上行云接入网压缩解码策略优化研究
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更新于2024-07-15
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"这篇研究论文探讨了上行云无线电接入网（C-RAN）中最佳的前传压缩和解码策略。在该网络模型中，多天线基站（BSs）通过容量有限的前传链路连接到基于云计算的中央处理器（CP）。BSs使用Wyner-Ziv编码对接收到的信号进行压缩，并将表示位发送到CP，由CP执行所有用户消息的解码。本文针对这种架构，对压缩转发策略的前传压缩和CP解码策略的最优结构进行了研究。对于CP解码策略设计，论文指出在总前传容量约束下，可以采用一种允许在CP处任意交织顺序的量化和用户消息码字的通用级联解码策略。"
本文深入研究了上行云无线接入网络（C-RAN）的压缩转发策略，这是一个新兴的无线通信架构，它利用云计算技术集中处理来自多个基站的信号，以提高效率和资源利用率。在C-RAN中，多天线基站（BSs）不直接解码用户数据，而是首先对接收到的多用户信号进行压缩，使用Wyner-Ziv编码，这是一种考虑信源相关性的无损数据压缩方法。 Wyner-Ziv编码旨在利用基站之间的空间相关性和用户信号的统计特性来优化压缩过程，减少前传链路上的数据量，从而减轻带宽限制。
论文的重点在于前传压缩和中央处理器（CP）的解码策略设计。在总前传容量有限的情况下，找到最优的策略至关重要，因为它直接影响到系统性能和用户服务质量。作者提出了一种通用的级联解码策略，该策略允许CP在解码过程中灵活地交织量化码字和用户消息码字。这种灵活性使得CP可以根据信号质量和用户需求动态调整解码顺序，以最大化整体系统的吞吐量和降低错误率。
此外，论文可能还涵盖了如何优化CP的解码顺序、压缩率的选择以及如何平衡压缩与解码复杂性之间的权衡，这些都是在实际部署C-RAN时需要解决的关键问题。通过对这些策略的深入分析和优化，论文为上行C-RAN的设计提供了理论基础和实用指导，有助于提升未来无线网络的性能和效率。
这篇论文对C-RAN的前传压缩和解码策略进行了详尽的研究，为实现高效、灵活且适应性强的上行C-RAN提供了新的见解和解决方案，对于推动无线通信领域的理论发展和技术进步具有重要意义。
ZHOU et al.: ON OPTIMAL FRONTHAUL COMPRESSION AND DECODING STRATEGIES FOR UPLINK C-RANs 7405
II. ACHIEVABLE RATE REGIONS FOR UPLINK C-RAN
A. Channel Model
This paper considers an uplink C-RAN model, where
K mobile users communicate with a CP through L BSs,
as shown in Fig. 1. The noiseless digital fronthaul link
connecting the BS to the CP has the capacity of C
bits
per complex dimension. The fronthaul capacity C
is the
maximum long-term average throughput of the th fronthaul
link, i.e., lim
n→∞
1
n
n
i=1
C
(i) ≤ C
,whereC
(i) represents the
instantaneous transmission rate of the th fronthaul link at the
ith time slot. Each user terminal is equipped with M antennas;
each BS is equipped with N antennas. Perfect channel state
information (CSI) is assumed to be available to all the BSs
and to the CP. For simple notation, we denote K ={1, ··· , K }
and L ={1, ··· , L} in this paper.
Let X
k
∈ C
M
be the signal transmitted by the kth user,
which is subject to per-user transmit power constraint of P
k
,
i.e. E
X
k
X
†
k
≤ P
k
. The signal received at the th BS can be
expressed as
Y
=
K
k=1
H
,k
X
k
+ Z
,= 1, 2,...,L, (1)
where Z
∼ CN(0,
) represents the additive Gaussian noise
for BS and is independent across different BSs, and H
,k
denotes the complex channel matrix from user k to BS .
We consider the compress-and-forward scheme [25], [26]
applied to the uplink C-RAN system, in which the BSs
compress the received signals Y
, and forward the quantization
bits to the CP for decoding. At the CP, the user messages
are decoded using either joint decoding or some form of
successive decoding. In joint decoding, the quantization code-
words and the message codewords are decoded simultaneously,
whereas, in successive decoding, the quantization codewords
and messages are decoded successively in some prescribed
order. Different orderings can potentially result in different
achievable rates.
B. Achievable Rate-Fronthaul Regions for Joint Decoding,
Successive Decoding, and Generalized Successive Decoding
In the following, we present the achievable rate-fronthaul
regions of compress-and-forward with joint decoding and
different forms of successive decoding.
Proposition 1 ([3, Proposition IV.1]): For the uplink
C-RAN model shown in Fig. 1, the achievable
rate-fronthaul region of compress-and-forward with joint
decoding, P
∗
JD
, is the closure of the convex hull of all
(R
1
, ··· , R
K
, C
1
,...,C
L
) ∈ R
K +L
+
satisfying
k∈T
R
k
<
∈S
C
− I
Y
;
ˆ
Y
|X
K
+ I
X
T
;
ˆ
Y
S
c
|X
T
c
(2)
for all T ⊆ K and S ⊆ L, for some product distribution
K
k=1
p(x
k
)
L
=1
p(ˆy
|y
) such that E
X
k
X
†
k
≤ P
k
for k =
1,...,K .
Note that for the uplink C-RAN model, the rate region (2)
given by compress-and-forward with joint decoding is identi-
cal to the rate region of the noisy network coding scheme [9],
which is an extension of the compress-and-forward scheme
to the general multiple access relay network by using joint
decoding at the receiver and block Markov coding at the
transmitters.
As a more practical decoding strategy, successive decoding
of quantization codewords first, and then the user messages
at the CP can also be used in uplink C-RAN. The following
proposition states the rate-fronthaul region achieved by suc-
cessive decoding.
Proposition 2: ([5, Theorem 1]): For the uplink C-RAN
model shown in Fig. 1, the achievable rate-fronthaul region of
compress-and-forward with successive decoding, P
∗
SD
,isthe
closure of the convex hull of all (R
1
, ··· , R
K
, C
1
,...,C
L
) ∈
R
K +L
+
satisfying
k∈T
R
k
< I
X
T
;
ˆ
Y
L
|X
T
c
, ∀ T ⊆ K, (3)
and
I
Y
S
;
ˆ
Y
S
|
ˆ
Y
S
c
<
∈S
C
, ∀ S ⊆ L, (4)
for some product distribution
K
k=1
p
(
x
k
)
L
=1
p(ˆy
|y
) such
that E
X
k
X
†
k
≤ P
k
for k = 1,...,K .
Note that (3) is the multiple-access rate region, (4) repre-
sents the Berger-Tung rate region for distributed lossy com-
pression [6, Theorem 12.1], while (2) incorporates the joint
decoding of the quantization codewords and the user messages.
Because of its lower decoding complexity, successive decoding
is usually preferred for practical implementation of the uplink
C-RAN systems [21], [22]. Note that in the above strategy,
successive decoding applies only to the vector X
k
(user mes-
sage codewords) and the vector Y
(quantization codewords);
the elements within vectors X
k
and Y
are still decoded
jointly.
It is possible to improve upon the successive decoding
scheme by allowing arbitrary interleaved decoding orders
between quantization codewords and user message codewords.
We call this the generalized successive decoding scheme in
this paper. The generalized successive decoding scheme is
first suggested in [27] under the name of joint base-station
successive interference cancelation scheme. In such a succes-
sive decoding strategy, the set of potential decoding orders
includes all the permutations of quantization and user message
codewords.
Denote π as a permutation on the set of quantization and
user message codewords
ˆ
Y
1
,
ˆ
Y
2
,...,
ˆ
Y
L
, X
1
, X
2
,...X
K
.
For a given permutation π, the decoding order is given by
the index of the elements in π, i.e., π(1) → π(2) →
··· → π(L + K ). For example, consider an uplink C-RAN
model as shown in Fig. 1 with 2 BSs and 2 users. If π =
ˆ
Y
1
, X
1
,
ˆ
Y
2
, X
2
, then the decoding of
ˆ
Y
2
and X
2
can
use both previously decoded user messages and quantization
codewords as side information. The resulting rate region is
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