980 research outputs found
Offloading of Users in NOMA-HetNet Using Repulsive Point Process
Ever increasing number of cellular users and their high data requirements,
necessitates need for improvement in the present heterogeneous cellular
networks (HetNet). Carrier sensing prevents base stations within a certain
range of the transmitter from transmitting and hence aids in reducing the
interference. Non-orthogonal multiple access (NOMA) has proven its superiority
for the 5th generation (5G) networks. This work proposes a mathematical model
for an improved HetNet with macro base station (MBS) and femto base station
(FBS) tier. The FBS tier is equipped to support NOMA and carrier sensing for
its transmissions. Offloading is performed for load balancing in HetNet where
the macro users (MU) from congested MBS tier are offloaded to the FBS tier. The
FBS tier pairs the offloaded MU (OMU) with an appropriate pairing user (PU) to
perform NOMA. The performance of the OMU is studied under different channel
conditions with respect to the available PU at the FBS and some useful
observations are drawn. A decrease in outage probability by for cell
center user (CCU) and for cell edge user (CEU) is observed for low
density FBS. The outage probability decreases by , for both the CCU
and CEU, for high density FBS using the proposed carrier sensing in NOMA. The
results are validated using simulations
Harvest the potential of massive MIMO with multi-layer techniques
Massive MIMO is envisioned as a promising technology for 5G wireless networks
due to its high potential to improve both spectral and energy efficiency.
Although the massive MIMO system is based on innovations in the physical layer,
the upper layer techniques also play important roles in harvesting the
performance gains of massive MIMO. In this article, we begin with an analysis
of the benefits and challenges of massive MIMO systems. We then investigate the
multi-layer techniques for incorporating massive MIMO in several important
network deployment scenarios. We conclude this article with a discussion of
open and potential problems for future research.Comment: IEEE Networ
MEC-aware Cell Association for 5G Heterogeneous Networks
The need for efficient use of network resources is continuously increasing
with the grow of traffic demand, however, current mobile systems have been
planned and deployed so far with the mere aim of enhancing radio coverage and
capacity. Unfortunately, this approach is not sustainable anymore, as 5G
communication systems will have to cope with huge amounts of traffic,
heterogeneous in terms of latency among other Qualityof- Service (QoS)
requirements. Moreover, the advent of Multiaccess Edge Computing (MEC) brings
up the need to more efficiently plan and dimension network deployment by means
of jointly exploiting the available radio and processing resources. From this
standpoint, advanced cell association of users can play a key role for 5G
systems. Focusing on a Heterogeneous Network (HetNet), this paper proposes a
comparison between state-of-the-art (i.e., radio-only) and MEC-aware cell
association rules, taking the scenario of task offloading in the Uplink (UL) as
an example. Numerical evaluations show that the proposed cell association rule
provides nearly 60% latency reduction, as compared to its standard,
radio-exclusive counterpart.Comment: 2018 IEEE Wireless Communications and Networking Conference Workshops
(WCNCW): The First Workshop on Control and management of Vertical slicing
including the Edge and Fog Systems (COMPASS
Edge and Central Cloud Computing: A Perfect Pairing for High Energy Efficiency and Low-latency
In this paper, we study the coexistence and synergy between edge and central
cloud computing in a heterogeneous cellular network (HetNet), which contains a
multi-antenna macro base station (MBS), multiple multi-antenna small base
stations (SBSs) and multiple single-antenna user equipment (UEs). The SBSs are
empowered by edge clouds offering limited computing services for UEs, whereas
the MBS provides high-performance central cloud computing services to UEs via a
restricted multiple-input multiple-output (MIMO) backhaul to their associated
SBSs. With processing latency constraints at the central and edge networks, we
aim to minimize the system energy consumption used for task offloading and
computation. The problem is formulated by jointly optimizing the cloud
selection, the UEs' transmit powers, the SBSs' receive beamformers, and the
SBSs' transmit covariance matrices, which is {a mixed-integer and non-convex
optimization problem}. Based on methods such as decomposition approach and
successive pseudoconvex approach, a tractable solution is proposed via an
iterative algorithm. The simulation results show that our proposed solution can
achieve great performance gain over conventional schemes using edge or central
cloud alone. Also, with large-scale antennas at the MBS, the massive MIMO
backhaul can significantly reduce the complexity of the proposed algorithm and
obtain even better performance.Comment: Accepted in IEEE Transactions on Wireless Communication
Wearable Communications in 5G: Challenges and Enabling Technologies
As wearable devices become more ingrained in our daily lives, traditional
communication networks primarily designed for human being-oriented applications
are facing tremendous challenges. The upcoming 5G wireless system aims to
support unprecedented high capacity, low latency, and massive connectivity. In
this article, we evaluate key challenges in wearable communications. A
cloud/edge communication architecture that integrates the cloud radio access
network, software defined network, device to device communications, and
cloud/edge technologies is presented. Computation offloading enabled by this
multi-layer communications architecture can offload computation-excessive and
latency-stringent applications to nearby devices through device to device
communications or to nearby edge nodes through cellular or other wireless
technologies. Critical issues faced by wearable communications such as short
battery life, limited computing capability, and stringent latency can be
greatly alleviated by this cloud/edge architecture. Together with the presented
architecture, current transmission and networking technologies, including
non-orthogonal multiple access, mobile edge computing, and energy harvesting,
can greatly enhance the performance of wearable communication in terms of
spectral efficiency, energy efficiency, latency, and connectivity.Comment: This work has been accepted by IEEE Vehicular Technology Magazin
Heterogeneous Services Provisioning in Small Cell Networks with Cache and Mobile Edge Computing
In the area of full duplex (FD)-enabled small cell networks, limited works
have been done on consideration of cache and mobile edge communication (MEC).
In this paper, a virtual FD-enabled small cell network with cache and MEC is
investigated for two heterogeneous services, high-data-rate service and
computation-sensitive service. In our proposed scheme, content caching and FD
communication are closely combined to offer high-data-rate services without the
cost of backhaul resource. Computing offloading is conducted to guarantee the
delay requirement of users. Then we formulate a virtual resource allocation
problem, in which user association, power control, caching and computing
offloading policies and resource allocation are jointly considered. Since the
original problem is a mixed combinatorial problem, necessary variables
relaxation and reformulation are conducted to transfer the original problem to
a convex problem. Furthermore, alternating direction method of multipliers
(ADMM) algorithm is adopted to obtain the optimal solution. Finally, extensive
simulations are conducted with different system configurations to verify the
effectiveness of the proposed scheme
Modeling, Analysis and Design for Carrier Aggregation in Heterogeneous Cellular Networks
Carrier aggregation (CA) and small cells are two distinct features of
next-generation cellular networks. Cellular networks with small cells take on a
very heterogeneous characteristic, and are often referred to as HetNets. In
this paper, we introduce a load-aware model for CA-enabled \textit{multi}-band
HetNets. Under this model, the impact of biasing can be more appropriately
characterized; for example, it is observed that with large enough biasing, the
spectral efficiency of small cells may increase while its counterpart in a
fully-loaded model always decreases. Further, our analysis reveals that the
peak data rate does not depend on the base station density and transmit powers;
this strongly motivates other approaches e.g. CA to increase the peak data
rate. Last but not least, different band deployment configurations are studied
and compared. We find that with large enough small cell density, spatial reuse
with small cells outperforms adding more spectrum for increasing user rate.
More generally, universal cochannel deployment typically yields the largest
rate; and thus a capacity loss exists in orthogonal deployment. This
performance gap can be reduced by appropriately tuning the HetNet coverage
distribution (e.g. by optimizing biasing factors).Comment: submitted to IEEE Transactions on Communications, Nov. 201
Small Cell Offloading Through Cooperative Communication in Software-Defined Heterogeneous Networks
To meet the ever-growing demand for a higher communicating rate and better
communication quality, more and more small cells are overlaid under the macro
base station (MBS) tier, thus forming the heterogeneous networks. Small cells
can ease the load pressure of MBS but lack of the guarantee of performance. On
the other hand, cooperation draws more and more attention because of the great
potential of small cell densification. Some technologies matured in wired
network can also be applied to cellular networks, such as Software-defined
networking (SDN). SDN helps simplify the structure of multi-tier networks. And
it's more reasonable for the SDN controller to implement cell coordination. In
this paper, we propose a method to offload users from MBSs through small cell
cooperation in heterogeneous networks. Association probability is the main
indicator of offloading. By using the tools from stochastic geometry, we then
obtain the coverage probabilities when users are associated with different
types of base stations (BSs). All the cell association and cooperation are
conducted by the SDN controller. Then on this basis, we compare the overall
coverage probabilities, achievable rate and energy efficiency with and without
cooperation. Numerical results show that small cell cooperation can offload
more users from MBS tier. It can also increase the system's coverage
performance. As small cells become denser, cooperation can bring more gains to
the energy efficiency of the network.Comment: 12 pages, 7 figure
Achieve Sustainable Ultra-Dense Heterogeneous Networks for 5G
Due to the exponentially increased demands of mobile data traffic, e.g., a
1000-fold increase in traffic demand from 4G to 5G, network densification is
considered as a key mechanism in the evolution of cellular networks, and
ultra-dense heterogeneous network (UDHN) is a promising technique to meet the
requirements of explosive data traffic in 5G networks. In the UDHN, base
station is brought closer and closer to users through densely deploying small
cells, which would result in extremely high spectral efficiency and energy
efficiency. In this article, we first present a potential network architecture
for the UDHN, and then propose a generalized orthogonal/non-orthogonal random
access scheme to improve the network efficiency while reducing the signaling
overhead. Simulation results demonstrate the effectiveness of the proposed
scheme. Finally, we present some of the key challenges of the UDHN
Energy-Efficient Resource Allocation for Mobile-Edge Computation Offloading (Extended Version)
Mobile-edge computation offloading (MECO) offloads intensive mobile
computation to clouds located at the edges of cellular networks. Thereby, MECO
is envisioned as a promising technique for prolonging the battery lives and
enhancing the computation capacities of mobiles. In this paper, we study
resource allocation for a multiuser MECO system based on time-division multiple
access (TDMA) and orthogonal frequency-division multiple access (OFDMA). First,
for the TDMA MECO system with infinite or finite computation capacity, the
optimal resource allocation is formulated as a convex optimization problem for
minimizing the weighted sum mobile energy consumption under the constraint on
computation latency. The optimal policy is proved to have a threshold-based
structure with respect to a derived offloading priority function, which yields
priorities for users according to their channel gains and local computing
energy consumption. As a result, users with priorities above and below a given
threshold perform complete and minimum offloading, respectively. Moreover, for
the cloud with finite capacity, a sub-optimal resource-allocation algorithm is
proposed to reduce the computation complexity for computing the threshold.
Next, we consider the OFDMA MECO system, for which the optimal resource
allocation is formulated as a non-convex mixed-integer problem. To solve this
challenging problem and characterize its policy structure, a sub-optimal
low-complexity algorithm is proposed by transforming the OFDMA problem to its
TDMA counterpart. The corresponding resource allocation is derived by defining
an average offloading priority function and shown to have close-to-optimal
performance by simulation.Comment: Accepted to IEEE Trans. on Wireless Communicatio
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