A Novel Technique for Multi User Multiple Access Spatial Modulation Using Adaptive Coding and Modulation

International Journal of Trend in Scientific Research and Development (IJTSRD)
Volume 6 Issue 1, November-December 2021 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470
@ IJTSRD | Unique Paper ID – IJTSRD47856 | Volume – 6 | Issue – 1 | Nov-Dec 2021 Page 833
A Novel Technique for Multi User Multiple Access Spatial
Modulation Using Adaptive Coding and Modulation
Anshul Sengar1
, Gaurav Morghare2
1
M Tech Scholar, 2
Research Guide,
1,2
Department of Electronics and Communication Engineering,
Oriental Institute of Science and Technology, Bhopal, Madhya Pradesh, India
ABSTRACT
The need for high peak data rates with the corresponding need for
significantly increased spectral efficiencies, and the support for
service specific quality of service (QoS) requirements are the key
elements that drive the research in the area of wireless
communication access technologies Since space constellations and
signal constellations are orthogonal, the well-known digital signal
modulation schemes can be used on top. The spatial multiplexing
gain comes from the simultaneous transmission of spatially encoded
bits. Analytical and numerical performance of SM in different
channel conditions, including practical channel considerations, are
studied and compared to existing MIMO techniques in this thesis.
Results show that SM achieve low BER (bit error ratio) with a
tremendous reduction in receiver complexity without sacrificing
spectral efficiency.
KEYWORDS: MIMO, Spatial Modulation, BER, Bit Error Rate,
Modulation Technique etc
How to cite this paper: Anshul Sengar |
Gaurav Morghare "A Novel Technique
for Multi User Multiple Access Spatial
Modulation Using Adaptive Coding and
Modulation" Published in International
Journal of Trend in
Scientific Research
and Development
(ijtsrd), ISSN:
2456-6470,
Volume-6 | Issue-1,
December 2021,
pp.833-841, URL:
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International Journal of Trend in
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I. INTRODUCTION
The upsurge of wireless communication systems such
as vehicle-to-vehicle (V2V) communication [1] and
wireless high definition (HD) television have fuelled
research into MIMO technology. In recent years,
MIMO has been considered as one of the core
techniques for improving data throughput, link
reliability and spectral efficiency [2-4]. MIMO
techniques can be divided into two main categories,
namely spatial diversity and spatial multiplexing
schemes. Spatial diversity techniques [5, 6] improve
link reliability by transmitting multiple redundant
copies of data to a receiver over independent
channels. Alamouti’s scheme [6] is a popular transmit
diversity technique, which uses a pair of transmit
antennas to achieve full transmit diversity. However,
diversity gains are traded off by low spectral
efficiency, which remains unchanged when compared
to a single-input multiple-output (SIMO) system [7].
Among the set of existing technologies, multiple-
input multiple-output (MIMO) with adaptive coding
and modulation is promising candidate for future
wireless systems. MIMO system boosts the spectral
efficiency by using multiple transmit-antennas to
simultaneously transmit data to the receiver. Adaptive
modulation and coding enables robust and spectrally-
efficient transmission over time-varying channels.
The basic premise is to estimate the channel at the
receiver and feed this estimate back to the transmitter,
so that the transmission scheme can be adapted
relative to the channel characteristics. The idea is to
consider the transmit antenna array as a spatial
constellation diagram with the constellation points
being the actual antennas. Each spatial constellation
point is then mapped to a different bit sequence. At
any time instant, only one antenna transmits energy,
and the actual information resides in the actual
physical location of the transmitting antenna, or
spatial constellation point. This, of course, requires a
new detection process at the receiver, namely the
antenna detection.
IJTSRD47856

International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470
II. REVIEW OF LITERATURE SURVEY
Space shift keying (SSK) modulation in [28] can be
considered as a special instance of SM, since only the
transmit antenna indices carry information. The SSK
scheme eliminated the use of conventional
modulation techniques, which reduced receiver
complexity as compared to SM. Furthermore, this
decrease in receiver complexity was attained without
sacrificing performance gains. Similar to SM, SSK
schemes work only for a number of transmit antennas
which are a power of two. In cases where the transmit
antenna constraint cannot be met, the generalized
SSK (GSSK) scheme [29] provides a viable solution.
In [29], GSSK used a combination of antenna indices
to transmit information, which may be applied to any
antenna configuration. However, the flexibility of
GSSK is traded off by reduced performance as
compared to SSK [29].
Fractional bit encoded spatial modulation (FBE-SM)
in [30], which is a more versatile SM scheme based
on the theory of modulus conversion. The FBE-SM
approach allowed the transmitter to function with an
arbitrary number of antennas. As a result, this scheme
is well suited to compact mobile devices, where space
constraints pose limits on the number of transmit
antennas. Numerical results have shown that FBE-SM
offers flexibility in design and the necessary degrees
of freedom for trading-off attainable performance and
capacity [30].
Trellis coded spatial modulation (TCSM) in [31],
which applied trellis coded modulation (TCM) to the
antenna constellation points of SM. This increased the
free distance between antenna constellation points,
thus leading to improved performance over spatially
correlated channels. The TCSM scheme was analysed
in [32], where an analytical framework for the
performance over correlated fading channels was
proposed.
Soft-output ML detection in [33], where a soft-output
ML detector for SM orthogonal frequency division
multiplexing (OFDM) systems was introduced and
shown to outperform the conventional hard decision
based SM detector.
SM with partial channel state information (CSI) at the
receiver in [34], where an SM detector with unknown
phase reference at the receiver was developed and
analysed. Monte Carlo simulations were used as a
tool to verify the analytical frameworks and
investigate the performance of the proposed detector.
Results indicated that SM performance was severely
degraded when phase information was not available
at the receiver. This highlights the fact that accurate
channel estimation is required for the efficient
operation of SM detectors [34].
Optical spatial modulation (OSM) in [35], which is an
indoor optical wireless communication technique
based on SM. The OSM scheme was shown to
achieve twice and four times the data rate as
compared to conventional on-off keying and pulse-
position modulation techniques, respectively [35]. In
[36], channel coding was applied to OSM and the
BER performance of both hard and soft detectors
were determined analytically. Monte Carlo simulation
results demonstrated that the application of channel
coding techniques can enhance OSM performance by
approximately 5dB and 7dB for hard and soft
decisions, respectively [36].
Normalized maximum ratio combining (NMRC)
detector in [37], where a low complexity sub-optimal
SM detection algorithm for unconstrained channels
was proposed. In addition, an antenna index (AI) list
based detector was also introduced in [37]. Monte
Carlo simulation results and corresponding analysis
indicated that the AI list based scheme can achieve
near optimal performance and reduced complexity
when compared to the optimal SM detector.
However, the AI list based detector can only operate
efficiently for list sizes equal to half the number of
transmit antennas. This resulted in a 40% increase in
complexity as compared to the NMRC scheme.
Space-time block coded spatial modulation (STBC-
SM) in [38], which combined SM and STBC in order
to exploit the transmit diversity potential of MIMO
channels. The proposed scheme was analysed in [38],
where a closed form expression for the average BER
was derived. Monte Carlo simulations results were
used to support analytical frameworks and also
demonstrated the performance advantages of STBC-
SM over SM. Results indicated that STBC-SM offers
performance enhancements of 3dB-5dB (depending
on the spectral efficiency) over conventional SM [38].
It must be highlighted that a similar scheme termed
Alamouti coded spatial modulation is proposed in this
dissertation. However, both schemes have been
developed independently based on the different
paradigms employed.
III. SPATIAL MODULATION MIMO
In this section, we commence byintroducing the SM–
MIMO concept, illustrating it with the aid of some
simple examples. Again, we denote by Nt and Nr the
number of TAs and RAs, respectively. The
cardinality of the signal constellation diagram is
denoted by M. Either PSK or QAM are considered. In
general, Nt, Nr, andM can be chosen independently of
each other. At the receiver, optimum ML
demodulation is considered. Thus, Nr can be chosen
independently of Nt . For ease of exposition, we
assume and with nt and m being two

positive integers. In Section IV, we describe general
SM–MIMO encodings as well as some suboptimal
(non-ML) demodulation schemes.
In Fig. 1, the SM–MIMO concept is illustrated for Nt
= M = 2, and it is compared to the conventional SMX
scheme and the OSTBC scheme designed for transmit
diversity. In the latter case, the Alamouti scheme is
considered as an example [80].
In SMX–MIMO, two PSK/QAM symbols (S1 and
S2) are simultaneously transmitted from a pair of TAs
in a single channel use. For arbitrary Nt and M, the
rate of SMX is bpcu.
Fig 1 Illustration of three MIMO concepts: (a)
spatial multiplexing; (b) transmit diversity; and
(c) SM.
In OSTBC–MIMO, two PSK/QAM symbols (S1 and
S2) are first encoded and then simultaneously
transmitted from a pair of TAs in two channel uses.
For arbitrary Nt and M, the rate of OSTBC is
bpcu, where is the
rate of the space-time block code and NM is the
number of information symbols transmitted in Ncu
channel uses If, as shown in Fig. 1, the Alamouti code
is chosen, then we have Rc = 1.
In SM–MIMO, only one (S1) out of the two symbols
is explicitly transmitted, while the other symbol (S2)
is implicitly transmitted by determining the index of
the active TA in each channel use. In other words, in
SM–MIMO, the information symbols are modulated
onto two information carrying units: a) one
PSK/QAM symbol; and b) a single active TA via an
information-driven antenna-switching mechanism.
For arbitrary Nt and M, the rate of SM
is bpcu [76], [82].
In Figs. 2 and 3, the encoding mechanism of SM–
MIMO is illustrated for Nt = M= 4 by considering
two generic channel uses, where the concept of ‘‘SM
or spatialconstellation diagram’’ is also introduced.
The rate of this MIMO setup is
bpcu, hence the encoder
processes the information bits in blocks of four bits
each. In the first channel use shown in Fig. 2, the
block of bits to be encoded is ‘‘1100.’’ The first
bits, ‘‘11,’’ determine the single active TA
(TX3), while the second bits, ‘‘00,’’
determine the transmitted PSK/QAM symbol.
Likewise, in thesecond channel use shown in Fig. 3,
the block of bits to be encoded is ‘‘0001.’’ The first
bits, ‘‘00,’’ determine the single active TA
(TX0), while the second bits, ‘‘01,’’
determine the transmitted PSK/ QAM symbol.The
activated TA may change every channel use
according to the input information bits. Thus, TA
switching is an effective way of mapping the
information bits to TA indices and of increasing the
transmission rate. It is worth mentioning here that the
idea of increasing the rate of wireless
communications using TA switching has been alluded
in pioneering MIMO papers under the concept of
‘‘spatial cycling using one transmitter at a time’’.
Fig 2: Illustration of the 3-D encoding of SM
(first channel use).
Fig 3: Illustration of the 3-D encoding of SM
(second channel use).
The information bits are modulated onto a 3-D
constellation diagram, which generalizes the known
2-D (complex) signal-constellation diagram of
PSK/QAM modulation schemes. The thirddimension
is provided by the antenna array, where some of the

bits are mapped to the TAs. In SM–MIMO research,
this third dimension is termed the ‘‘spatial-
constellation diagram’’ [76]. In simple mathematical
terms, the signal model of SM– MIMO, assuming a
frequency-flat channel model, is as follows:
where is the complex received vector;
is the complex channel matrix; is
the complex AWGN at the receiver; and
is the complex modulated vector with
being the complex (scalar) PSK/QAM modulated
symbol belonging to the signal-constellation diagram
and being the Nt×1 vector belonging to the
spatial-constellation diagram as follows:
where et is the tth entry of e for t = 1, 2,...,Nt. In other
words, the points (Nt-dimensional vectors) of the
spatialconstellation diagram are the Nt unit vectors of
the natural basis of the Nt-dimensional Euclidean
space.
If Nt = 1, SM–MIMO reduces to conventional
singleantenna communications, where the
information bits are encoded only onto the signal-
constellation diagram. In this case, the rate
is . On the other hand, if M = 1 the
information is encoded only onto the spatial-
constellation diagram by providing et equal
to . In particular, SSK modulation is a
MIMO scheme, where data transmission takes place
only through the informationdriven TA switching
mechanism. It is apparent that SM– MIMO can be
viewed as the combination of single-antenna
PSK/QAM and SSK–MIMO modulations.
IV. PROPOSED METHODOLOGY
The system model of the proposed 2 user MA-SM
with AMC is shown in Fig. 4, which consists of a
MIMO wireless link. We have two user transmitters,
transmitting over a Rayleigh fading channel to a
common receiver. The receiver is a SM demodulator
with a ML detector explained in previous section. The
receiver calculates the joint probability of the two
received signals. The ML detector then computes the
Euclidean distance between the received vector signal
and the set of all possible received signals, selecting
the closest one.
Fig. 4. 2-user Multiple Access Spatial
Modulation with ACM System Model
At the user end, in contrast to conventional SM which
uses the same modulation order for data mapping, in
the proposed scheme, the modulation orders assigned
to transmit antennas are chosen by the switching unit.
More specifically, when the channel is slowlyvarying,
the adaptive unit at the receiver computes the
optimum candidate for transmission and sends this
information to the two users through a low-bandwidth
feedback path. The transmitters then employ the
corresponding modulation orders for the next channel
use.
The Adaptive unit uses the channel state information
obtained by the receiver to decide the optimum level
of modulation. It then relays it back to the transmitters
of the two users, which utilise this information to
modulate the next transmitted signal accordingly. If
the nature of the channel changes, then so does the
modulation order. This is done by predefining a
minimum and maximum level of BER. The receiver
then compares the BER of the received signal to the
pre-set values, for a signal that has a lower BER value
than desired, the frame size of the transmitted signal
can be increased and for a signal that has higher BER
value than desired, the frame size of the transmitted
signal is reduced. Quintessentially, we aim to
optimally use the channel bandwidth available to us.
According to the requirement and characteristics of
the channel, the transmitter adapts itself to the best
modulation scheme so as to maintain BER for better
spectral efficiency.
V. SIMULATION AND RESULTS
In this section, we study and compare the graphs
obtained for various levels of channel attenuation with
and without ACM.

Each simulation graph shown below consists of four
results. The pink and cyan graphs depict the results
for two user multiple access spatial modulation
without ACM. The red and blue graphs denote the
outcomes of our proposed methodology with ACM.
As can be seen in the following section, we consider
the different variations in the simulation results by
varying the channel attenuation of the system. This
change in channel attenuation leads to change in the
CSI and shows adaptive modulation in action. The
graphs study the BER of the system in reference to the
SNR in dB of the proposed scheme. The main aim as
stated is to minimise BER of the received signal, and
by merely studying the graph it is beautifully
apparent. Also, if we do a detailed deliberation of each
case and compare the results with previous outcomes,
we can easily see and analyse the changes in the
various outcomes for different inputs.
Fig. 5 Comparison Results for users 1 and 2 with
and without ACM and channel attenuation of 1
and 2 dB respectively.
This graph illustrates the behaviour of the signal with
and without ACM for channel attenuation of User1
and User2 as 1 and 2 respectively. For the graph
without ACM i.e. only multiple access spatial
modulation the BER is higher. It does gradually
decrease with the SNR but doesn’t go lower than
0.001 whereas in the presence of ACM, the BER is
more of a straight line and sharply decreases to the
lower bound values. The upper and lower bound
values of BER are predefined so as to maintain it in a
prescribed range and modulate accordingly.
Now, this graph is a reversal of the previous one. Here
the values are similar except the fact that the channel
attenuation for user1 is now 2 and for user2 is 1. The
behaviour is identical as was in the previous graph.
This is because we are working in a system that
calculates joint probability of both the received signals
and thus the result always remains the same. The
behaviour is predominantly dependant on the channel
properties. This stems the fact that irrespective of the
number or position of the TA, the behaviour of the
receiver remains the same.
In this graph, we increase the channel attenuation of
user1 to 3 while maintaining channel attenuation of
user2 at 1. The behaviour of user2 shows the BER is
higher and for user1 the BER is lower but more
sharply decreasing. If we compare it to the previous
graph, where user2 was still 1 the graph is steeper and
by increasing the channel attenuation of user1, the
receiver properties change to lowering the overall
value of BER.

For a more in depth study, the channel attenuation of
user1 is further lowered to 0 and that of user2 is still 1.
The result now is vastly different as can be seen. For
user1 transmission with and without ACM the BER of
both is at a constant value. This might be because the
receiver can no longer calculate the minimum distance
and thus no variation in the BER. Whereas for the
case of user2, with ACM the graph abruptly cuts off.
This needs to be studied in more depth to understand
the behaviour of the systems.
Let us up the ante now by taking channel attenuation
of user 1 as 10. When the difference in channel
attenuation value is high the receiver sends back
information to compensate for the BER and thus the
BER of the other user, user2 in this case drastically
increases. The BER of user1 is lower than in any of
the previous cases.
Fig. 10 Comparison Results for users 1 and 2
with and without ACM and channel attenuation
of 0.5 and 1 dB respectively.
This is an interesting graph. As we can see the channel
attenuation of user1 is 0.5 here i.e. between 0 and 1.
The graph shows properties closer to the case of
channel attenuation being 1. This leads to the
conclusion that if the difference in channel attenuation
is small then the difference in the BER values also
reduces. Although the behaviour remains the same.
of 0.005 and 1 dB respectively.
Further decreasing the channel attenuation of user1 to
0.005 shows an entirely new result, which is
somewhere in between the BER values of users with
channel attenuation of 0 and 1. Both user1’s with and
without ACM, are nearly at a constant value as it was
for the case of channel attenuation 0. But the second
users, just show a more extended declining version the
graph. It can be assumed that the receiver is trying to
balance the two channel attenuation values for an
efficient use of the channel, which is actually what we
set out to achieve.

of 1 and 0.1 dB respectively.
For channel attenuation values of 1 and 0.1 for user1
and user2 respectively. The graph for multiple access
SM case is not much different just a little lower than it
was before. This leads to the user with ACM to
overlap with it for the case of 0.1 channel attenuation.
VI. CONCLUSION
The performance of multi user multiple access spatial
modulation with adaptive coding and modulation was
studied. The ML receiver was successfully
implemented in the Rayleigh fading channel and used
to enhance the performance through ACM. For
different channel attenuation values and changing
number of antennas the impact on BER was studied.
The BER shows a significant improvement with ACM
as opposed to without ACM. We also studied the BER
of the two users for various changes in the channel
attenuation. The receiver shows consistent
performance characteristics for both users, which was
also a point of concern. As we utilized the concept of
joint probability to optimize both of the obtained
signals.
The generalization of this system for multiple user
with different modulation techniques can be studied in
the future. Applying coding schemes to this multiple
user interface should impact BER and can be further
investigated.In recent years, manysolutions have been
proposed independently with the aim of simplifying
the design of MIMO communications. We believe that
this trend reinforces the potential impact of SM–
MIMO research in the context of next-generation
wireless systems.
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