14,457 research outputs found
Autonomous Algorithms for Centralized and Distributed Interference Coordination: A Virtual Layer Based Approach
Interference mitigation techniques are essential for improving the
performance of interference limited wireless networks. In this paper, we
introduce novel interference mitigation schemes for wireless cellular networks
with space division multiple access (SDMA). The schemes are based on a virtual
layer that captures and simplifies the complicated interference situation in
the network and that is used for power control. We show how optimization in
this virtual layer generates gradually adapting power control settings that
lead to autonomous interference minimization. Thereby, the granularity of
control ranges from controlling frequency sub-band power via controlling the
power on a per-beam basis, to a granularity of only enforcing average power
constraints per beam. In conjunction with suitable short-term scheduling, our
algorithms gradually steer the network towards a higher utility. We use
extensive system-level simulations to compare three distributed algorithms and
evaluate their applicability for different user mobility assumptions. In
particular, it turns out that larger gains can be achieved by imposing average
power constraints and allowing opportunistic scheduling instantaneously, rather
than controlling the power in a strict way. Furthermore, we introduce a
centralized algorithm, which directly solves the underlying optimization and
shows fast convergence, as a performance benchmark for the distributed
solutions. Moreover, we investigate the deviation from global optimality by
comparing to a branch-and-bound-based solution.Comment: revised versio
Cross-layer Congestion Control, Routing and Scheduling Design in Ad Hoc Wireless Networks
This paper considers jointly optimal design of crosslayer congestion control, routing and scheduling for ad hoc
wireless networks. We first formulate the rate constraint and scheduling constraint using multicommodity flow variables, and formulate resource allocation in networks with fixed wireless channels (or single-rate wireless devices that can mask channel variations) as a utility maximization problem with these constraints.
By dual decomposition, the resource allocation problem
naturally decomposes into three subproblems: congestion control,
routing and scheduling that interact through congestion price.
The global convergence property of this algorithm is proved. We
next extend the dual algorithm to handle networks with timevarying
channels and adaptive multi-rate devices. The stability
of the resulting system is established, and its performance is
characterized with respect to an ideal reference system which
has the best feasible rate region at link layer.
We then generalize the aforementioned results to a general
model of queueing network served by a set of interdependent
parallel servers with time-varying service capabilities, which
models many design problems in communication networks. We
show that for a general convex optimization problem where a
subset of variables lie in a polytope and the rest in a convex set,
the dual-based algorithm remains stable and optimal when the
constraint set is modulated by an irreducible finite-state Markov
chain. This paper thus presents a step toward a systematic way
to carry out cross-layer design in the framework of “layering as
optimization decomposition” for time-varying channel models
Joint Scheduling of URLLC and eMBB Traffic in 5G Wireless Networks
Emerging 5G systems will need to efficiently support both enhanced mobile
broadband traffic (eMBB) and ultra-low-latency communications (URLLC) traffic.
In these systems, time is divided into slots which are further sub-divided into
minislots. From a scheduling perspective, eMBB resource allocations occur at
slot boundaries, whereas to reduce latency URLLC traffic is pre-emptively
overlapped at the minislot timescale, resulting in selective
superposition/puncturing of eMBB allocations. This approach enables minimal
URLLC latency at a potential rate loss to eMBB traffic.
We study joint eMBB and URLLC schedulers for such systems, with the dual
objectives of maximizing utility for eMBB traffic while immediately satisfying
URLLC demands. For a linear rate loss model (loss to eMBB is linear in the
amount of URLLC superposition/puncturing), we derive an optimal joint
scheduler. Somewhat counter-intuitively, our results show that our dual
objectives can be met by an iterative gradient scheduler for eMBB traffic that
anticipates the expected loss from URLLC traffic, along with an URLLC demand
scheduler that is oblivious to eMBB channel states, utility functions and
allocation decisions of the eMBB scheduler. Next we consider a more general
class of (convex/threshold) loss models and study optimal online joint
eMBB/URLLC schedulers within the broad class of channel state dependent but
minislot-homogeneous policies. A key observation is that unlike the linear rate
loss model, for the convex and threshold rate loss models, optimal eMBB and
URLLC scheduling decisions do not de-couple and joint optimization is necessary
to satisfy the dual objectives. We validate the characteristics and benefits of
our schedulers via simulation
Hierarchical Radio Resource Optimization for Heterogeneous Networks with Enhanced Inter-cell Interference Coordination (eICIC)
Interference is a major performance bottleneck in Heterogeneous Network
(HetNet) due to its multi-tier topological structure. We propose almost blank
resource block (ABRB) for interference control in HetNet. When an ABRB is
scheduled in a macro BS, a resource block (RB) with blank payload is
transmitted and this eliminates the interference from this macro BS to the pico
BSs. We study a two timescale hierarchical radio resource management (RRM)
scheme for HetNet with dynamic ABRB control. The long term controls, such as
dynamic ABRB, are adaptive to the large scale fading at a RRM server for
co-Tier and cross-Tier interference control. The short term control (user
scheduling) is adaptive to the local channel state information within each BS
to exploit the multi-user diversity. The two timescale optimization problem is
challenging due to the exponentially large solution space. We exploit the
sparsity in the interference graph of the HetNet topology and derive structural
properties for the optimal ABRB control. Based on that, we propose a two
timescale alternative optimization solution for the user scheduling and ABRB
control. The solution has low complexity and is asymptotically optimal at high
SNR. Simulations show that the proposed solution has significant gain over
various baselines.Comment: 14 pages, 8 figure
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