and improve the network lifetime. Extensive

simulation results are provided to demonstrate the validity of the proposed analytic model in estimating

the network lifetime and energy hosle evolution process.

ETPL

SNP -001 Lifetime and Energy Hole Evolution Analysis in Data-Gathering Wireless

Sensor Networks

The cognitive femtocell has emerged as an exciting technology to solve the indoor coverage problem

in future cellular networks. Recently, several technical issues for the cognitive femtocell have been

studied, e.g., spectrum sharing and interference mitigation. However, the incentive method that is very

important for practical hybrid access cognitive femtocell deployment has not been well investigated.

In this paper, we propose a new dynamic spectrum allocation method for the hybrid access cognitive

femtocell. In the proposed method, the macro base station (BS) allocates a portion of subchannels to

the femto access point (FAP) to spur the FAP to serve the macro users (MUs). Then, the FAP allocates

the subchannels and power to maximize the femtocell network utility, whereas the throughput of the

served MUs is guaranteed. Moreover, we formulate the corresponding resource allocation problem as

a sum-utility maximization problem and propose an optimization method to solve it via the dual

decomposition method. Simulation results show that both the wireless service provider and the

femtocell could benefit from the proposed method.

ETPL

SNP - 002 Dynamic Spectrum Allocation for the Downlink of OFDMA-Based

Hybrid-Access Cognitive Femtocell Networks

In a large and complex distributed sensor network, the individual sensor directly communicates only

with its neighboring sensors. Despite being restricted to such local communications, the network itself

should be self-organizing to maintain certain desirable properties, such as network connectivity and

lifetime maximization. Topology control is a technique to determine the optimal transmit parameters

for each sensor, such that the network topology has the best possible network performance. In this

paper, we investigate a dynamic topology control scheme to improve the network lifetime for wireless

sensor networks in the presence of selfish sensors. A non-cooperative game aided topology control

approach is developed for designing energy-efficient and energy balanced network topologies

dynamically. Each sensor in the topology control game tries to minimize its unwillingness for

constructing a connected network according to its residual energy and transmit power. We prove the

existence of Nash equilibrium (NE) and demonstrate that the NE is Pareto optimal as well. Specifically,

a fully distributed algorithm-topology control with lifetime extension (TCLE)-is proposed to construct

dynamic network topologies. The topologies derived by TCLE algorithm can improve the network

lifetime significantly as compared with existing algorithms. Simulations demonstrate the efficiency of

our TCLE algorithm.

ETPL

SNP -003 Distributed Topology Control with Lifetime Extension Based on Non-

Cooperative Game for Wireless Sensor Networks

The capacity of the intensity-modulation direct-detection optical broadcast channel (OBC) is

investigated, under both average and peak intensity constraints. An outer bound on the capacity region

is derived by adapting Bergmans' approach to the OBC. Inner bounds are derived by using

superposition coding with either truncated-Gaussian (TG) distributions or discrete distributions. While

the discrete distribution achieves higher rates, the TG distribution leads to a simpler representation of

the achievable rate region. At high signal-to-noise ratio (SNR), it is shown that the TG distribution is

nearly optimal. It achieves the symmetric-capacity within a constant gap (independent of SNR), which

approaches half a bit as the number of users grows. It also achieves the capacity region within a constant

gap. At low SNR, it is shown that on-off keying (OOK) with time-division multiple-access (TDMA)

is optimal. This is interesting in practice since both OOK and TDMA have low complexity. At

moderate SNR (typically [0, 8] dB), a discrete distribution with a small alphabet size achieves fairly

good performance.

ETPL

SNP - 004 On the Capacity of the Intensity-Modulation Direct-Detection Optical

Broadcast Channel

Multiple signal classification (MUSIC) can be seen as one of the most popular solutions to the

direction-of-arrival estimation problem in array signal processing. Traditionally, this method utilizes

the whole array observations to construct the noise subspace. In this letter, based on the randomly

chosen sensors outputs, we exploit the Nyström method to approximate the noise subspace, and

develop a low-complexity modified MUSIC version for far-field sources localization. Computer

simulations confirm that the novel algorithm using only one-fourth sensors outputs provides almost the

same estimation performance as its traditional version.

ETPL

SNP -005 Modified MUSIC Algorithm for DOA Estimation with Nyström

Approximation

Multiple access broadcasting (MABC) is a spectral efficient protocol in a two-way relay channel

(TWRC). The drawback of this protocol in decode-and-forward (DF) mode is decoding complexity at

the relay side. Erroneous decoding at the relay can lead to error propagation that degrades the

performance of all relay networks operating in the DF mode. In this paper, a maximum likelihood (ML)

detector at the multiple-antenna relay is used, and it is proved that this decoding rule can achieve full

diversity order at the multiple access (MAC) phase of the MABC protocol. Then, a new beam forming

algorithm named as channel alignment is proposed, and it is shown that it achieves full diversity order.

A new relay selection algorithm is proposed, and a tight upper bound for the bit error rate (BER) of

this scheme is derived. It is proved that it achieves full diversity order. The analytical results and BER

performance of the proposed schemes are investigated and compared through simulations.

ETPL

SNP - 006 New Beam forming and Relay Selection for Two-Way Decode-and-

Forward Relay Networks

This paper proposes a relay selection (RS) scheme that aims to improve the end-to-end symbol error

rate (SER) performance of a two-way relay network (TWRN). The TWRN consists of two single-

antenna sources and multiple relays employing a decode-and-forward (DF) protocol. It is shown that

the SER performance is determined by the minimum decision distance (DD) observed in the TWRN.

However, the minimum DD is likely to be made arbitrarily small by channel fading. To tackle this

problem, a phase rotation (PR)-aided RS scheme is proposed to enlarge the minimum DD, which, in

turn, improves the SER performance. The proposed PR-based scheme rotates the phases of the

transmitted symbols of one source and of the selected relay according to the channel state information

(CSI), aiming for increasing all DDs to be above a desired bound. The lower bound is further optimized

by using a MaxMin-RS criterion associated with the channel gains. It is demonstrated that the PR-

aided MaxMin-RS approach achieves full diversity gain and improved array gain. Furthermore,

compared with the existing DF-based schemes, the proposed scheme allows more flexible relay

antenna configurations.

ETPL

SNP -007 Phase-Rotation-Aided Relay Selection in Two-Way Decode-and-Forward

Relay Networks

Green communications is an inevitable trend for future communication network design, especially for

a cognitive radio network. Power allocation strategies are of crucial importance for green cognitive

radio networks. However, energy-efficient power allocation strategies in green cognitive radio

networks have not been fully studied. Energy efficiency maximization problems are analyzed in delay-

insensitive cognitive radio, delay-sensitive cognitive radio, and simultaneously delay-insensitive and

delay-sensitive cognitive radio, where a secondary user coexists with a primary user and the channels

are fading. Using fractional programming and convex optimization techniques, energy-efficient

optimal power allocation strategies are proposed subject to constraints on the average interference

power, along with the peak/average transmit power. It is shown that the secondary user can achieve

energy efficiency gains under the average transmit power constraint, in contrast to the peak transmit

power constraint. Simulation results show that the fading of the channel between the primary user

transmitter and the secondary user receiver and the fading of the channel between the secondary user

transmitter and the primary user receiver are favorable to the secondary user with respect to the energy

efficiency maximization of the secondary user, whereas the fading of the channel between the

secondary user transmitter and the secondary user receiver is unfavorable to the secondary user.

ETPL

SNP - 008 Energy-Efficient Optimal Power Allocation for Fading Cognitive Radio

Channels: Ergodic Capacity, Outage Capacity, and Minimum-Rate Capacity

We consider a full-duplex (FD) decode-and-forward system in which the time-switching protocol is

employed by the multiantenna relay to receive energy from the source and transmit information to the

destination. The instantaneous throughput is maximized by optimizing receive and transmit

beamformers at the relay and the time-split parameter. We study both optimum and suboptimum

schemes. The reformulated problem in the optimum scheme achieves closed-form solutions in terms

of transmit beamformer for some scenarios. In other scenarios, the optimization problem is formulated

as a semidefinite relaxation problem and a rank-one optimum solution is always guaranteed. In the

suboptimum schemes, the beamformers are obtained using maximum ratio combining, zero-forcing,

and maximum ratio transmission. When beamformers have closed-form solutions, the achievable

instantaneous and delay-constrained throughput are analytically characterized. Our results reveal that

beamforming increases both the energy harvesting and loop interference suppression capabilities at the

FD relay. Moreover, simulation results demonstrate that the choice of the linear processing scheme as

well as the time-split plays a critical role in determining the FD gains.

ETPL

SNP - 009 Throughput Analysis and Optimization of Wireless-Powered Multiple

Antenna Full-Duplex Relay Systems

Wireless sensor technologies have been in development for a long time. Many wireless sensor

technology studies have been conducted on improving device performance. Huge technology resources

were recently found under the ocean. Previous research methods cannot mapping the underwater

environment directly. Underwater wireless sensor networks are being developed for ocean resources

deep water exploration. However, little agreement has been reached on an appropriate underwater

dedicated distribution model. We focus on accurately depicting 3-D underwater sensor placement

based on the coverage issue. Therefore, we design a novel distribution model using the spline function

for more accurate underwater wireless sensor placement.

ETPL

SNP - 010 A Robust Coverage Scheme for UWSNs Using the Spline Function

In this paper, we consider a two-path relay channel (TPRC) with the assistance of two decode-and-

forward relays alternatively. Upon successfully decoding a source packet, a relay proceeds to forward

the decoded packet to the destination, which brings an interference to the other relay. Owing to this

inter-relay interference, the decoding result at one relay in the current time slot depends on the decoding

result at the other relay in the previous time slot. Exploiting this single-slot memory, the decoding

performance of the relays is analyzed using a Markov chain. Furthermore, since the relay transmission

is one slot behind the source transmission, the neighboring source packets received at the destination

interfere with one another. Then depending on whether a packet is subject to the residual interference

from its previous packet, the decoding performance of the destination can be similarly analyzed using

a Markov chain. Thus we can obtain the overall outage probability of TPRC in closed-form

expressions. Simulation results are provided to demonstrate the performance of the considered TPRC,

where the effects of various parameters are evaluated. By comparisons with existing works, a

reasonably good performance is achieved for TPRC with only a single-slot delay.

ETPL

SNP - 011 Outage Analysis of Co-Operative Two-Path Relay Channels

Explicit derivation of interferences in hexagonal wireless networks has been widely considered

intractable and requires extensive computations with system level simulations. In this paper, we

fundamentally tackle this problem and explicitly evaluate the downlink interference-to-signal ratio

(ISR) for any mobile location m in a hexagonal wireless network, whether composed of omni-

directional or tri-sectorized sites. The explicit formula of ISR is a very convergent series on m and

involves the use of Gauss hypergeometric and Hurwitz Riemann zeta functions. Besides, we establish

simple identities that well approximate this convergent series and turn out quite useful compared to

other approximations in literature. The derived expression of ISR is easily extended to any frequency

reuse pattern. Moreover, it is also exploited in the derivation of an explicit form of SINR distribution

for any arbitrary distribution of mobile user locations, reflecting the spatial traffic density in the

network. Knowing explicitly about interferences and SINR distribution is very useful information in

capacity and coverage planning of wireless cellular networks and particularly for macro-cells' layer

that forms almost a regular point pattern.

ETPL

SNP - 012 Analytical Tractability of Hexagonal Network Model with Random User

Location

The recent advance in radio-frequency (RF) wireless energy transfer (WET) has motivated the study

of wireless powered communication network (WPCN) in which distributed wireless devices are

powered via dedicated WET by the hybrid access-point (H-AP) in the downlink (DL) for uplink (UL)

wireless information transmission (WIT). In this paper, by utilizing the cognitive radio (CR) technique,

we study a new type of CR enabled secondary WPCN, called cognitive WPCN, under spectrum sharing

with the primary wireless communication system. In particular, we consider a cognitive WPCN,

consisting of one single H-AP with constant power supply and distributed wireless powered users,

shares the same spectrum for its DL WET and UL WIT with an existing primary communication link,

where the WPCN's WET/WIT and the primary link's WIT may interfere with each other. Under this

new setup, we propose two coexisting models for spectrum sharing of the two systems, namely

underlay and overlay-based cognitive WPCNs, depending on different types of knowledge on the

primary user transmission available for the cognitive WPCN. For each model, we maximize the sum-

throughput of the cognitive WPCN by optimizing its transmission under different constraints applied

to protect the primary user transmission. Analysis and simulation results are provided to compare the

sum-throughput of the cognitive WPCN versus the achievable rate of the primary user under two

proposed coexisting models. It is shown that the overlay based cognitive WPCN outperforms the

underlay based counterpart, thanks to its fully co-operative WET/WIT design with the primary WIT,

while also requiring higher complexity for implementation.

ETPL

SNP - 013 Cognitive Wireless Powered Network: Spectrum Sharing Models and

Throughput Maximization.

Spectrum sharing and energy harvesting are promising techniques to improve the bandwidth and

energy efficiencies to meet the ever-growing demand of wireless data transmissions. In this paper, we

propose an efficient relay-based spectrum sharing protocol in the cognitive radio network, where the

secondary user (SU) can implicitly harvest the radio frequency (RF) energy from the primary user (PU)

transmissions. Using the harvested energy, the SU can assist the PU transmission to exchange for the

opportunity of spectrum access. Both the Alamouti coding and the superposition coding techniques are

adopted by the transmitters to facilitate the primary data relaying and the secondary data transmission

simultaneously. The joint decoding and the interference cancelation techniques are adopted by the

receivers to retrieve the desired signals. The more power allocated for the primary data relaying, the

higher throughput can be achieved for the PU, while the less throughput is available for the SU with

less energy remained. The optimal power allocation is numerically determined by maximizing the SU

throughput while guaranteeing the PU throughput. Numerical results show that our protocol can greatly

improve the system throughput and the impacts of various system settings are revealed for the network

deployment.

ETPL

SNP - 014 Relay-Based Spectrum Sharing With Secondary Users Powered by

Wireless Energy Harvesting

This work proposes adaptive buffer-aided distributed space-time coding schemes and algorithms with

feedback for wireless networks equipped with buffer-aided relays. The proposed schemes employ a

maximum likelihood receiver at the destination, and adjustable codes subject to a power constraint

with an amplify-and-forward cooperative strategy at the relays. The adjustable codes are part of the

proposed space-time coding schemes and the codes are sent back to relays after being updated at the

destination via feedback channels. Each relay is equipped with a buffer and is capable of storing blocks

of received symbols and forwarding the data to the destination if selected. Different antenna

configurations and wireless channels, such as static block fading channels, are considered. The effects

of using buffer-aided relays to improve the bit error rate (BER) performance are also studied.

Adjustable relay selection and optimization algorithms that exploit the extra degrees of freedom of

relays equipped with buffers are developed to improve the BER performance. We also analyze the

pairwise error probability and diversity of the system when using the proposed schemes and algorithms

in a cooperative network. Simulation results show that the proposed schemes and algorithms obtain

performance gains over previously reported techniques.

ETPL

SNP - 015 Adaptive Buffer-Aided Distributed Space-Time Coding for Cooperative

Wireless Networks

In this paper, we propose an optimal relay transmission policy by using a stochastic energy harvesting

(EH) model for the EH two-way relay network, wherein the relay is solar-powered and equipped with

a finite-sized battery. In this policy, the long-term average outage probability is minimized by adapting

the relay transmission power to the wireless channel states, battery energy amount, and causal solar

energy states. The designed problem is formulated as a Markov decision process (MDP) framework,

and conditional outage probabilities for both decode-and-forward (DF) and amplify-and-forward (AF)

cooperation protocols are adopted as the reward functions. We uncover a monotonic and bounded

differential structure for the expected total discounted reward, and prove that such an optimal

transmission policy has a threshold structure with respect to the battery energy amount in sufficiently

high SNRs. Finally, the outage probability performance is analyzed and an interesting saturated

structure for the outage performance is revealed, i.e., the expected outage probability converges to the

battery empty probability in high SNR regimes, instead of going to zero. Furthermore, we propose a

saturation-free condition that can guarantee a zero outage probability in high SNRs. Computer

simulations confirm our theoretical analysis and show that our proposed optimal transmission policy

outperforms other compared policies.

ETPL

SNP - 016 On Outage Probability for Two-Way Relay Networks with Stochastic

Energy Harvesting

Multiuser MIMO (MU-MIMO) in general, and block diagonalization (BD) in particular, are playing a

prominent role toward the achievement of higher spectral efficiencies in modern OFDMA-based

wireless networks. The utilization of such techniques necessarily has implications in the scheduling

and resource allocation processes taking care of assigning subcarriers, power, and transmission modes

to the different users. In this paper, a framework for channel- and queue-aware scheduling and resource

allocation for BD-based MU-MIMO-OFDMA wireless networks is introduced. In particular, using an

SNR-based abstraction of the physical layer, the proposed design is able to cater for different BD-MU-

MIMO processing schemes [co-ordinated Tx-Rx (CTR) or receive antenna selection (RAS)], uniform

or adaptive power allocation (UPA/APA), continuous or discrete rate allocation (CRA/DRA), and

many different scheduling rules. Additionally, the different strategies are complemented by a new

greedy user/stream selection algorithm that is shown to perform very close to the optimal user/stream

selection policy at a much lower complexity. Results using system parameters typically found in 4G

networks reveal that, in most cases, low-complexity solutions (RAS-, UPA-based) achieve a

performance close to the one attained by their more complex counterparts (CTR-, APA-based).

ETPL

SNP - 017 Scheduling and Resource Allocation in Downlink Multiuser MIMO-

OFDMA Systems

Two key traits of 5G cellular networks are much higher base station (BS) densities-especially in the

case of low-power BSs-and the use of massive MIMO at these BSs. This paper explores how massive

MIMO can be used to jointly maximize the offloading gains and minimize the interference challenges

arising from adding small cells. We consider two interference management approaches: joint

transmission (JT) with local precoding, where users are served simultaneously by multiple BSs without

requiring channel state information exchanges among cooperating BSs, and resource blanking, where

some macro BS resources are left blank to reduce the interference in the small cell downlink. A key

advantage offered by massive MIMO is channel hardening, which enables to predict instantaneous

rates a priori. This allows us to develop a unified framework, where resource allocation is cast as a

network utility maximization (NUM) problem, and to demonstrate large gains in cell-edge rates based

on the NUM solution. We propose an efficient dual subgradient based algorithm, which converges

towards the NUM solution. A scheduling scheme is also proposed to approach the NUM solution.

Simulations illustrate more than 2x rate gain for 10th percentile users vs. an optimal association without

interference management.

ETPL

SNP - 018 User Association and Interference Management in Massive MIMO

HetNets

As a promising solution for handling super dense wireless networks, wireless local area networks

(WLANs) have been intensively considered due to their wide availability. However, the contention-

based MAC protocol in WLANs should be modified because of its inefficiency. To this end, we

consider a recently proposed novel MAC protocol called the renewal access protocol (RAP). With the

RAP, we analyze two strategies for resolving collisions efficiently and achieving optimal throughput

performance in super dense WLANs: strategies without and with grouping. First, we analyze the

asymptotic behavior of the RAP itself (i.e., without grouping) as the number of terminals goes to

infinity. We show that the RAP can achieve optimal throughput even in super dense WLANs and the

relevant optimal access probability can be derived in a closed form. Second, we propose a grouping

strategy in the RAP called the grouped RAP (G-RAP). While a grouping strategy in the IEEE 802.11ah

standard is based on time division, our G-RAP is based on transmission attempts. So the G-RAP does

not waste channel resources. We show that the G-RAP achieves the optimal network throughput for

any group structure if terminals use the optimal access probability that we derive. Our analytical results

are validated by simulation.

ETPL

SNP - 019 Throughput Performance Optimization of Super Dense Wireless

Networks With the Renewal Access Protocol

Multihop spatial time division multiple access (STDMA) medium access control (MAC) protocols

constitute an important building block of wireless networks. There are not many practical power

control algorithms that can optimally tradeoff power consumption against transmission rates with a

reasonable computational complexity. In this paper, we introduce an energy-efficient distributed power

control algorithm for STDMA MAC protocols. The motivation for this study is two fold, namely,

maximizing the spatial reuse of the system resources and maximizing power efficiency. We develop a

mathematical formulation for maximizing spatial reuse and power efficiency under discrete SINR and

rate constrains. After proving that power is a convex function of data rates in our problem, we

demonstrate that our problem in simultaneous transmission environments can be reduced to a linear

programming (LP) problem. Then, we solve this LP problem using dynamic programming (DP).

Finally, based on our proposed solution, we propose a low complexity optimal power control (OPC)

algorithm which can be generically embedded within any existing STDMA MAC protocol. Through

analytical and experimental studies, we show that our power control algorithm cannot only

significantly improve the throughput, power consumption, and delay performance of STDMA MAC

protocols compared to their baseline alternatives, but also outperform existing STDMA algorithms.

ETPL

SNP - 020 An optimal power control algorithm for STDMA MAC protocols in

multihop wireless networks

We consider a wireless transmission scheme based on randomized space-time spreading (STS) systems

over massive multiple-input multiple-output (MIMO) channels. In the systems, the signals are spread

by spreading matrices over time and antenna domains at the transmitter. We propose that the spreading

matrices are generated from binary pseudo-noise (PN) and mutual quasi-orthogonal sequences, by

partitioning each of the sequences into subsequences treated as real and imagination parts of rows or

columns of a spreading matrix. We give a likelihood ascent search (LAS) detector for the STS systems

and analyze its computational complexity. The number of multiplication and division operations for

the LAS detector does not depend on its iterative process and the number of chips in the bit period. We

use Monte Carlo method to solve the bit error rates (BERs) of the STS systems and to count numbers

of evaluating bits in the LAS detectors. We choose the system parameters, such as the number of chips

in the bit period and the transmission bit rates, according to BER performance and/or the numbers of

evaluating bits attained from Monte-Carlo method. With the spreading matrices, the detection, and the

suitable parameters, the STS systems achieve BERs near to their single-bit performance, which is the

BER of a STS system with a single bit spread by orthonormal sequences at each transmitting antenna.

We also compare our systems with V-BLAST-like spatial multiplexing systems with BPSK signals to

show the improvement of the BER performance.

ETPL

SNP - 022 A Randomized Space-Time Spreading Scheme for Massive MIMO

Channels

In this paper, we propose a novel algorithm to maximize the sum-rate in interference-limited scenarios

where each user decodes its own message with the presence of unknown interferences and noise. The

problem of adapting the transmit and receive filters of the users to maximize the sum-rate with a

transmit power constraint is nonconvex. Our novel approach is to formulate the sum-rate maximization

problem as an equivalent multiconvex optimization problem by adding two sets of auxiliary variables.

An iterative algorithm, which alternatingly adjusts the system variables and the auxiliary variables is

proposed to solve the multiconvex optimization problem and we show that the algorithm converges to

a stationary point. The proposed algorithm is applied to a downlink cellular scenario consisting of

several cells each of which contains a base station serving several mobile stations. We examine the two

cases, with or without several half-duplex amplify-and-forward relays assisting the transmission. A

sum power constraint at the base stations and at the relays are assumed. The applicability of our

approach to the individual power constraints case is also shown. Finally, we show that the proposed

multiconvex formulation of the sum-rate maximization problem is applicable to many other wireless

systems in which the estimated data symbols are multiaffine functions of the system variables.

ETPL

SNP - 021 Maximizing the Sum Rate in Cellular Networks Using Multiconvex

Optimization

Interference alignment (IA) is a promising transmission technology enabling, essentially, the maximum

achievable degrees of freedom (DOF) in K-user multiple-input-multiple-output (MIMO) interference

channels. The ideal DOF of IA systems have been obtained using independent MIMO channels, which

is, usually rarely observed in reality, particularly in indoor environments. Therefore, the data sum rate

and symbol error-rate of IA are dramatically degraded in real-world scenarios since the correlation

between MIMO channels decreases the signal-to-noise ratio (SNR) of the received signal after

alignment. For this reason, an acceptable sum rate of IA in real MIMO-orthogonal frequency-division

multiplexing (MIMO-OFDM) interference channels was obtained in the literature by modifying the

distance between network nodes and the separation between the antennas within each node to minimize

the spatial correlation. In this paper, we propose to apply transmit antenna selection to MIMO-OFDM

IA systems either through bulk or per-subcarrier selection, aiming at improving the sum-rate and/or

error-rate performance under real-world channel circumstances, while keeping the minimum spatial

antenna separation of 0.5 wavelengths within each node. Two selection criteria are considered:

maximum sum rate (Max-SR) and minimum error rate (Min-ER). To avoid subcarrier imbalance across

the antennas of each user, which is caused by per-subcarrier selection, a constrained per-subcarrier

antenna selection is operated. Furthermore, a suboptimal antenna selection algorithm is proposed to

reduce the computational complexity of the optimal algorithm. An experimental validation of MIMO-

OFDM IA with antenna selection in an indoor wireless network scenario is presented. The

experimental results are compared with deterministic channels that are synthesized using hybrid

electromagnetic (EM) ray-tracing models. Our performance evaluation shows that the practical

feasibility of MIMO-OFDM IA systems is signific- ntly increased by antenna selection in real-world

scenarios.

ETPL

SNP - 023 Antenna Selection for Reliable MIMO-OFDM Interference Alignment

Systems: Measurement-Based Evaluation

Multiuser orthogonal frequency-division multiplexing (OFDM) and multiple-output multiple-output

(MIMO) have been widely adopted to enhance the system throughput and combat the detrimental

effects of wireless channels. Interference alignment has been proposed to exploit interference to enable

concurrent transmissions of multiple signals. In this paper, we investigate how to combine these

techniques to further enhance the system throughput. We first reveal the unique characteristics and

challenges brought about by using interference alignment in diagonal channels. We then derive a

performance bound for the multiuser (MIMO) OFDM interference alignment system under practical

constraints and show how to achieve this bound with a decomposition approach. The superior

performance of the proposed scheme is validated with simulations.

ETPL

SNP - 024 Interference Alignment Improves the Capacity of OFDM Systems

This paper investigates the effect of various operation parameters on the downlink user performance

in overlaid small-cell networks. The case study considers closed-access small cells (e.g., femtocells),

wherein only active authorized user equipment (UE) can be served, each of which is allocated a single

downlink channel at a time. On the other hand, the macrocell base station can unconditionally serve

macrocell UEs that exist inside its coverage space. The available channels can be shared

simultaneously in the macrocell network and the femtocell network. Moreover, a channel can be reused

only at the macrocell base station. The analysis provides quantitative approaches to model UE

identities, their likelihoods of being active, and their likelihoods of producing interference, considering

UE classifications, locations, and access capabilities. Moreover, it develops models for various

interference sources observed from effective interference femtocells, considering femtocells capacities

and operation conditions. The associated formulations to describe a desired UE performance and the

impact of the number of available channels and the adopted channel assignment approach are

thoroughly investigated. The results are generally presented for any channel models of interference

sources and the desired source of the served UE. Moreover, specific channel models are then adopted,

for which generalized closed-form analytical results for the desired UE outage probability performance

are obtained. Numerical and simulation results are presented to further clarify the main outcomes of

the developed analysis.

ETPL

SNP - 025 Impact of User Identities and Access Conditions on Downlink

Performance in Closed Small-Cell Networks

A dual-layered downlink transmission scheme is proposed for intrinsically amalgamating multiple-

input-multiple-output (MIMO) spatial multiplexing (SMX) with spatial modulation (SM). The

proposed scheme employs a classic SMX transmission that is known to offer superior bandwidth

efficiency (BE) compared with SM. We exploit receive-antenna-based SM (RSM) on top of this

transmission as an enhancement of the BE. The RSM here is applied to the combined spatial and power-

level domain not by activating and deactivating the RAs but rather by choosing between two power

levels {P1, P2} for the received symbols in these antennas. In other words, the combination of symbols

received at a power level P1 carries information in the spatial domain in the same manner as the

combination of nonzero elements in the receive symbol vector carries information in the RSM

transmission. This allows for the coexistence of RSM with SMX, and the results show increased BE

for the proposed scheme compared with both SMX and SM. To characterize the proposed scheme, we

carry out a mathematical analysis of its performance, and we use this to optimize the ratio between P1

and P2 for attaining the minimum error rates. Our analytical and simulation results demonstrate

significant BE gains for the proposed scheme compared with conventional SMX and SM.

ETPL

SNP - 026 Dual-Layered MIMO Transmission for Increased Bandwidth Efficiency

We study the robust linear equalizer for the uplink of massive multiple-input-multiple-output (MIMO)

systems with multicell pilot reuse. We use the worst-case approach for robust design to combat pilot

contamination. When the number of base station (BS) antennas is large, we build a novel model to

indicate the relationship between the instantaneous channel matrix and the imperfect channel

estimation in the presence of channel model uncertainty. Based on this model, we formulate the robust

equalizer design problem into a min-max problem. Furthermore, we transform the min-max problem

into an unconstrained one, and the optimality conditions are derived. With the resulting optimality

conditions, two iterative algorithms and a simple approximation method are proposed to compute the

optimal robust equalizers. Simulations are adopted to evaluate the performance of the proposed

algorithms and the approximation method. Compared with the conventional equalizers, the proposed

robust equalizers achieve a better bit error rate (BER) performance, particularly in the regime of high

signal-to-noise ratio (SNR), where pilot contamination is significant.

ETPL

SNP - 027 Robust Equalizer for Multicell Massive MIMO Uplink with Channel

Model Uncertainty

Vector orthogonal frequency-division multiplexing (V-OFDM) for single-transmit-antenna systems is

a generalization of OFDM where single-carrier frequency-domain equalization and OFDM are just two

special cases. Phase noise in a V-OFDM system leads to a common vector block phase error (CVBPE)

and an intervector block carrier interference effect. Severe performance degradation may occur if these

two effects are not estimated and compensated well. In this paper, blind and semiblind phase noise

estimation and compensation in a V-OFDM system is investigated by using the expectation-

maximization (EM) algorithm. This is motivated by the fact that the conventional frequency-domain

phase noise suppression schemes based on pilot-aided common phase error (CPE) or CVBPE

estimation and compensation are not spectrally efficient as the vector block size is increased. Two

novel schemes are proposed: One estimates the CVBPE only, and the other estimates the entire phase

noise sequence in the time domain. General closed-form formulas for the maximization step of the EM

algorithm for the two schemes are derived, and their computational complexity values are analyzed.

The performances of the two schemes are investigated by using linear-minimum-mean-square-error

(LMMSE) receivers. Simulations show that the two proposed schemes are very effective in estimating

and compensating for phase noise in V-OFDM systems. It turns out that the second proposed scheme

not only outperforms the traditional CPE or CVBPE schemes but is computationally efficient as well

when applied to V-OFDM systems.

ETPL

SNP - 028 EM-Based Phase Noise Estimation in Vector OFDM Systems Using

Linear MMSE Receivers

The communication delay of train control services has a great impact on the track utilization and speed

profile of high-speed trains. This paper undertakes stochastic delay analysis of train control services

over a high-speed railway fading channel using stochastic network calculus. The mobility model of

high-speed railway communications system is formulated as a semi-Markov process. Accordingly, the

instantaneous data rate of the wireless channel is characterized by a semi-Markov modulated process,

which takes into account the channel variations due to both large- and small-scale fading effects. The

stochastic service curve of high-speed railway communications system is derived based on the semi-

Markov modulated process. Based on the analytical approach of stochastic network calculus, the

stochastic upper delay bounds of train control services are derived with both the moment generating

function method and the complementary cumulative distribution function method. The analytical

results of the two methods are compared and validated by simulation.

ETPL

SNP - 029 Stochastic Delay Analysis for Train Control Services in Next-Generation

High-Speed Railway Communications System