MIMO技术转型：从单用户到多用户通信的比较
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"从单用户到多用户通信——转变MIMO范式"
在无线通信领域，MIMO（Multiple-Input Multiple-Output，多输入多输出）技术已经从理论概念发展成为增强无线网络性能的实际技术手段。过去十年间，MIMO通信在点对点（Single-User, SU-MIMO）和多用户（Multiuser, MU-MIMO）应用中都取得了显著的进步。SU-MIMO主要通过使用空间时间码（以多样性增益为导向）和流复用传输（以速率最大化为导向）来提供巨大的容量和可靠性提升。
在传统的单用户MIMO系统视角下，多天线带来的额外空间自由度被用来扩展信号处理和检测的维度，主要作为物理层（PHY层）的性能增强工具。这一方法使得链路层协议在多址接入（上行链路和下行链路）中间接享受到MIMO天线的优势，表现为每个用户的速率更高或信道质量更稳定，而无需全面了解MIMO的具体能力。
然而，随着多用户MIMO（MU-MIMO）的发展，情况发生了变化。MU-MIMO允许基站同时与多个用户进行通信，有效地利用了空间资源，提高了频谱效率。这种方式使得多个用户可以共享同一频率资源，而不互相干扰，从而实现了更高的系统总体吞吐量。MU-MIMO的核心在于调度和预编码技术，通过智能地分配资源和调整信号传输方向，可以实现多个用户的并行传输，进一步优化了无线网络的性能。
MU-MIMO的应用不仅仅局限于下行链路，还可以扩展到上行链路，形成所谓的全双工MIMO，即上下行数据可以同时传输。这要求更加复杂的同步和干扰消除技术，但潜在的效益是巨大的。此外，MU-MIMO也涉及到用户选择问题，选择哪些用户进行多用户传输是优化系统性能的关键，通常需要根据信道状态信息（Channel State Information, CSI）进行动态决策。
在实际的无线通信标准如4G LTE和5G NR中，MIMO技术扮演着至关重要的角色。例如，5G NR引入了大规模MIMO（Massive MIMO）的概念，通过使用数百个天线元素，不仅在MU-MIMO上下文中提升了性能，还在波束成形、干扰管理和覆盖范围扩展等方面带来了革命性的改进。
从单用户到多用户通信的转变，标志着MIMO技术从单一性能提升工具进化为复杂网络优化策略的关键部分。这种转变对于无线通信系统的设计、规划和资源管理提出了新的挑战，同时也为提高网络容量、效率和用户体验打开了全新的可能性。未来的研究将继续探索MIMO技术在更复杂的网络环境和更高级别的通信服务中的应用，例如物联网（IoT）、车联网（V2X）以及毫米波通信等场景。
In the downlink illustrated in Figure 1, the received signal at
the
k
th receiver can be written as
y
k
= H
k
x + n
k
for k = 1,... ,U,(2)
where
H
k
∈ C
M
k
×N
represents the downlink channel and
n
k
∈ C
M
k
×1
is the additive Gaussian noise at receiver
k
. We
assume that each receiver also has perfect and instantaneous
knowledge of its own channel
H
k
. The transmitted signal
x
is a
function of the multiple users’ information data, an example of
which takes the superposition form
x =
k
x
k
where
x
k
is the sig-
nal carrying, possibly nonlinearly encoded, user
k
’s message, with
covariance
Q
k
= E(x
k
x
H
k
)
, with
E(·)
the expectation operator.
The power allocated to user
k
is therefore given by
P
k
= Tr(Q
k
)
,
where
Tr
is the trace operator. Under a sum power constraint at
the BS, the power allocation needs to maintain
k
P
k
≤ P
.
Assuming a unit variance for the noise, it is now known that
the capacity region for a given matrix channel realization can be
written as [7]:
C
BC
=
P
1
,..P
U
s.t.
k
P
k
=P
⎧
⎨
⎩
(R
1
,..R
U
) ∈
+U
, R
i
≤ log
2
det
I + H
i
(
j≥i
Q
j
)H
H
i
det
I + H
i
(
j>i
Q
j
)H
H
i
⎫
⎬
⎭
,(3)
where the expression should in turn be optimized over each pos-
sible user ordering. Although difficult to realize in practice, the
computation of the region above is facilitated by exploiting the
so-called duality results between the BC and the much simple to
obtain MAC capacity region, which stipulate that the BC region
can be calculated through the union of regions of the dual MAC
with all uplink power allocation vectors meeting the sum power
constraint
P
[8], [9].
The fundamental role played by the multiple antennas at
either the BS or the users in expanding the channel capacity is
best apprehended by examining how the sum rate (the point
yielded by the maximum
k
R
k
in the capacity region) scales
with the number of active users.
Assuming a block fading channel model and an homoge-
neous network where all users have the same signal-to-noise
ratio (SNR), the scaling law of the sum rate capacity of MIMO
Gaussian BC, denoted as
R
DPC
for
M
k
= M
, fixed
N
and
P
, and
large
U
is given by [10]
lim
U→∞
E(R
DPC
)
N log log(UM)
= 1.(4)
The result in (4) indicates that, with full CSIT, the system can
enjoy a multiplexing gain of
N
, obtained by the BS sending data
to
N
carefully selected users out of
U
. Since each user exhibits
M
independent fading coefficients, the total number of DoF for
multiuser diversity is
UM
, thus giving the extra gain
log log(UM)
.
In contrast with (4), the capacity obtained in a situation
where the BS is deprived from the users’ channel information is
reduced to (in the high SNR regime)
E(R
NoCSIT
) ≈ min(M, N) log SN R.(5)
DESIGN LESSONS
Information theory highlights several fundamental aspects
of MU-MIMO systems, which are in contrast much with the
conventional SU-MIMO setting. First, the results above
advocate for serving multiple users simultaneously in a
SDMA fashion, with a suitably chosen precoding scheme at
the transmitter. Although the multiplexing gain is limited by
the number of transmit antennas, the number of simultane-
ously served users is, in principle, arbitrary. How many and
which users should effectively be served with nonzero power
at any given instant is the problem addressed by the
resource allocation algorithm. Unlike in the single-user set-
ting, the spatial multiplexing of different data streams can
be done while users are equipped with single antenna
receivers, thus enabling the capacity gains of MIMO while
maintaining a low cost for user terminals. Having multiple
antennas at the terminal can thus be viewed as optional
equipment allowing extra diversity gain for certain users or
giving the flexibility toward interference canceling and mul-
tiplexing of several data streams to such users (but reducing
the number of other users served simultaneously). In addi-
tion to yielding MIMO multiplexing gains without the need
for MIMO user terminals, the multiuser setup presents the
advantage of being immune with respect to the possible ill-
behavior of the propagation channel, which often plagues
SU-MIMO communications, i.e., rank loss due to small spac-
ing and/or the presence of strong line-of-sight component
thanks to the wide physical separation between the users.
Finally, also in contrast with the conventional SU-MIMO set-
ting, the multiplexing factor
N
in the downlink comes at the
condition of channel knowledge at the transmitter. In the uplink
this multiplexing gain is more easily extracted because the BS
can be safely assumed to have uplink channel knowledge and
simply implements a classical multiuser receiver to separate the
contributions of the selected users in (1).
[FIG1] Downlink of a multiuser MIMO network. A BS
communicates simultaneously with several multiple antenna
terminals.
MU
User 1
User
k
User
U
M
1
M
k
Base Station
(
N
Antennas)
U
Users (User
k
Has
M
k
Antennas)
IEEE SIGNAL PROCESSING MAGAZINE [38] SEPTEMBER 2007
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